diff --git a/CHANGELOG.md b/CHANGELOG.md index 421323d5..b73df5af 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,19 @@ ## Changelog -### v1.4.11 (Janouary 5, 2026) +### v1.5.1 (February 23, 2026) +- Fix challenge learner +- Update requirements. + +### v1.5.0 (February 5, 2026) +- Fix challenge learners +- Adding MDS-Onto Ontologizer +- Fix to Ontologizers and Processor +- Minor code refactoring +- Change to the theme of documentatin website +- Update documentation website +- Add LLM learners (Logit, Thinking, etc) + +### v1.4.11 (January 5, 2026) - Add `text2onto` component for challenge learners with their documentation. - Code refactoring - OS compatibility CI/CD diff --git a/CITATION.cff b/CITATION.cff index 38ec1b0f..eeb4639e 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -31,5 +31,5 @@ keywords: - Large Language Models - Text-to-ontology license: MIT -version: 1.4.10 -date-released: '2025' +version: 1.5.0 +date-released: '2026' diff --git a/README.md b/README.md index ff116abd..8cab52a6 100644 --- a/README.md +++ b/README.md @@ -12,182 +12,211 @@ [![Hugging Face Collection](https://img.shields.io/badge/🤗HuggingFace-Collection-blue)](https://huggingface.co/collections/SciKnowOrg/) [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit) [![Documentation Status](https://app.readthedocs.org/projects/ontolearner/badge/)](https://ontolearner.readthedocs.io/) -[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](MAINTANANCE.md) +[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](MAINTENANCE.md) [![DOI](https://zenodo.org/badge/913867999.svg)](https://doi.org/10.5281/zenodo.15399773) - -**OntoLearner** is a modular and extensible architecture designed to support ontology learning and reuse. The conceptual and functional architecture of OntoLearner is shown as following. The framework comprises three core components—**Ontologizers**, **Learning Tasks**, and **Learner Models**—structured to enable reusable and customizable ontology engineering workflows. +--- + +**OntoLearner** is a modular and extensible Python library for **ontology learning** powered by Large Language Models (LLMs). It provides a unified framework covering the full workflow — from loading and modularizing ontologies to training, predicting, and evaluating learner models across multiple ontology learning tasks. + +The framework is built around three core components: + +- 🧩 **Ontologizers** — load, parse, and modularize ontologies from 150+ ready-to-use sources across 20+ domains. +- 📋 **Learning Tasks** — support for Term Typing, Taxonomy Discovery, Non-Taxonomic Relation Extraction, and Text2Onto. +- 🤖 **Learner Models** — plug-and-play LLM, Retriever, and RAG-based learners with a consistent `fit → predict → evaluate` interface. + +--- ## 🧪 Installation -OntoLearner is available on [PyPI](https://pypi.org/project/OntoLearner/) and you can install using `pip`: +OntoLearner is available on [PyPI](https://pypi.org/project/OntoLearner/) and can be installed with `pip`: ```bash pip install ontolearner ``` -Next, verify the installation: +Verify the installation: + ```python import ontolearner print(ontolearner.__version__) ``` -Please refer to [Installation](https://ontolearner.readthedocs.io/installation.html) page for further options. +> For additional installation options (e.g., from source, with optional dependencies), see the [Installation Guide](https://ontolearner.readthedocs.io/installation.html). + +--- ## 🔗 Essential Resources -| Resource | Info | -|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| **[📚 OntoLearner Documentation](https://ontolearner.readthedocs.io/)** | OntoLearner's extensive documentation website. | -| **[🤗 Datasets on Hugging Face](https://huggingface.co/collections/SciKnowOrg/ontolearner-benchmarking-6823bcd051300c210b7ef68a)** | Access curated, machine-readable ontologies. | -| **Quick Tour on OntoLearner** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DuElAyEFzd1vtqTjDEXWcc0zCbiV2Yee?usp=sharing) ``version=1.2.1`` | OntoLearner hands-on Colab tutorials. | -| **[🚀 Quickstart](https://ontolearner.readthedocs.io/quickstart.html)** | Get started quickly with OntoLearner’s main features and workflow. | -| **[🕸️ Learning Tasks](https://ontolearner.readthedocs.io/learning_tasks/learning_tasks.html)** | Explore supported ontology learning tasks like LLMs4OL Paradigm tasks and Text2Onto. | | -| **[🧠 Learner Models](https://ontolearner.readthedocs.io/learners/llm.html)** | Browse and configure various learner models, including LLMs, Retrieval, or RAG approaches. | -| **[📚 Ontologies Documentations](https://ontolearner.readthedocs.io/benchmarking/benchmark.html)** | Review benchmark ontologies and datasets used for evaluation and training. | -| **[🧩 How to work with Ontologizer?](https://ontolearner.readthedocs.io/ontologizer/ontology_modularization.html)** | Learn how to modularize and preprocess ontologies using the Ontologizer module. | +| Resource | Description | +|:---------|:------------| +| **[📚 Documentation](https://ontolearner.readthedocs.io/)** | Full documentation website. | +| **[🤗 Datasets on Hugging Face](https://huggingface.co/collections/SciKnowOrg/ontolearner-benchmarking-6823bcd051300c210b7ef68a)** | Curated, machine-readable ontology datasets. | +| **[🚀 Quickstart](https://ontolearner.readthedocs.io/quickstart.html)** | Get started in minutes. | +| **[🕸️ Learning Tasks](https://ontolearner.readthedocs.io/learning_tasks/learning_tasks.html)** | Term Typing, Taxonomy Discovery, Relation Extraction, and Text2Onto. | +| **[🧠 Learner Models](https://ontolearner.readthedocs.io/learners/llm.html)** | LLM, Retriever, and RAG-based learner models. | +| **[📖 Ontologies Documentation](https://ontolearner.readthedocs.io/benchmarking/benchmark.html)** | Browse 150+ benchmark ontologies across 20+ domains. | +| **[🧩 Ontologizer Guide](https://ontolearner.readthedocs.io/ontologizer/ontology_modularization.html)** | How to modularize and preprocess ontologies. | +| **[📊 Metrics Dashboard](https://huggingface.co/spaces/SciKnowOrg/OntoLearner-Benchmark-Metrics)** | Explore benchmark ontology metrics and complexity scores. | + +--- + +## ✨ Key Features + +- **150+ Ontologizers** across 20+ domains (biology, medicine, agriculture, chemistry, law, finance, and more). +- **Multiple learning tasks**: Term Typing, Taxonomy Discovery, Non-Taxonomic Relation Extraction, and Text2Onto. +- **Three learner paradigms**: LLM-based, Retriever-based, and Retrieval-Augmented Generation (RAG). +- **Hugging Face integration**: auto-download ontologies and models directly from the Hub. +- **Unified API**: consistent `fit → predict → evaluate` interface across all learners. +- **LearnerPipeline**: end-to-end pipeline in a single call. +- **Extensible**: easily plug in custom ontologies, learners, or retrievers. + +--- ## 🚀 Quick Tour -Get started with OntoLearner in just a few lines of code. This guide demonstrates how to initialize ontologies, load datasets, and train an LLM-assisted learner for ontology engineering tasks. -**Basic Usage - Automatic Download from Hugging Face**: +### Loading an Ontology + +Load any of the 150+ built-in ontologies and extract task datasets in just a few lines: + ```python from ontolearner import Wine -# 1. Initialize an ontologizer from OntoLearner +# Initialize an ontologizer ontology = Wine() -# 2. Load the ontology automatically from HuggingFace +# Auto-download from Hugging Face and load ontology.load() -# 3. Extract the learning task dataset +# Extract learning task datasets data = ontology.extract() -``` -To see the ontology metadata you can print the ontology: -```python +# Inspect ontology metadata print(ontology) ``` -Now, explore [150+ ready-to-use ontologies](https://ontolearner.readthedocs.io/benchmarking/benchmark.html) or read on [how to work with ontologizers](https://ontolearner.readthedocs.io/ontologizer/ontology_modularization.html). +> Explore [150+ ready-to-use ontologies](https://ontolearner.readthedocs.io/benchmarking/benchmark.html) or learn [how to work with ontologizers](https://ontolearner.readthedocs.io/ontologizer/ontology_modularization.html). -**Learner Models**: +--- + +### Retriever-Based Learner + +Use a dense retriever model to perform non-taxonomic relation extraction: ```python from ontolearner import AutoRetrieverLearner, AgrO, train_test_split, evaluation_report -# 1. Programmatic import of an ontology +# Load and extract ontology data ontology = AgrO() ontology.load() - -# 2. Load tasks datasets ontological_data = ontology.extract() -# 3. Split into train and test sets +# Split into train and test sets train_data, test_data = train_test_split(ontological_data, test_size=0.2, random_state=42) -# 4. Initialize Learner +# Initialize and load a retriever-based learner task = 'non-taxonomic-re' ret_learner = AutoRetrieverLearner(top_k=5) ret_learner.load(model_id='sentence-transformers/all-MiniLM-L6-v2') -# 5. Fit the model to training data and do the predict +# Fit on training data and predict on test data ret_learner.fit(train_data, task=task) predicts = ret_learner.predict(test_data, task=task) -# 6. Evaluation +# Evaluate predictions truth = ret_learner.tasks_ground_truth_former(data=test_data, task=task) metrics = evaluation_report(y_true=truth, y_pred=predicts, task=task) print(metrics) ``` -Other learners: -* [LLM-Based Learner](https://ontolearner.readthedocs.io/learners/llm.html) -* [RAG-Based Learner](https://ontolearner.readthedocs.io/learners/rag.html) -**LearnerPipeline**: The OntoLearner also offers a streamlined `LearnerPipeline` class that simplifies the entire process of initializing, training, predicting, and evaluating a RAG setup into a single call. +Other available learners: +- [LLM-Based Learner](https://ontolearner.readthedocs.io/learners/llm.html) +- [RAG-Based Learner](https://ontolearner.readthedocs.io/learners/rag.html) +--- +### LearnerPipeline + +`LearnerPipeline` consolidates the entire workflow — initialization, training, prediction, and evaluation — into a single call: ```python -# Import core components from the OntoLearner library from ontolearner import LearnerPipeline, AgrO, train_test_split -# Load the AgrO ontology, which includes structured agricultural knowledge +# Load ontology and extract data ontology = AgrO() -ontology.load() # Load ontology data (e.g., entities, relations, metadata) +ontology.load() -# Extract relation instances from the ontology and split them into training and test sets train_data, test_data = train_test_split( - ontology.extract(), # Extract annotated (head, tail, relation) triples - test_size=0.2, # 20% for evaluation - random_state=42 # Ensures reproducible splits + ontology.extract(), + test_size=0.2, + random_state=42 ) -# Initialize the learning pipeline using a dense retriever +# Initialize the pipeline with a dense retriever pipeline = LearnerPipeline( - retriever_id='sentence-transformers/all-MiniLM-L6-v2', # Hugging Face model ID for retrieval - batch_size=10, # Number of samples to process per batch (if batching is enabled internally) - top_k=5 # Retrieve top-5 most relevant support instance per query + retriever_id='sentence-transformers/all-MiniLM-L6-v2', + batch_size=10, + top_k=5 ) -# Run the pipeline on the training and test data -# The pipeline performs: fit() → predict() → evaluate() in sequence +# Run: fit → predict → evaluate outputs = pipeline( train_data=train_data, test_data=test_data, - evaluate=True, # If True, computes precision, recall, and F1-score - task='non-taxonomic-re' # Specifies that we are doing non-taxonomic relation prediction + evaluate=True, + task='non-taxonomic-re' ) -# Print the evaluation metrics (precision, recall, F1) print("Metrics:", outputs['metrics']) - -# Print the total elapsed time for training and evaluation print("Elapsed time:", outputs['elapsed_time']) - -# Print the full output dictionary (includes predictions) -print(outputs) ``` +--- + ## ⭐ Contribution -We welcome contributions to enhance OntoLearner and make it even better! Please review our contribution guidelines in [CONTRIBUTING.md](CONTRIBUTING.md) before getting started. You are also welcome to assist with the ongoing maintenance by referring to [MAINTENANCE.md](MAINTENANCE.md). Your support is greatly appreciated. +We welcome contributions of all kinds — bug reports, new features, documentation improvements, or new ontologies! +Please review our guidelines before getting started: +- [CONTRIBUTING.md](CONTRIBUTING.md) — contribution guidelines +- [MAINTENANCE.md](MAINTENANCE.md) — ongoing maintenance notes -If you encounter any issues or have questions, please submit them in the [GitHub issues tracker](https://github.com/sciknoworg/OntoLearner/issues). +For bugs or questions, please open an issue in the [GitHub Issue Tracker](https://github.com/sciknoworg/OntoLearner/issues). +--- ## 💡 Acknowledgements -If you find this repository helpful or use OntoLearner in your work or research, feel free to cite our publication: +If OntoLearner is useful in your research or work, please consider citing one of our publications: ```bibtex @inproceedings{babaei2023llms4ol, - title={LLMs4OL: Large language models for ontology learning}, - author={Babaei Giglou, Hamed and D’Souza, Jennifer and Auer, S{\"o}ren}, - booktitle={International Semantic Web Conference}, - pages={408--427}, - year={2023}, - organization={Springer} + title = {LLMs4OL: Large Language Models for Ontology Learning}, + author = {Babaei Giglou, Hamed and D'Souza, Jennifer and Auer, S{\"o}ren}, + booktitle = {International Semantic Web Conference}, + pages = {408--427}, + year = {2023}, + organization = {Springer} } ``` -or: + ```bibtex @software{babaei_giglou_2025_15399783, - author = {Babaei Giglou, Hamed and D'Souza, Jennifer and Aioanei, Andrei and Mihindukulasooriya, Nandana and Auer, Sören}, - title = {OntoLearner: A Modular Python Library for Ontology Learning with LLMs}, - month = may, - year = 2025, - publisher = {Zenodo}, - version = {v1.3.0}, - doi = {10.5281/zenodo.15399783}, - url = {https://doi.org/10.5281/zenodo.15399783}, + author = {Babaei Giglou, Hamed and D'Souza, Jennifer and Aioanei, Andrei + and Mihindukulasooriya, Nandana and Auer, Sören}, + title = {OntoLearner: A Modular Python Library for Ontology Learning with LLMs}, + month = may, + year = 2025, + publisher = {Zenodo}, + version = {v1.3.0}, + doi = {10.5281/zenodo.15399783}, + url = {https://doi.org/10.5281/zenodo.15399783} } ``` -*** +--- -This software is archived in Zenodo under the DOI [![DOI](https://zenodo.org/badge/913867999.svg)](https://doi.org/10.5281/zenodo.15399773) and is licensed under [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT). +This software is archived on Zenodo under [![DOI](https://zenodo.org/badge/913867999.svg)](https://doi.org/10.5281/zenodo.15399773) and is licensed under [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT). diff --git a/docs/source/benchmarking/agriculture/agro.rst b/docs/source/benchmarking/agriculture/agro.rst index d1edada2..41be71f0 100644 --- a/docs/source/benchmarking/agriculture/agro.rst +++ b/docs/source/benchmarking/agriculture/agro.rst @@ -25,7 +25,7 @@ Agronomy Ontology (AgrO) ======================================================================================================== -An ontology is a formal representation of a disciplinary domain, representing a semantic standard that can be employed to annotate data where key concepts are defined, as well as the relationships that exist between those concepts (Gruber, 2009). Ontologies provide a common language for different kinds of data to be easily interpretable and interoperable allowing easier aggregation and analysis. The Agronomy Ontology (AgrO) provides terms from the agronomy domain that are semantically organized and can facilitate the collection, storage and use of agronomic data, enabling easy interpretation and reuse of the data by humans and machines alike. To fully understand the implications of varying practices within cropping systems and derive insights, it is often necessary to pull together information from data in different disciplinary domains. For example, data on field management, soil, weather and crop phenotypes may need to be aggregated to assess performance of particular crop under different management interventions. However, agronomic data are often collected, described, and stored in inconsistent ways, impeding data comparison, mining, interpretation reuse. The use of standards for metadata and data annotation play a key role in addressing these challenges. While the CG Core Metadata Schema provides a metadata standard to describe agricultural datasets, the Agronomy Ontology enables the description of agronomic data variables using standard terms. +An ontology is a formal representation of a disciplinary domain, representing a semantic standard that can be employed to annotate data where key concepts are defined, as well as the relationships that exist between those concepts (Gruber, 2009). Ontologies provide a common language for different kinds of data to be easily interpretable and interoperable, allowing for easier aggregation and analysis. The Agronomy Ontology (AgrO) provides terms from the agronomy domain that are semantically organized and can facilitate the collection, storage, and use of agronomic data, enabling easy interpretation and reuse of the data by humans and machines alike. To fully understand the implications of varying practices within cropping systems and derive insights, it is often necessary to pull together information from data in different disciplinary domains. For example, data on field management, soil, weather, and crop phenotypes may need to be aggregated to assess performance of a particular crop under different management interventions. However, agronomic data are often collected, described, and stored in inconsistent ways, impeding data comparison, mining, interpretation, and reuse. The use of standards for metadata and data annotation plays a key role in addressing these challenges. While the CG Core Metadata Schema provides a metadata standard to describe agricultural datasets, the Agronomy Ontology enables the description of agronomic data variables using standard terms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/agriculture/agrovoc.rst b/docs/source/benchmarking/agriculture/agrovoc.rst index 0e51dc41..14c252ca 100644 --- a/docs/source/benchmarking/agriculture/agrovoc.rst +++ b/docs/source/benchmarking/agriculture/agrovoc.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,9 @@ AGROVOC Multilingual Thesaurus (AGROVOC) ======================================================================================================== -AGROVOC is a relevant Linked Open Data set about agriculture available for public use and facilitates access and visibility of data across domains and languages. It offers a structured collection of agricultural concepts, terms, definitions and relationships which are used to unambiguously identify resources, allowing standardized indexing processes and making searches more efficient. +AGROVOC is a comprehensive Linked Open Data resource developed and maintained by the Food and Agriculture Organization (FAO) of the United Nations. It provides a structured collection of agricultural concepts, terms, definitions, and relationships that enable unambiguous identification of resources and standardized indexing. As a multilingual thesaurus, AGROVOC supports multiple languages, facilitating access and visibility of agricultural data across domains and languages. The ontology covers diverse agricultural domains including crops, livestock, farm management practices, soil science, and food production. AGROVOC's hierarchical structure enables both broader and narrower term relationships, supporting semantic interoperability and making searches more efficient. The resource is widely used by research institutions, government agencies, and international organizations for data annotation, knowledge organization, and information retrieval. With millions of concept nodes and sophisticated relationship mappings, AGROVOC serves as a critical backbone for agricultural knowledge representation and data integration in the global agricultural community. + +**Example Usage**: Annotate a multilingual agricultural dataset with AGROVOC terms for crops, soil types, and farming practices to enable standardized indexing and cross-language search in international agricultural databases. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/agriculture/atol.rst b/docs/source/benchmarking/agriculture/atol.rst index 4414bc33..40263d23 100644 --- a/docs/source/benchmarking/agriculture/atol.rst +++ b/docs/source/benchmarking/agriculture/atol.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Animal Trait Ontology for Livestock (ATOL) ======================================================================================================== -ATOL (Animal Trait Ontology for Livestock) is an ontology of characteristics defining phenotypes of livestock in their environment (EOL). ATOL aims to: - provide a reference ontology of phenotypic traits of farm animals for the international scientific and educational - communities, farmers, etc.; - deliver this reference ontology in a language which can be used by computers in order to support database management, semantic analysis and modeling; - represent traits as generic as possible for livestock vertebrates; - make the ATOL ontology as operational as possible and closely related to measurement techniques; - structure the ontology in relation to animal production. +ATOL (Animal Trait Ontology for Livestock) is an ontology of characteristics defining phenotypes of livestock in their environment. ATOL aims to: provide a reference ontology of phenotypic traits of farm animals for the international scientific and educational communities, farmers, etc.; deliver this reference ontology in a language which can be used by computers in order to support database management, semantic analysis and modeling; represent traits as generic as possible for livestock vertebrates; make the ATOL ontology as operational as possible and closely related to measurement techniques; and structure the ontology in relation to animal production. + +The ontology employs a class-based modeling approach, defining classes for different types of phenotypic traits, measurement techniques, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. ATOL supports the integration of data from various sources, promoting interoperability and data-driven research in animal science. + +Typical applications of ATOL include the development of new phenotypic trait analysis methods, the optimization of livestock management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, ATOL enhances collaboration and innovation in the field of animal science. + +**Example Usage**: +Annotate a livestock dataset with ATOL terms to specify phenotypic traits, measurement techniques, and related data, enabling semantic search and integration with animal science research platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/agriculture/foodon.rst b/docs/source/benchmarking/agriculture/foodon.rst index e1f6722c..683fe0c5 100644 --- a/docs/source/benchmarking/agriculture/foodon.rst +++ b/docs/source/benchmarking/agriculture/foodon.rst @@ -25,7 +25,7 @@ Food Ontology (FoodON) ======================================================================================================== -FoodOn, the food ontology, contains vocabulary for naming food materials and their anatomical and taxonomic origins, from raw harvested food to processed food products, for humans and domesticated animals. It provides a neutral and ontology-driven standard for government agencies, industry, nonprofits and consumers to name and reference food products and their components throughout the food supply chain. +FoodOn, the Food Ontology, provides comprehensive vocabulary for naming and classifying food materials throughout the entire food supply chain. It encompasses raw harvested foods and their botanical/zoological origins as well as processed food products designed for both human consumption and animal feed. FoodOn integrates anatomical and taxonomic knowledge, enabling precise semantic representation of food items and their components. The ontology is designed as a neutral, ontology-driven standard that bridges the interests of government agencies, industry stakeholders, nonprofits, and consumers. Its hierarchical structure captures relationships between ingredient components, processing methods, and final food products. FoodOn facilitates standardized naming conventions and interoperability across diverse food-related databases and systems. The ontology supports critical applications including food safety traceability, nutritional analysis, dietary research, and supply chain transparency. By providing unambiguous semantic definitions, FoodOn enables automated systems to track food products, ingredients, and allergens, enhancing data integration across the food industry and supporting evidence-based policy decisions in food security and nutrition. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/agriculture/po.rst b/docs/source/benchmarking/agriculture/po.rst index cccf17ca..d42d6e22 100644 --- a/docs/source/benchmarking/agriculture/po.rst +++ b/docs/source/benchmarking/agriculture/po.rst @@ -25,7 +25,7 @@ Plant Ontology (PO) ======================================================================================================== -The Plant Ontology (PO) is a structured vocabulary and database resource that links plant anatomy, morphology and growth and development to plant genomics data. +The Plant Ontology (PO) is a structured vocabulary and database resource that links plant anatomy, morphology and growth and development to plant genomics data. Developed collaboratively by plant biologists and ontology experts, PO provides a comprehensive framework for describing plant structures and developmental stages. The ontology integrates anatomical terms that can be associated with plant genes, enabling researchers to annotate phenotypic data and support comparative genomics across plant species. PO is designed to facilitate seamless data integration and interoperability in plant science research, allowing scientists to discover relationships between genes and plant structures. With its hierarchical organization of plant parts from whole organism level down to cellular structures, PO supports diverse applications including literature curation, genome annotation, and systems biology studies. The ontology is actively maintained by the Planteome project and integrated with other biological ontologies to ensure compatibility and comprehensive semantic representation in the plant science community. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/arts_and_humanities/chordontology.rst b/docs/source/benchmarking/arts_and_humanities/chordontology.rst index 0f8e02c8..a5e0c7f8 100644 --- a/docs/source/benchmarking/arts_and_humanities/chordontology.rst +++ b/docs/source/benchmarking/arts_and_humanities/chordontology.rst @@ -25,7 +25,7 @@ Chord Ontology (ChordOntology) ======================================================================================================== -The Chord Ontology is an ontology for describing chords in musical pieces. +The Chord Ontology is a formal representation for describing and classifying chords in musical compositions. It provides a structured vocabulary for representing harmonic concepts and chord structures, enabling precise annotation and analysis of music at the semantic level. The ontology captures essential chord properties including chord type (major, minor, diminished, augmented), root note, and constituent pitch classes. It facilitates semantic annotation of audio files, musical scores, and music information retrieval systems, allowing researchers and musicians to query and discover musical pieces based on harmonic content. The ontology integrates with broader music theory frameworks and supports interoperability with other music-related ontologies. By formalizing chord relationships and structures, the Chord Ontology enables computational music analysis, music recommendation systems, and digital musicology applications. It provides a common framework for music annotation across diverse platforms and datasets, supporting music education, composition analysis, and music information systems development. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/arts_and_humanities/icon.rst b/docs/source/benchmarking/arts_and_humanities/icon.rst index 6cc26267..96b82c9b 100644 --- a/docs/source/benchmarking/arts_and_humanities/icon.rst +++ b/docs/source/benchmarking/arts_and_humanities/icon.rst @@ -25,7 +25,7 @@ Icon Ontology (ICON) ======================================================================================================== -The ICON ontology deals with high granularity art interpretation. It was developed by conceptualizing Panofsky's theory of levels of interpretation, therefore artworks can be described according to Pre-iconographical, Iconographical and Iconological information. +The ICON ontology provides a formal framework for high-granularity art interpretation and analysis. It was developed by conceptualizing Panofsky's theory of levels of interpretation, enabling artworks to be systematically described according to three complementary analytical perspectives: Pre-iconographical (visual elements and their formal properties), Iconographical (symbolic meaning and subject matter), and Iconological (deeper cultural and philosophical context). This three-level approach enables comprehensive semantic annotation of artworks, capturing both surface-level visual descriptions and deeper interpretive insights. The ontology supports structured knowledge representation of artistic elements, iconographic themes, cultural references, and symbolic meanings. It facilitates semantic interoperability in digital art collections, museum databases, and art history research platforms. The ICON ontology enables advanced search and discovery capabilities based on artistic interpretation levels, supporting art historians, curators, and researchers in analyzing and understanding artworks. It also supports linked data integration with other cultural heritage ontologies and knowledge bases, enabling rich cross-domain art historical research and interpretation analysis. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/arts_and_humanities/musicontology.rst b/docs/source/benchmarking/arts_and_humanities/musicontology.rst index f49ec5d7..587e007d 100644 --- a/docs/source/benchmarking/arts_and_humanities/musicontology.rst +++ b/docs/source/benchmarking/arts_and_humanities/musicontology.rst @@ -25,7 +25,7 @@ Music Ontology (MusicOntology) ======================================================================================================== -The Music Ontology Specification provides main concepts and properties fo describing music (i.e. artists, albums and tracks) on the Semantic Web. +The Music Ontology Specification provides a comprehensive framework for describing music and related entities on the Semantic Web. It defines core concepts and properties for representing artists, albums, tracks, performances, and musical relationships. The ontology enables standardized music metadata annotation, facilitating interoperability across music information systems, streaming platforms, and digital libraries. It supports rich description of musical works including production details, distribution information, and artistic collaborations. The Music Ontology integrates with other semantic web vocabularies and allows linking of music resources with external datasets and knowledge bases. It enables music recommendation systems, search engines, and music discovery applications to leverage structured semantic data. The ontology supports various music-related use cases including discography management, performance tracking, playlist creation, and music history documentation. By providing a common framework for music representation, the Music Ontology facilitates semantic data integration across the music industry and enables advanced music information retrieval and analysis capabilities. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/arts_and_humanities/nomisma.rst b/docs/source/benchmarking/arts_and_humanities/nomisma.rst index 05fe4619..0e99de67 100644 --- a/docs/source/benchmarking/arts_and_humanities/nomisma.rst +++ b/docs/source/benchmarking/arts_and_humanities/nomisma.rst @@ -25,7 +25,7 @@ Nomisma Ontology (Nomisma) ======================================================================================================== -Nomisma Ontology is a collaborative project to provide stable digital representations of numismatic concepts according to the principles of Linked Open Data. These take the form of http URIs that provide access to the information about a concept in various formats. The project is a collaborative effort of the American Numismatic Society and the Institute for the Study of the Ancient World at New York University. +The Nomisma Ontology is a collaborative project that provides stable, standardized digital representations of numismatic concepts following the principles of Linked Open Data. It offers HTTP URIs that provide persistent access to information about numismatic entities in multiple formats, enabling seamless integration with other linked open data resources. Developed collaboratively by the American Numismatic Society and the Institute for the Study of the Ancient World at New York University, Nomisma represents a comprehensive framework for describing coins, denominations, mints, rulers, and monetary systems across different periods and cultures. The ontology facilitates semantic annotation of numismatic data, supporting interoperability across digital coin collections, archaeological databases, and historical research platforms. It enables advanced querying and analysis capabilities for numismatists, archaeologists, and historians seeking to understand monetary systems and economic aspects of historical societies. The ontology integrates with broader cultural heritage and historical linked data ecosystems, supporting cross-domain research on ancient economies, trade networks, and political history. By providing standardized semantic representations, Nomisma enhances discoverability and reusability of numismatic data in the global digital humanities and cultural heritage communities. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/arts_and_humanities/timelineontology.rst b/docs/source/benchmarking/arts_and_humanities/timelineontology.rst index 95669aaf..61f4378b 100644 --- a/docs/source/benchmarking/arts_and_humanities/timelineontology.rst +++ b/docs/source/benchmarking/arts_and_humanities/timelineontology.rst @@ -25,7 +25,7 @@ Timeline Ontology (TimelineOntology) ======================================================================================================== -The Timeline Ontology is centered around the notion of timeline, seen here as a way to identify a temporal backbone. A timeline may support a signal, a video, a score, a work, etc. +The Timeline Ontology provides a formal framework for representing and managing temporal information in multimedia and music contexts. It centers around the notion of timelines as temporal backbones that can support various types of media including signals, videos, musical scores, and musical works. The ontology enables precise temporal annotation and synchronization of multimedia elements, allowing for structured representation of time-based relationships between different media components. It supports rich temporal modeling including durations, intervals, and temporal landmarks within multimedia documents. The Timeline Ontology facilitates content synchronization across different representations, such as aligning audio signals with musical notation or video with accompanying metadata. It is particularly valuable for music information retrieval systems, multimedia annotation tools, and digital humanities research. The ontology enables advanced applications including temporal querying of multimedia content, cross-media alignment, and time-aware metadata management. By providing a common temporal framework, the Timeline Ontology supports interoperability in music and media analysis systems, enabling researchers and practitioners to work with complex temporal structures in a standardized, machine-readable format. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/biology_and_life_sciences/biopax.rst b/docs/source/benchmarking/biology_and_life_sciences/biopax.rst index 9106c0cb..4aac998b 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/biopax.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/biopax.rst @@ -25,7 +25,9 @@ Biological Pathways Exchange (BioPAX) ======================================================================================================== -BioPAX is a standard language that aims to enable integration, exchange, visualization and analysis of biological pathway data. Specifically, BioPAX supports data exchange between pathway data groups and thus reduces the complexity of interchange between data formats by providing an accepted standard format for pathway data. +BioPAX (Biological Pathways Exchange) is a standard RDF/OWL-based language and ontology for exchanging, integrating, and analyzing biological pathway data. It enables comprehensive representation of molecular interaction networks, including biochemical reactions, gene regulatory pathways, signaling cascades, and transport processes. BioPAX models core pathway concepts such as Interactions (reactions, complexes), Participants (proteins, small molecules), Pathways (sequences of interactions), and their physical and functional properties. The ontology is designed to reduce complexity in data interchange by providing a unified format that bridges disparate pathway databases, modeling tools, and computational analysis platforms. It supports interoperability across systems like Reactome, KEGG, and other pathway databases, facilitating systems biology analysis and network visualization. + +**Example Usage**: Represent a phosphorylation reaction as a BioPAX BiochemicalReaction with specific proteins as catalysts and substrates, linked to cellular locations and regulatory conditions. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/biology_and_life_sciences/efo.rst b/docs/source/benchmarking/biology_and_life_sciences/efo.rst index d32741b2..80d3f206 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/efo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/efo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Experimental Factor Ontology (EFO) ======================================================================================================== -The Experimental Factor Ontology (EFO) provides a systematic description of many experimental variables available in EBI databases, and for projects such as the GWAS catalog. It combines parts of several biological ontologies, such as UBERON anatomy, ChEBI chemical compounds, and Cell Ontology. The scope of EFO is to support the annotation, analysis and visualization of data handled by many groups at the EBI and as the core ontology for Open Targets. EFO is developed by the EMBL-EBI Samples, Phenotypes and Ontologies Team (SPOT). +The Experimental Factor Ontology (EFO) is a comprehensive ontology developed to provide systematic, standardized descriptions of experimental variables and factors in biological and biomedical research. EFO integrates terms from multiple biological ontologies, including UBERON (anatomy), ChEBI (chemical compounds), and Cell Ontology, to support the annotation, analysis, and visualization of experimental data. It is widely used for annotating datasets in EBI databases, the GWAS catalog, and as the core ontology for Open Targets. EFO enables semantic interoperability, data integration, and advanced queries across diverse experimental datasets, facilitating reproducibility and meta-analysis. The ontology is actively maintained by the EMBL-EBI Samples, Phenotypes and Ontologies Team (SPOT) and is continuously updated to reflect new experimental techniques and research needs. By providing a unified framework for describing experimental factors, EFO supports data sharing, discovery, and knowledge integration in genomics, transcriptomics, and other life sciences domains. + +**Example Usage**: +Annotate a gene expression dataset with EFO terms to specify experimental variables such as tissue type, disease state, treatment, and assay platform, enabling cross-study comparison and meta-analysis. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/biology_and_life_sciences/go.rst b/docs/source/benchmarking/biology_and_life_sciences/go.rst index 50b6b50f..bfdf1929 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/go.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/go.rst @@ -25,7 +25,9 @@ Gene Ontology (GO) ======================================================================================================== -The Gene Ontology (GO) Provides structured controlled vocabularies for the annotation of gene products with respect to their molecular function, cellular component, and biological role. +The Gene Ontology (GO) is a comprehensive resource that provides structured controlled vocabularies for the annotation of gene products with respect to their molecular function, cellular component, and biological process role. Developed collaboratively by a consortium of model organism databases and bioinformatics resources, GO enables consistent annotation of genes and proteins across diverse species and databases. The ontology is organized into three hierarchical namespaces: Biological Process (GO:BP) describing what the gene product does in biological context, Molecular Function (GO:MF) characterizing its biochemical activity, and Cellular Component (GO:CC) indicating where it functions. GO supports sophisticated biological data analysis, enabling researchers to discover functional similarities between genes, identify enriched biological processes in genomics datasets, and understand relationships between genes in biological systems. + +**Example Usage**: Annotate a protein like "TP53" with GO terms such as "GO:0006294 (nucleotide-excision repair)" for process, "GO:0003677 (DNA binding)" for molecular function, and "GO:0005634 (nucleus)" for cellular component. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/biology_and_life_sciences/lifo.rst b/docs/source/benchmarking/biology_and_life_sciences/lifo.rst index a58e3b5f..cd2efb66 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/lifo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/lifo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Life Ontology (LifO) ======================================================================================================== -The Life Ontology (LifO) is an ontology of the life of organism. LifO represents the life processes of organisms and related entities and relations. LifO is a general purpose ontology that covers the common features associated with different organisms such as unicellular prokaryotes (e.g., E. coli) and multicellular organisms (e.g., human). +The Life Ontology (LifO) is a general-purpose ontology designed to represent the life processes of organisms and their associated entities and relationships. It provides a structured framework for describing common biological features across diverse organisms, including unicellular prokaryotes like E. coli and multicellular organisms such as humans. LifO captures essential biological concepts such as growth, reproduction, metabolism, and adaptation, enabling researchers to model and analyze life processes in a standardized manner. The ontology supports interoperability between biological databases and facilitates the integration of diverse datasets for comparative studies. LifO is particularly useful in systems biology, evolutionary research, and bioinformatics applications, where a unified representation of life processes is essential for data analysis and hypothesis generation. By providing a common vocabulary for life sciences, LifO enhances data sharing, reproducibility, and collaborative research. + +**Example Usage**: +Use LifO to annotate a dataset describing the metabolic pathways of E. coli, linking each pathway to its corresponding life process and associated biological entities, enabling cross-species comparisons and functional analyses. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst b/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst index f48e61c5..7248a0c6 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst @@ -25,7 +25,7 @@ Marine Taxonomy and Life Ontology (MarineTLO) ======================================================================================================== -MarineTLO is a top level ontology, generic enough to provide consistent abstractions or specifications of concepts included in all data models or ontologies of marine data sources and provide the necessary properties to make this distributed knowledge base a coherent source of facts relating observational data with the respective spatiotemporal context and categorical (systematic) domain knowledge. It can be used as the core schema for publishing Linked Data, as well as for setting up integration systems for the marine domain. It can be extended to any level of detail on demand, while preserving monotonicity. For its development and evolution we have adopted an iterative and incremental methodology where a new version is released every two months. For the implementation we use OWL 2, and to evaluate it we use a set of competency queries, formulating the domain requirements provided by the related communities. +MarineTLO is a top level ontology, generic enough to provide consistent abstractions or specifications of concepts included in all data models or ontologies of marine data sources and provide the necessary properties to make this distributed knowledge base a coherent source of facts relating observational data with the respective spatiotemporal context and categorical (systematic) domain knowledge. It can be used as the core schema for publishing Linked Data, as well as for setting up integration systems for the marine domain. It can be extended to any level of detail on demand, while preserving monotonicity. For its development and evolution we have adopted an iterative and incremental methodology where a new version is released every two months. For the implementation we use OWL 2, and to evaluate it we use a set of competency queries, formulating the domain requirements provided by the related communities. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/biology_and_life_sciences/mged.rst b/docs/source/benchmarking/biology_and_life_sciences/mged.rst index 4874f4b9..3854f28a 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/mged.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/mged.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ MGED Ontology (MGED) ======================================================================================================== -An ontology for microarray experiments in support of MAGE v.1. Concepts, definitions, terms, and resources for standardized description of a microarray experiment in support of MAGE v.1. The MGED ontology is divided into the MGED Core ontology which is intended to be stable and in synch with MAGE v.1; and the MGED Extended ontology which adds further associations and classes not found in MAGE v.1 +The MGED Ontology (MGED) is a domain-specific ontology designed to standardize the description of microarray experiments. It provides a structured vocabulary for representing experimental designs, protocols, and data in the context of microarray gene expression studies. The ontology is divided into two components: the MGED Core Ontology, which aligns with the Microarray Gene Expression (MAGE) standard, and the MGED Extended Ontology, which introduces additional classes and associations beyond the MAGE specification. MGED facilitates interoperability between microarray data repositories, enabling researchers to share, compare, and analyze experimental data effectively. By providing a common framework for describing experimental metadata, MGED supports reproducibility, data integration, and meta-analysis in functional genomics research. The ontology is widely used in bioinformatics tools and databases to annotate experimental datasets and ensure compliance with community standards. + +**Example Usage**: +Annotate a microarray experiment with MGED terms to describe the experimental design, sample preparation protocols, and data processing steps, ensuring that the dataset is interoperable with other repositories and tools. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/biology_and_life_sciences/mo.rst b/docs/source/benchmarking/biology_and_life_sciences/mo.rst index 01704a7b..693e00b1 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/mo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/mo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Microscopy Ontology (MO) ======================================================================================================== -The Microscopy Ontology (MO) extends the ontological framework of the PMDco. The MO facilitates semantic integration and the interoperable connection of diverse data sources from the fields of microscopy and microanalysis. Consequently, the MO paves the way for new, adaptable data applications and analyses across various experiments and studies +The Microscopy Ontology (MO) is a domain ontology designed to provide a structured framework for describing microscopy and microanalysis experiments, data, and equipment. MO extends the PMDco ontological framework and enables semantic integration and interoperability of diverse data sources in microscopy research. The ontology covers key concepts such as imaging modalities, sample preparation methods, instrument components, acquisition parameters, and data analysis techniques. MO supports the annotation of experimental workflows, facilitating data sharing, reproducibility, and advanced analysis across various microscopy studies. By providing a standardized vocabulary, MO enables the development of adaptable data applications and cross-experiment comparisons in materials science, biology, and medical research. The ontology is actively maintained and extended to incorporate new microscopy techniques and research requirements. + +**Example Usage**: +Annotate a microscopy dataset with MO terms to specify the imaging modality (e.g., "scanning electron microscopy"), sample preparation method, instrument configuration, and analysis workflow, enabling semantic search and integration with other microscopy data sources. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/biology_and_life_sciences/npo.rst b/docs/source/benchmarking/biology_and_life_sciences/npo.rst index a02ef8c3..40a52b98 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/npo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/npo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ NanoParticle Ontology (NPO) ======================================================================================================== -NanoParticle Ontology (NPO) is developed within the framework of the Basic Formal Ontology (BFO), and implemented in the Ontology Web Language (OWL) using well-defined ontology design principles. The NPO was developed to represent knowledge underlying the preparation, chemical composition, and characterization of nanomaterials involved in cancer research. Public releases of the NPO are available through BioPortal website, maintained by the National Center for Biomedical Ontology. Mechanisms for editorial and governance processes are being developed for the maintenance, review, and growth of the NPO. +The NanoParticle Ontology (NPO) is a domain ontology developed within the Basic Formal Ontology (BFO) framework to represent knowledge about the preparation, chemical composition, and characterization of nanomaterials, especially in cancer research and nanomedicine. NPO provides a structured vocabulary for describing nanoparticle types, synthesis methods, physicochemical properties, surface modifications, and biological interactions. The ontology supports semantic annotation of nanomaterial data, enabling interoperability, data integration, and advanced queries across toxicology, biomedical, and materials science databases. NPO is publicly available through BioPortal and is maintained by the National Center for Biomedical Ontology, with ongoing editorial and governance processes for review and growth. By providing a standardized framework, NPO facilitates reproducibility, regulatory compliance, and knowledge sharing in nanotechnology research. The ontology is actively extended to incorporate new nanomaterial types, experimental techniques, and application domains as the field evolves. + +**Example Usage**: +Annotate a nanomedicine study with NPO terms to specify nanoparticle type (e.g., "gold nanoparticle"), synthesis method, surface coating, and biological assay results, enabling cross-study comparison and regulatory reporting. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/biology_and_life_sciences/pato.rst b/docs/source/benchmarking/biology_and_life_sciences/pato.rst index 283ead56..36d60915 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/pato.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/pato.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Phenotype and Trait Ontology (PATO) ======================================================================================================== -An ontology of phenotypic qualities (properties, attributes or characteristics). +The Phenotype and Trait Ontology (PATO) is a structured vocabulary designed to describe phenotypic qualities, attributes, and traits across a wide range of biological organisms. It provides a standardized framework for annotating and analyzing phenotypic data, enabling researchers to compare and integrate data across species and studies. PATO defines qualities such as color, shape, size, and behavior, and links them to specific biological entities and contexts. The ontology is widely used in genetics, developmental biology, and evolutionary studies to describe phenotypic variations and their underlying genetic and environmental factors. By providing a common language for phenotypic traits, PATO facilitates data sharing, integration, and computational analysis in the life sciences. The ontology is actively maintained and updated to incorporate new terms and relationships as research advances. + +**Example Usage**: +Annotate a genetic study with PATO terms to describe phenotypic traits such as "PATO:0000323 (red coloration)" for a flower petal or "PATO:0000383 (increased size)" for a specific organ, enabling cross-study comparisons and meta-analyses. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/afo.rst b/docs/source/benchmarking/chemistry/afo.rst index aa986f93..1945d0dd 100644 --- a/docs/source/benchmarking/chemistry/afo.rst +++ b/docs/source/benchmarking/chemistry/afo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Allotrope Foundation Ontology (AFO) ======================================================================================================== -The AFO is an ontology suite that provides a standard vocabulary and semantic model for the representation of laboratory analytical processes. The AFO suite is aligned at the upper layer to the Basic Formal Ontology (BFO). The core domains modeled include, Equipment, Material, Process, and Results. This artifact contains all triples of Allotrope Foundation Merged Without QUDT Ontology Suite (REC/2023/12) together with triples inferred with HermiT. +The Allotrope Foundation Ontology (AFO) is a comprehensive ontology suite designed to standardize the representation of laboratory analytical processes. It provides a semantic model and controlled vocabulary for describing key domains such as Equipment, Material, Process, and Results. The AFO is aligned with the Basic Formal Ontology (BFO) at its upper layer, ensuring compatibility with other ontological frameworks. This ontology suite is particularly valuable for integrating data from diverse laboratory systems, enabling semantic interoperability and facilitating advanced data analysis. By providing explicit definitions and relationships, the AFO supports the automation of laboratory workflows, enhances data reproducibility, and improves the traceability of analytical processes. Researchers and organizations can use the AFO to annotate experimental data, describe laboratory protocols, and ensure compliance with data standards. + +**Example Usage**: +Annotate a laboratory experiment with AFO terms to specify the equipment used (e.g., "mass spectrometer"), the materials analyzed (e.g., "chemical sample"), and the processes performed (e.g., "chromatography"), along with the results obtained. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/chebi.rst b/docs/source/benchmarking/chemistry/chebi.rst index 61560513..5af6773c 100644 --- a/docs/source/benchmarking/chemistry/chebi.rst +++ b/docs/source/benchmarking/chemistry/chebi.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Chemical Entities of Biological Interest (ChEBI) ======================================================================================================== -Chemical Entities of Biological Interest (ChEBI) is a dictionary of molecular entities focused on ‘small’ chemical compounds. The term ‘molecular entity’ refers to any constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc., identifiable as a separately distinguishable entity. The molecular entities in question are either products of nature or synthetic products used to intervene in the processes of living organisms. ChEBI incorporates an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. +Chemical Entities of Biological Interest (ChEBI) is a comprehensive ontology and dictionary of molecular entities, focusing on small chemical compounds. It provides a structured vocabulary for describing constitutionally or isotopically distinct atoms, molecules, ions, radicals, complexes, and other molecular entities. ChEBI includes both naturally occurring substances and synthetic products used in biological systems. The ontology incorporates an ontological classification system, specifying relationships between molecular entities and their parent or child classes. ChEBI is widely used in bioinformatics, cheminformatics, and systems biology to annotate chemical data, enabling interoperability between databases and facilitating advanced queries. By providing a standardized framework for describing chemical entities, ChEBI supports data integration, analysis, and sharing across diverse scientific domains. + +**Example Usage**: +Annotate a dataset of metabolites with ChEBI terms to specify their molecular structures and roles in metabolic pathways, such as "ChEBI:15377 (glucose)" or "ChEBI:15378 (ATP)." Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/cheminf.rst b/docs/source/benchmarking/chemistry/cheminf.rst index a4590d3c..736f842d 100644 --- a/docs/source/benchmarking/chemistry/cheminf.rst +++ b/docs/source/benchmarking/chemistry/cheminf.rst @@ -25,7 +25,9 @@ Chemical Information Ontology (CHEMINF) ======================================================================================================== -The chemical information ontology (cheminf) describes information entities about chemical entities. It provides qualitative and quantitative attributes to richly describe chemicals. Includes terms for the descriptors commonly used in cheminformatics software applications and the algorithms which generate them. +The Chemical Information Ontology (CHEMINF) provides a comprehensive vocabulary for representing and describing information entities and computational descriptors about chemical compounds and substances. It formalizes chemical descriptors, properties, and computational methods used in cheminformatics, enabling standardized representation of molecular attributes derived from chemical informatics tools and algorithms. CHEMINF captures qualitative and quantitative descriptors including molecular weight, logP, HOMO/LUMO energies, and topological indices used for chemical structure analysis and prediction. The ontology is designed to support semantic interoperability in chemistry databases, computational chemistry platforms, and drug discovery systems by providing unambiguous definitions of chemical information concepts. CHEMINF integrates with other chemistry ontologies (ChEBI, MOP, RXNO) to link chemical structures with their calculated properties and computational methods. + +**Example Usage**: Represent a computed molecular descriptor as a CHEMINF information entity linked to a ChEBI chemical structure, specifying the calculation method (e.g., "Wildman-Crippen LogP") and the resulting numeric value. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/chiro.rst b/docs/source/benchmarking/chemistry/chiro.rst index 9aeb8e90..23919bb4 100644 --- a/docs/source/benchmarking/chemistry/chiro.rst +++ b/docs/source/benchmarking/chemistry/chiro.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ CHEBI Integrated Role Ontology (CHIRO) ======================================================================================================== -CHEBI provides a distinct role hierarchy. Chemicals in the structural hierarchy are connected via a 'has role' relation. CHIRO provides links from these roles to useful other classes in other ontologies. This will allow direct connection between chemical structures (small molecules, drugs) and what they do. This could be formalized using 'capable of', in the same way Uberon and the Cell Ontology link structures to processes. +The CHEBI Integrated Role Ontology (CHIRO) is a specialized ontology designed to provide a structured role hierarchy for chemicals. It connects chemicals in the structural hierarchy via a 'has role' relation, linking them to relevant classes in other ontologies. This enables the formalization of relationships between chemical structures (e.g., small molecules, drugs) and their functional roles, such as their biological or chemical activities. CHIRO facilitates the integration of chemical data with biological and biomedical ontologies, supporting applications in drug discovery, chemical informatics, and systems biology. By providing a standardized framework for describing chemical roles, CHIRO enhances data interoperability and enables advanced semantic queries across chemical and biological datasets. The ontology is particularly useful for linking chemical entities to their roles in biological processes, such as enzyme inhibitors, signaling molecules, or structural components. + +**Example Usage**: +Annotate a dataset of small molecules with CHIRO terms to specify their roles, such as "enzyme inhibitor" or "neurotransmitter," and link these roles to relevant biological processes or pathways. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/chmo.rst b/docs/source/benchmarking/chemistry/chmo.rst index bba07e9f..11141b06 100644 --- a/docs/source/benchmarking/chemistry/chmo.rst +++ b/docs/source/benchmarking/chemistry/chmo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Chemical Methods Ontology (ChMO) ======================================================================================================== -The Chemical Methods Ontology contains more than 3000 classes and describes methods used to: - collect data in chemical experiments, such as mass spectrometry and electron microscopy. - prepare and separate material for further analysis, such as sample ionisation, chromatography, and electrophoresis - synthesise materials, such as epitaxy and continuous vapour deposition It also describes the instruments used in these experiments, such as mass spectrometers and chromatography columns and their outputs. +The Chemical Methods Ontology (ChMO) is a comprehensive ontology that provides a structured vocabulary for describing chemical methods, experimental techniques, and analytical procedures used in chemistry and related sciences. ChMO contains over 3000 classes covering methods for data collection (e.g., mass spectrometry, electron microscopy), sample preparation and separation (e.g., ionisation, chromatography, electrophoresis), and material synthesis (e.g., epitaxy, vapor deposition). The ontology also describes the instruments and equipment used in these experiments, such as mass spectrometers and chromatography columns, as well as the outputs and results generated. ChMO enables semantic annotation of experimental workflows, facilitating data integration, reproducibility, and advanced analysis across chemical research and laboratory information systems. By providing a standardized framework, ChMO supports interoperability between chemical databases, electronic lab notebooks, and computational tools. The ontology is actively maintained and extended to incorporate new methods and technologies as the field evolves. + +**Example Usage**: +Annotate a chemical experiment with ChMO terms to specify the analytical method (e.g., "liquid chromatography-mass spectrometry"), sample preparation steps, instrument configuration, and data outputs, enabling semantic search and integration with other chemical research datasets. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/fix.rst b/docs/source/benchmarking/chemistry/fix.rst index 1d56acce..2773ec5a 100644 --- a/docs/source/benchmarking/chemistry/fix.rst +++ b/docs/source/benchmarking/chemistry/fix.rst @@ -25,7 +25,9 @@ FIX Ontology (FIX) ======================================================================================================== -An ontology of physico-chemical methods and properties. +The FIX (Physico-Chemical Methods and Properties) Ontology provides a systematic vocabulary for describing and classifying physico-chemical methods and their associated properties. It represents analytical and experimental techniques used in chemistry and materials science, including measurement methods, analytical procedures, and the physical and chemical properties they determine. The ontology captures relationships between methods and properties, enabling precise description of experimental workflows and results in laboratory and industrial settings. It supports semantic interoperability in chemistry databases, laboratory information systems (LIMS), and scientific data repositories. + +**Example Usage**: Represent a mass spectrometry measurement as an instance of FIX:AnalyticalMethod linked to FIX:ChemicalProperty instances such as molecular weight or compound identification. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/massspectrometry.rst b/docs/source/benchmarking/chemistry/massspectrometry.rst index 0645cc6b..cdbb4532 100644 --- a/docs/source/benchmarking/chemistry/massspectrometry.rst +++ b/docs/source/benchmarking/chemistry/massspectrometry.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -27,6 +25,11 @@ Mass Spectrometry Ontology (MassSpectrometry) A structured controlled vocabulary for the annotation of experiments concerned with proteomics mass spectrometry. +The Mass Spectrometry Ontology (MassSpectrometry) is a structured controlled vocabulary designed to annotate experiments in the field of proteomics mass spectrometry. It provides a standardized framework for describing experimental setups, instrumentation, data acquisition methods, and analysis workflows. The ontology captures essential concepts such as ionization techniques, mass analyzers, fragmentation methods, and data processing algorithms. By offering a common language for mass spectrometry, it facilitates data sharing, integration, and reproducibility across proteomics studies. The ontology is widely used in bioinformatics pipelines, proteomics databases, and experimental repositories to ensure semantic consistency and interoperability. Researchers can leverage the Mass Spectrometry Ontology to annotate datasets, describe experimental protocols, and enable advanced queries for comparative analyses. + +**Example Usage**: +Annotate a proteomics experiment with MassSpectrometry terms to specify the ionization method (e.g., "electrospray ionization"), mass analyzer type (e.g., "time-of-flight"), and data processing software used for peak detection and quantification. + Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/mop.rst b/docs/source/benchmarking/chemistry/mop.rst index c0faa25e..90accb6b 100644 --- a/docs/source/benchmarking/chemistry/mop.rst +++ b/docs/source/benchmarking/chemistry/mop.rst @@ -25,7 +25,9 @@ Molecular Process Ontology (MOP) ======================================================================================================== -MOP is the molecular process ontology. It contains the molecular processes that underlie the name reaction ontology RXNO, for example cyclization, methylation and demethylation. +The Molecular Process Ontology (MOP) is a systematic vocabulary for describing and classifying molecular-level chemical processes and transformations that occur in organic chemistry. It provides formal definitions of common molecular processes such as cyclization, methylation, demethylation, oxidation, reduction, and other fundamental reaction steps underlying named reactions in chemistry. MOP serves as the foundational semantic layer for the Reaction Ontology (RXNO), enabling precise description of chemical reaction mechanisms and their constituent molecular processes. The ontology facilitates integration of chemical databases, computational chemistry platforms, and reaction informatics systems by providing standardized semantic representations of molecular transformations. MOP enables advanced searching and classification of reactions based on their underlying molecular mechanisms. + +**Example Usage**: Represent a cyclization reaction step in RXNO by linking to MOP terms for the specific cyclization type (e.g., "6-membered ring closure"), enabling automated discovery of similar reactions across chemical databases. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/nmrcv.rst b/docs/source/benchmarking/chemistry/nmrcv.rst index 4becef36..870a2ce8 100644 --- a/docs/source/benchmarking/chemistry/nmrcv.rst +++ b/docs/source/benchmarking/chemistry/nmrcv.rst @@ -25,7 +25,7 @@ Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV) ======================================================================================================== -This artefact is an MSI-approved controlled vocabulary primarily developed under COSMOS EU and PhenoMeNal EU governance. The nmrCV is supporting the nmrML XML format with standardized terms. nmrML is a vendor agnostic open access NMR raw data standard. Its primaly role is analogous to the mzCV for the PSI-approved mzML XML format. It uses BFO2.0 as its Top level. This CV was derived from two predecessors (The NMR CV from the David Wishart Group, developed by Joseph Cruz) and the MSI nmr CV developed by Daniel Schober at the EBI. This simple taxonomy of terms (no DL semantics used) serves the nuclear magnetic resonance markup language (nmrML) with meaningful descriptors to amend the nmrML xml file with CV terms. Metabolomics scientists are encouraged to use this CV to annotrate their raw and experimental context data, i.e. within nmrML. The approach to have an exchange syntax mixed of an xsd and CV stems from the PSI mzML effort. The reason to branch out from an xsd into a CV is, that in areas where the terminology is likely to change faster than the nmrML xsd could be updated and aligned, an externally and decentrallised maintained CV can accompensate for such dynamics in a more flexible way. A second reason for this set-up is that semantic validity of CV terms used in an nmrML XML instance (allowed CV terms, position/relation to each other, cardinality) can be validated by rule-based proprietary validators: By means of cardinality specifications and XPath expressions defined in an XML mapping file (an instances of the CvMappingRules.xsd ), one can define what ontology terms are allowed in a specific location of the data model. +This artefact is an MSI-approved controlled vocabulary primarily developed under COSMOS EU and PhenoMeNal EU governance. The nmrCV is supporting the nmrML XML format with standardized terms. nmrML is a vendor-agnostic open access NMR raw data standard. Its primary role is analogous to the mzCV for the PSI-approved mzML XML format. It uses BFO2.0 as its top level. This CV was derived from two predecessors (The NMR CV from the David Wishart Group, developed by Joseph Cruz) and the MSI nmr CV developed by Daniel Schober at the EBI. This simple taxonomy of terms (no DL semantics used) serves the nuclear magnetic resonance markup language (nmrML) with meaningful descriptors to amend the nmrML XML file with CV terms. Metabolomics scientists are encouraged to use this CV to annotate their raw and experimental context data, i.e. within nmrML. The approach to have an exchange syntax mixed of an XSD and CV stems from the PSI mzML effort. The reason to branch out from an XSD into a CV is that in areas where the terminology is likely to change faster than the nmrML XSD could be updated and aligned, an externally and decentralized maintained CV can compensate for such dynamics in a more flexible way. A second reason for this setup is that semantic validity of CV terms used in an nmrML XML instance (allowed CV terms, position/relation to each other, cardinality) can be validated by rule-based proprietary validators: by means of cardinality specifications and XPath expressions defined in an XML mapping file (an instance of the CvMappingRules.xsd), one can define what ontology terms are allowed in a specific location of the data model. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/ontokin.rst b/docs/source/benchmarking/chemistry/ontokin.rst index 5fc76dda..c451c09b 100644 --- a/docs/source/benchmarking/chemistry/ontokin.rst +++ b/docs/source/benchmarking/chemistry/ontokin.rst @@ -25,7 +25,9 @@ Chemical Kinetics Ontology (OntoKin) ======================================================================================================== -OntoKin is an ontology developed for representing chemical kinetic reaction mechanisms. +OntoKin is a comprehensive ontology developed for formal and standardized representation of chemical kinetics data, reaction mechanisms, and kinetic rate parameters used in chemistry and combustion science. It provides structured definitions of reaction mechanisms, including species involved, reaction pathways, elementary steps, and kinetic rate coefficients essential for modeling chemical processes. OntoKin captures important chemical kinetics concepts such as reaction types (forward, reverse, three-body), activation energies, temperature dependencies, and pressure effects on reaction rates. The ontology facilitates data integration and knowledge sharing in computational chemistry, combustion research, and chemical process modeling by providing unambiguous semantic representations. OntoKin supports automated reasoning and knowledge discovery in chemical databases, enabling researchers to search, compare, and reuse kinetic mechanisms across different applications. + +**Example Usage**: Represent a chemical reaction mechanism with OntoKin terms for species (CH4, O2, H2O), elementary reaction steps with activation energies, and temperature-dependent rate coefficients using Arrhenius or modified Arrhenius equations. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/proco.rst b/docs/source/benchmarking/chemistry/proco.rst index 25fde4cd..dda4abb4 100644 --- a/docs/source/benchmarking/chemistry/proco.rst +++ b/docs/source/benchmarking/chemistry/proco.rst @@ -25,7 +25,9 @@ PROcess Chemistry Ontology (PROCO) ======================================================================================================== -PROCO (PROcess Chemistry Ontology) is a formal ontology that aims to standardly represent entities and relations among entities in the domain of process chemistry. +PROCO (PROcess Chemistry Ontology) is a formal ontology that standardizes representation of entities, relationships, and processes in process chemistry and chemical manufacturing. It provides comprehensive vocabulary for describing chemical reactions, reaction conditions, reactants, products, catalysts, and process equipment used in industrial and laboratory chemical processes. PROCO captures essential process chemistry concepts including reaction parameters (temperature, pressure, time, stirring), process steps (mixing, heating, separation), material flows, and safety-critical information for chemical synthesis. The ontology enables semantic interoperability across chemical engineering databases, laboratory information management systems (LIMS), and process simulation tools by providing explicit, unambiguous definitions. PROCO supports knowledge integration and reasoning in process chemistry research, enabling automated workflow design, process optimization, and risk assessment. + +**Example Usage**: Represent a multi-step synthesis process with PROCO terms for each reaction step including reactants, reaction conditions (temperature and pressure ranges), catalysts, solvents, workup procedures, and desired products with yield information. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/psimod.rst b/docs/source/benchmarking/chemistry/psimod.rst index baed3d17..6c9440cf 100644 --- a/docs/source/benchmarking/chemistry/psimod.rst +++ b/docs/source/benchmarking/chemistry/psimod.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Protein Modifications Ontology (PSIMOD) ======================================================================================================== -PSI-MOD is an ontology developed by the Proteomics Standards Initiative (PSI) that describes protein chemical modifications, logically linked by an is_a relationship in such a way as to form a direct acyclic graph (DAG). The PSI-MOD ontology has more than 45 top-level nodes, and provides alternative hierarchical paths for classifying protein modifications either by the molecular structure of the modification, or by the amino acid residue that is modified. +The Protein Modifications Ontology (PSIMOD) is a comprehensive ontology developed by the Proteomics Standards Initiative (PSI) to describe chemical modifications of proteins. It organizes protein modifications into a directed acyclic graph (DAG) structure, enabling hierarchical classification based on molecular structure or the modified amino acid residue. PSIMOD captures a wide range of protein modifications, including phosphorylation, acetylation, ubiquitination, and glycosylation, providing detailed descriptions of their chemical nature and biological significance. The ontology supports semantic annotation of proteomics datasets, facilitating data integration, analysis, and sharing across proteomics studies. By providing a standardized vocabulary, PSIMOD enhances the reproducibility and interoperability of proteomics research, enabling advanced queries and comparative analyses. + +**Example Usage**: +Annotate a proteomics dataset with PSIMOD terms to specify protein modifications, such as "PSI-MOD:00046 (phosphorylation)" or "PSI-MOD:00048 (acetylation)," and link these modifications to their respective biological pathways. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/rex.rst b/docs/source/benchmarking/chemistry/rex.rst index d51b29fd..c99e0fc8 100644 --- a/docs/source/benchmarking/chemistry/rex.rst +++ b/docs/source/benchmarking/chemistry/rex.rst @@ -25,7 +25,9 @@ Physico-chemical process ontology (REX) ======================================================================================================== -REX is an ontology of physico-chemical processes, i.e. physico-chemical changes occurring in course of time. REX includes both microscopic processes (involving molecular entities or subatomic particles) and macroscopic processes. Some biochemical processes from Gene Ontology (GO Biological process) can be described as instances of REX. +REX is a comprehensive ontology for formal representation of physico-chemical processes, including both microscopic molecular transformations and macroscopic chemical phenomena occurring over time. It provides structured vocabulary for describing processes at different scales: molecular-level processes (involving chemical bonds, molecular rearrangement, electron transfer) and macroscopic processes (phase changes, dissolution, crystallization). REX distinguishes between different process types and captures temporal and causal relationships between processes, enabling precise semantic representation of chemical transformations. The ontology integrates with biological process vocabularies (e.g., Gene Ontology's biological process namespace) to bridge molecular biochemistry and cellular processes. REX facilitates knowledge integration in chemistry databases, computational chemistry platforms, and systems biology models by providing standardized process definitions. + +**Example Usage**: Represent a multi-step chemical transformation process using REX terms to describe molecular-level processes (e.g., nucleophilic substitution, oxidation) linked together in sequence, with temporal ordering and causal dependencies between elementary steps. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/rxno.rst b/docs/source/benchmarking/chemistry/rxno.rst index e496321b..2d614421 100644 --- a/docs/source/benchmarking/chemistry/rxno.rst +++ b/docs/source/benchmarking/chemistry/rxno.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Reaction Ontology (RXNO) ======================================================================================================== -RXNO is the name reaction ontology. It contains more than 500 classes representing organic reactions such as the Diels–Alder cyclization. +The Reaction Ontology (RXNO) is a specialized ontology that provides a comprehensive vocabulary for representing organic chemical reactions. It includes over 500 classes that describe named reactions, such as the Diels–Alder cyclization, and their associated mechanisms. RXNO enables the semantic annotation of chemical reaction data, facilitating data sharing, integration, and advanced querying in cheminformatics and organic chemistry research. By standardizing the representation of chemical reactions, RXNO supports the development of reaction databases, computational chemistry tools, and automated synthesis planning systems. The ontology also captures relationships between reactions, reactants, products, and catalysts, enabling detailed modeling of reaction networks. + +**Example Usage**: +Annotate a reaction database with RXNO terms to specify reaction types, such as "RXNO:000001 (Diels–Alder reaction)," and link these reactions to their reactants, products, and catalysts. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/chemistry/vibso.rst b/docs/source/benchmarking/chemistry/vibso.rst index 88ddf29f..0ac67f78 100644 --- a/docs/source/benchmarking/chemistry/vibso.rst +++ b/docs/source/benchmarking/chemistry/vibso.rst @@ -25,7 +25,9 @@ Vibrational Spectroscopy Ontology (VIBSO) ======================================================================================================== -The Vibration Spectroscopy Ontology defines technical terms with which research data produced in vibrational spectroscopy experiments can be semantically enriched, made machine readable and FAIR. +The Vibrational Spectroscopy Ontology (VIBSO) is a domain-specific vocabulary for comprehensive and standardized semantic description of vibrational spectroscopy experiments and their resulting research data. It provides formal definitions of technical terms and concepts used in vibrational spectroscopy including spectroscopy types (infrared, Raman, NIR), sample preparation methods, measurement parameters, instrumentation, and data analysis procedures. VIBSO enables researchers to annotate spectroscopy datasets with machine-readable semantic metadata, making data FAIR (Findable, Accessible, Interoperable, Reusable) and supporting automated data discovery and integration. The ontology captures the complete experimental context including measurement conditions, instrument specifications, data processing steps, and quality metrics critical for reproducibility and data reuse. VIBSO facilitates semantic interoperability in spectroscopy databases, analytical chemistry platforms, and materials science research by providing unambiguous definitions of spectroscopic concepts. + +**Example Usage**: Annotate a Raman spectroscopy experiment with VIBSO terms including spectroscopy type (Raman), sample material, laser wavelength, temperature conditions, and data processing methods to enable automated discovery of similar spectroscopic studies. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/ecology_and_environment/envo.rst b/docs/source/benchmarking/ecology_and_environment/envo.rst index 1384d8c5..c031c2bb 100644 --- a/docs/source/benchmarking/ecology_and_environment/envo.rst +++ b/docs/source/benchmarking/ecology_and_environment/envo.rst @@ -25,7 +25,9 @@ Environment Ontology (ENVO) ======================================================================================================== -ENVO is an expressive, community ontology which helps humans, machines, and semantic web applications understand environmental entities of all kinds, from microscopic to intergalactic scales. As a FAIR-compliant resource, it promotes interoperability through the concise, controlled description of all things environmental. +ENVO is a comprehensive, expressive community-driven ontology that enables humans, machines, and semantic web applications to understand environmental entities and concepts across multiple scales, from microscopic organisms to astronomical phenomena. It provides standardized vocabulary for describing environmental features including biomes, ecosystems, habitats, environmental materials (air, water, soil), and environmental conditions (temperature, humidity, pollution levels). ENVO captures relationships between environmental entities and enables precise semantic annotation of environmental datasets, research findings, and monitoring data. As a FAIR-compliant resource, ENVO promotes semantic interoperability in environmental science by providing concise, controlled descriptions of environmental concepts using formal ontological definitions. The ontology supports diverse applications including environmental data management, ecology research, climate science studies, and environmental impact assessment. + +**Example Usage**: Annotate an environmental science dataset with ENVO terms such as "tropical rainforest" (biome), "Amazonia" (biogeographic region), and "high soil moisture" (environmental condition) to enable automated discovery of related environmental studies and climate research. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/ecology_and_environment/oeo.rst b/docs/source/benchmarking/ecology_and_environment/oeo.rst index 181dadd2..a305b5bf 100644 --- a/docs/source/benchmarking/ecology_and_environment/oeo.rst +++ b/docs/source/benchmarking/ecology_and_environment/oeo.rst @@ -25,7 +25,10 @@ The Open Energy Ontology (OEO) ======================================================================================================== -The Open Energy Ontology (OEO) is a domain ontology of the energy system analysis context. It is developed as part of the Open Energy Family. The OEO is published on GitHub under an open source license. The language used is the Manchester OWL Syntax, which was chosen because it is user-friendly for editing and viewing differences of edited files. The OEO is constantly being extended. The first version of the OEO has been released on June 11th 2020. A Steering Committee (OEO-SC) was created to accompany the development, increase awareness of the ontology and include it in current projects. +The Open Energy Ontology (OEO) is a comprehensive domain ontology specifically designed for the energy system analysis context, covering concepts, relationships, and entities relevant to energy research and planning. Developed as part of the Open Energy Platform ecosystem, OEO provides standardized terminology for representing energy systems including generation, transmission, distribution, and consumption across diverse energy sources and technologies. The ontology is expressed in Manchester OWL Syntax to ensure user-friendliness for editing and version control, facilitating collaborative development and maintenance. OEO is actively maintained and continuously extended to incorporate emerging energy concepts, technologies, and regulatory frameworks relevant to energy system analysis and planning. The ontology is governed by a Steering Committee (OEO-SC) that ensures quality, alignment with community needs, and integration with ongoing energy research and policy projects. OEO enables standardized data representation, knowledge integration, and automated reasoning about energy systems for research, policy analysis, and strategic planning. + +**Example Usage**: +Annotate an energy system dataset with OEO terms to describe energy sources (solar, wind, biomass), generation capacity, transmission networks, demand patterns, and storage systems to enable automated analysis of energy system configurations and transition scenarios. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/ecology_and_environment/sweet.rst b/docs/source/benchmarking/ecology_and_environment/sweet.rst index 03ef31b6..f07874af 100644 --- a/docs/source/benchmarking/ecology_and_environment/sweet.rst +++ b/docs/source/benchmarking/ecology_and_environment/sweet.rst @@ -25,7 +25,9 @@ Semantic Web for Earth and Environment Technology Ontology (SWEET) ======================================================================================================== -The Semantic Web for Earth and Environment Technology Ontology (SWEET) is an investigation in improving discovery and use of Earth science data, through software understanding of the semantics of web resources. SWEET is a collection of ontologies conceptualizing a knowledge space for Earth system science, represented using the web ontology language (OWL). It includes both orthogonal concepts (space, time, Earth realms, physical quantities, etc.) and integrative science knowledge concepts (phenomena, events, etc.). +SWEET is a comprehensive collection of interconnected ontologies designed to enhance discovery and utilization of Earth science data through semantic understanding of web resources and Earth system science concepts. It conceptualizes a knowledge space for Earth system science including orthogonal (cross-cutting) concepts such as space, time, Earth realms (atmosphere, hydrosphere, lithosphere), physical quantities, and units, alongside integrative science knowledge concepts such as phenomena, events, and processes. SWEET is represented in OWL (Web Ontology Language) to enable automated reasoning and semantic interoperability in Earth science research. The ontology supports integration of heterogeneous Earth science datasets and models by providing shared semantic definitions across atmospheric science, oceanography, geology, and climate science domains. SWEET facilitates Earth science data discovery and knowledge management by enabling semantic search and automated linking of related datasets and research findings. + +**Example Usage**: Annotate a climate dataset with SWEET terms to describe measured phenomena (e.g., "temperature increase"), spatial context (e.g., "atmosphere" realm, "troposphere" layer), temporal extent (e.g., "1980-2020"), and physical quantities (e.g., "degrees Celsius"). Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/education/bibframe.rst b/docs/source/benchmarking/education/bibframe.rst index e81b4bed..7b423adf 100644 --- a/docs/source/benchmarking/education/bibframe.rst +++ b/docs/source/benchmarking/education/bibframe.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Bibliographic Framework Ontology (BIBFRAME) ======================================================================================================== -The Bibframe vocabulary consists of RDF classes and properties used for the description of items cataloged principally by libraries, but may also be used to describe items cataloged by museums and archives. Classes include the three core classes - Work, Instance, and Item - in addition to many more classes to support description. Properties describe characteristics of the resource being described as well as relationships among resources. For example: one Work might be a "translation of" another Work; an Instance may be an "instance of" a particular Bibframe Work. Other properties describe attributes of Works and Instances. For example: the Bibframe property "subject" expresses an important attribute of a Work (what the Work is about), and the property "extent" (e.g. number of pages) expresses an attribute of an Instance. +The Bibliographic Framework Ontology (BIBFRAME) is a comprehensive RDF-based vocabulary developed by the Library of Congress to modernize bibliographic description for libraries, museums, and archives. It provides a structured model for representing bibliographic resources, focusing on three core classes: Work (the abstract creative content), Instance (the physical or digital embodiment), and Item (the specific copy). BIBFRAME supports detailed description of relationships among resources, such as translations, adaptations, and editions, as well as attributes like subject, extent, and publication information. The ontology is designed to facilitate linked data publishing, interoperability, and integration with other metadata standards, enabling richer discovery and reuse of bibliographic information. BIBFRAME is widely adopted by libraries and cultural heritage institutions transitioning from MARC records to semantic web technologies. Its extensible structure allows for domain-specific adaptations and integration with authority files, vocabularies, and digital repositories. + +**Example Usage**: +Describe a library book using BIBFRAME by linking the Work (e.g., "Pride and Prejudice"), its Instance (the 2003 Penguin Classics edition), and the Item (the specific copy held by a library), including relationships to subjects, authors, and related works. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/education/common.rst b/docs/source/benchmarking/education/common.rst index 0980bb41..5ed8a1c8 100644 --- a/docs/source/benchmarking/education/common.rst +++ b/docs/source/benchmarking/education/common.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Common Ontology (Common) ======================================================================================================== -Ontology for the representation of commons elements in the Trias ontology +The Common Ontology provides a foundational vocabulary for representing shared and reusable elements used across the Trias (Transport and Mobility) ontology framework. It defines core concepts such as entities, attributes, relationships, and constraints that are applicable to multiple mobility and transportation domains. The ontology captures common modeling patterns for representing agents, organizations, times, locations, and relationships in a standardized way across diverse transportation contexts. Common Ontology enables semantic interoperability across Trias domain modules (mobility, traffic, vehicles) by providing consistent foundational concepts and relationships. It serves as the shared semantic backbone for specialized domain ontologies in transportation, facilitating knowledge integration and reasoning. + +**Example Usage**: +Use Common Ontology entity types to represent agents (persons, organizations), locations (stops, streets, zones), time concepts, and relationships across transportation domain ontologies that extend or reference Common. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/education/doco.rst b/docs/source/benchmarking/education/doco.rst index 6051b81e..ef938f7a 100644 --- a/docs/source/benchmarking/education/doco.rst +++ b/docs/source/benchmarking/education/doco.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Document Components Ontology (DoCO) ======================================================================================================== -DoCO, the Document Components Ontology, is an OWL 2 DL ontology that provides a general-purpose structured vocabulary of document elements. DoCO has been designed as a general unifying ontological framework for describing different aspects related to the content of scientific and other scholarly texts. Its primary goal has been to improve the interoperability and shareability of academic documents (and related services) when multiple formats are actually used for their storage. +DoCO, the Document Components Ontology, is an OWL 2 DL ontology that provides a general-purpose structured vocabulary of document elements. DoCO has been designed as a general unifying ontological framework for describing different aspects related to the content of scientific and other scholarly texts. Its primary goal has been to improve the interoperability and shareability of academic documents (and related services) when multiple formats are actually used for their storage. The ontology defines a wide range of document components, such as sections, paragraphs, figures, tables, references, and metadata elements, supporting detailed semantic annotation of document structure. DoCO enables automated document processing, content extraction, and knowledge management in digital libraries, publishing platforms, and research repositories. By providing a standardized vocabulary, DoCO facilitates the integration of scholarly content across diverse formats and platforms, enhancing discoverability and reuse of academic knowledge. + +**Example Usage**: +Annotate a scientific article with DoCO terms to specify its sections (introduction, methods, results, discussion), figures, tables, and references, enabling automated extraction and semantic search of document components. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/events/conference.rst b/docs/source/benchmarking/events/conference.rst index fb8a1080..287894c5 100644 --- a/docs/source/benchmarking/events/conference.rst +++ b/docs/source/benchmarking/events/conference.rst @@ -25,7 +25,9 @@ Conference Ontology (Conference) ======================================================================================================== -The conference-ontology is a new self-contained ontology for modeling knowledge about conferences. The conference-ontology adopts the best ontology design practices (e.g., Ontology Design Patterns, ontology reuse and interlinking) and guarantees interoperability with SWC ontology and all other pertinent vocabularies. +The Conference Ontology is a self-contained ontology for modeling conferences, workshops, and related scholarly events. It captures core entities (events, organizers, venues, sessions, papers, posters, and participants) and their relationships, allowing structured representation of program schedules, affiliations, and scholarly communications around conferences. Designed following ontology design patterns and reuse principles, it reuses established vocabularies (e.g., FOAF, BIBO, schema.org) where appropriate and interlinks with the SWC (Semantic Web Conference) ontology to ensure interoperability. The ontology models temporal and spatial aspects (start/end times, room assignments), roles and responsibilities (chairs, speakers, reviewers), and provenance metadata (submission dates, acceptance decisions). Typical applications include conference management systems, semantic search of proceedings, program generation, and linking publications to presentation metadata. + +**Example usage**: represent a conference session as an Event with start/end times, linked to a Room (Venue), containing multiple Talk instances each linked to Speaker agents and associated Paper resources. The ontology supports export to RDF/OWL and integration with digital libraries, repositories, and research discovery services. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/events/icalendar.rst b/docs/source/benchmarking/events/icalendar.rst index 23aa289d..f44a5762 100644 --- a/docs/source/benchmarking/events/icalendar.rst +++ b/docs/source/benchmarking/events/icalendar.rst @@ -25,7 +25,9 @@ iCalendar Vocabulary (iCalendar) ======================================================================================================== -iCalendar is an Internet standard for exchanging calendar and scheduling data across different applications and platforms using a standardized text-based format (.ics). It enables interoperability for events, tasks, and scheduling, supporting features like recurring events, invitations, and time zone adjustments. While widely used in applications like Google Calendar and Outlook, its complexity and partial implementations pose challenges, leading to efforts to integrate it with Semantic Web technologies for enhanced data linking and automation. +iCalendar is an Internet standard and RDF vocabulary for exchanging calendar and scheduling data across applications using a standardized representation (.ics and RDF views). It models core scheduling concepts such as Events, To-Dos, Recurrence Rules, Attendees, Time Zones, and Alarms, enabling interoperable representation of meetings, recurring appointments, and calendar invitations. While the original iCalendar format (.ics) is a text standard widely implemented in client applications (Google Calendar, Outlook), the iCalendar vocabulary expresses these concepts in RDF to support Semantic Web integration, richer linking, and automated reasoning. Key characteristics include explicit recurrence modelling, timezone-aware datatypes, and support for invitations and participation roles. Common applications include calendar synchronization, meeting scheduling services, automated reminders, and semantic linking of event metadata with external datasets. + +**Example usage**: represent a weekly team meeting as an Event with a RRULE for weekly recurrence, linked Attendee agents with roles (organizer, participant), and timezone-aware start/end datetimes. The vocabulary can be combined with other ontologies (e.g., FOAF for people) to enrich event descriptions and support advanced calendar automation. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/events/lode.rst b/docs/source/benchmarking/events/lode.rst index baf4f806..2682e2cd 100644 --- a/docs/source/benchmarking/events/lode.rst +++ b/docs/source/benchmarking/events/lode.rst @@ -25,7 +25,9 @@ Linking Open Descriptions of Events (LODE) ======================================================================================================== -People conventionally refer to an action or occurrence taking place at a certain time at a specific location as an event. This notion is potentially useful for connecting individual facts recorded in the rapidly growing collection of linked data sets and for discovering more complex relationships between data. The LODE provide an overview and comparison of existing event models, looking at the different choices they make of how to represent events. It is a model for publishing records of events as Linked Data. A tools for populating this model and a prototype “event directory” web service, which can be used to locate stable URIs for events that have occurred, provide RDFS+OWL descriptions and link to related resources. +LODE (Linking Open Descriptions of Events) is an ontology for publishing and interlinking structured event descriptions as Linked Data. It provides lightweight classes and properties for representing events, their time and place, and simple relationships to agents and sources. LODE is intentionally minimalistic to maximize interoperability and ease of adoption: it models events as occurrences with temporal extents and locations, and supports linking to richer event models when needed. Typical use cases include event directories, cultural heritage timelines, news event annotation, and discovery services that aggregate event records from multiple data providers. LODE emphasizes stable URIs and practical tools for populating event descriptions, enabling the creation of a searchable event directory of historical and contemporary events. + +**Example usage**: describe a public lecture as an lode:Event with a start/end time, a dcterms:spatial property linking to a Place URI, and a dc:source pointing to a news article; link the event to authority URIs for the speaker. The ontology's simplicity makes it a useful pivot for integrating event data across heterogeneous datasets. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/finance/goodrelations.rst b/docs/source/benchmarking/finance/goodrelations.rst index 34e8cc48..699ce0c3 100644 --- a/docs/source/benchmarking/finance/goodrelations.rst +++ b/docs/source/benchmarking/finance/goodrelations.rst @@ -25,7 +25,9 @@ Good Relations Language Reference (GoodRelations) ======================================================================================================== -GoodRelations is a standardized vocabulary (also known as "schema", "data dictionary", or "ontology") for product, price, store, and company data that can (1) be embedded into existing static and dynamic Web pages and that (2) can be processed by other computers. This increases the visibility of your products and services in the latest generation of search engines, recommender systems, and other novel applications. +GoodRelations is a widely-used ontology for describing products, services, offers, and commercial entities on the Web. It provides a rich vocabulary for modeling commercial information such as Products, Offers, Stores, Sellers, Payment and Delivery options, Price specifications, and Availability. GoodRelations emphasizes machine-actionable, fine-grained descriptions that support e-commerce discovery, comparison shopping, and automated processing by search engines, marketplaces, and recommendation systems. Key characteristics include a modular class structure (distinguishing Products/Services from Offers and Sellers), detailed modeling of price specifications (including currency, unit price, and price components), temporal validity of offers, and explicit representation of delivery and payment methods. The ontology is designed for interoperability: it can be embedded in HTML pages (microdata/RDFa/JSON-LD), linked with vocabularies like schema.org and FOAF, and exported in RDF/OWL for semantic-web use. GoodRelations supports provenance and business metadata, enabling trust and auditing use cases in marketplaces. Typical applications include SEO and product rich snippets, integration of catalog data across vendors, automated price aggregation, and semantic matching in recommender systems. + +**Example usage**: describe a product offering as an gr:Offering that links to a gr:Product (with identifiers and brand), includes a gr:UnitPriceSpecification (with priceCurrency, price, and validFrom/validThrough), and connects to a gr:BusinessEntity representing the seller with contact details and opening hours. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/food_and_beverage/wine.rst b/docs/source/benchmarking/food_and_beverage/wine.rst index 59458105..b89d141b 100644 --- a/docs/source/benchmarking/food_and_beverage/wine.rst +++ b/docs/source/benchmarking/food_and_beverage/wine.rst @@ -25,7 +25,7 @@ Wine Ontology (Wine) ======================================================================================================== -A project to define an RDF style ontology for wines and the wine-industry +The Wine Ontology is a comprehensive RDF-based vocabulary for describing wines, wine-making processes, vineyards, and the wine industry. It provides a detailed classification system for different types of wines (red, white, rosé, sparkling), wine regions, grape varieties, producers, and tasting characteristics. The ontology captures properties such as alcohol content, acidity, vintage year, and flavor profiles, enabling precise semantic representation of wine products and attributes. It integrates with related ontologies for representing food, geography, and commercial information, supporting applications in e-commerce, wine recommendation systems, and food science research. Example: A specific wine instance can be linked to its grape variety (Pinot Noir), wine region (Burgundy), producer, vintage year, and taste descriptors through well-defined ontology properties. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/general_knowledge/cco.rst b/docs/source/benchmarking/general_knowledge/cco.rst index 6e7d257c..537166ba 100644 --- a/docs/source/benchmarking/general_knowledge/cco.rst +++ b/docs/source/benchmarking/general_knowledge/cco.rst @@ -25,7 +25,10 @@ Common Core Ontologies (CCO) ======================================================================================================== -The Common Core Ontologies (CCO) is a widely-used suite of eleven ontologies that consist of logically well-defined generic terms and relations among them reflecting entities across all domains of interest. +The Common Core Ontologies (CCO) is a comprehensive suite of eleven interconnected ontologies providing logically well-defined generic terms and relations applicable across all domains of interest. CCO is built on formal semantic principles, ensuring that its concepts are unambiguous, semantically consistent, and applicable to diverse knowledge representation tasks. The ontology covers foundational concepts including objects, events, qualities, locations, and abstract entities, with explicit definitions of relationships between them. CCO is designed for maximum reusability across domain ontologies, enabling developers to extend CCO terms for specialized applications while maintaining semantic interoperability. The ontologies are documented with formal definitions, examples, and competency questions supporting both human understanding and computational reasoning. CCO has been adopted in enterprise information systems, knowledge graph construction, and semantic data integration projects requiring rigorous ontological foundations. + +**Example Usage**: +Represent a business domain ontology by extending CCO's generic Object and Event concepts to define company-specific entities (employees, contracts, transactions) and their relationships, ensuring compatibility with other systems using CCO foundations. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/general_knowledge/dbpedia.rst b/docs/source/benchmarking/general_knowledge/dbpedia.rst index 862d7a8e..939a94e4 100644 --- a/docs/source/benchmarking/general_knowledge/dbpedia.rst +++ b/docs/source/benchmarking/general_knowledge/dbpedia.rst @@ -25,7 +25,10 @@ DBpedia Ontology (DBpedia) ======================================================================================================== -The DBpedia ontology is generated from the manually created specifications in the DBpedia Mappings Wiki. Each release of this ontology corresponds to a new release of the DBpedia dataset, which contains instance data extracted from various language versions of Wikipedia. The DBpedia ontology has evolved into a crowd-sourced effort, resulting in a shallow cross-domain ontology. +The DBpedia ontology is generated from manually curated specifications in the DBpedia Mappings Wiki, providing a structured semantic model extracted from Wikipedia's rich content across multiple language editions. Each DBpedia release corresponds to a new Wikipedia data extraction, resulting in continuously evolving ontology versions that reflect growing knowledge representation in Wikipedia. The DBpedia ontology has become a shallow but comprehensive cross-domain ontology through crowd-sourced development involving thousands of contributors worldwide. It covers diverse knowledge domains including people, organizations, places, creative works, scientific concepts, and many others with relationships between them. DBpedia serves as a critical bridge between Wikipedia's unstructured information and the semantic web, enabling knowledge graph applications and linked data integration. The ontology is widely used in knowledge graph construction, information retrieval, entity linking, and semantic data integration projects leveraging Wikipedia's comprehensive and multilingual knowledge base. + +**Example Usage**: +Query DBpedia to find relationships between entities (e.g., all people born in Berlin, all films directed by a specific director, companies in a particular industry) by using ontology classes (Person, Film, Company) and properties to enable advanced knowledge discovery and data analytics. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/general_knowledge/dublincore.rst b/docs/source/benchmarking/general_knowledge/dublincore.rst index 6e346f3a..ce58940b 100644 --- a/docs/source/benchmarking/general_knowledge/dublincore.rst +++ b/docs/source/benchmarking/general_knowledge/dublincore.rst @@ -25,7 +25,10 @@ Dublin Core Vocabulary (DublinCore) ======================================================================================================== -The Dublin Core Schema is a small set of vocabulary terms that can be used to describe several kinds of resources. Dublin Core Metadata may be used for multiple purposes, from simple resource description, to combining metadata vocabularies of different metadata standards, to providing interoperability for metadata vocabularies in the Linked Data cloud and Semantic Web implementations. +The Dublin Core Schema is a small yet powerful vocabulary providing essential metadata elements for describing resources across diverse domains. Dublin Core Metadata can be used for multiple purposes including simple resource description, cross-standard metadata interoperability, and Linked Data cloud integration. It comprises fifteen core metadata elements (title, creator, subject, description, publisher, contributor, date, type, format, identifier, source, language, relation, coverage, rights) that are universally applicable across resource types. Dublin Core supports both simple and qualified metadata representation, enabling both basic and complex semantic annotation requirements. The vocabulary is language-independent and has become the de facto standard for resource description in digital libraries, institutional repositories, and data catalogs worldwide. Dublin Core facilitates semantic interoperability across heterogeneous information systems and enables automated resource discovery and management. + +**Example Usage**: +Annotate a research dataset or publication with Dublin Core terms including title, creator (author), date, subject (keywords), description, format (data type), identifier (DOI/URL), and rights (license) to enable standardized discovery and citation across digital repositories. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/general_knowledge/edam.rst b/docs/source/benchmarking/general_knowledge/edam.rst index 36fa2a55..44e9838f 100644 --- a/docs/source/benchmarking/general_knowledge/edam.rst +++ b/docs/source/benchmarking/general_knowledge/edam.rst @@ -25,7 +25,9 @@ The ontology of data analysis and management (EDAM) ======================================================================================================== -EDAM is a domain ontology of data analysis and data management in bio- and other sciences, and science-based applications. It comprises concepts related to analysis, modelling, optimisation, and data life cycle. Targetting usability by diverse users, the structure of EDAM is relatively simple, divided into 4 main sections: Topic, Operation, Data (incl. Identifier), and Format. +EDAM is a domain ontology that formalizes concepts, operations, and data types used in computational data analysis and data management across biological sciences and related domains. It provides structured vocabulary for describing bioinformatics analysis workflows, computational operations (alignment, clustering, prediction), data types, data formats, and the relationships between analysis steps. EDAM comprises four main sections: Topic (research domains and concepts), Operation (analysis/processing operations), Data (data types and identifiers), and Format (computational data formats and standards). The ontology is designed for usability by diverse stakeholders including bioinformaticians, tool developers, and researchers, with a relatively simple hierarchical structure to facilitate adoption. EDAM supports standardization of bioinformatics tool descriptions, workflow definitions, and dataset annotations, enabling automated tool discovery and workflow composition. + +**Example Usage**: Annotate a bioinformatics tool or service with EDAM terms for Input data (e.g., EDAM:data_0006 for sequence alignment), Operation (e.g., EDAM:operation_0496 for pairwise sequence alignment), Output format (e.g., EDAM:format_1929 for FASTA), and research Topic (e.g., EDAM:topic_0199 for sequence analysis). Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/general_knowledge/gist.rst b/docs/source/benchmarking/general_knowledge/gist.rst index 5318bff9..d3c40c1d 100644 --- a/docs/source/benchmarking/general_knowledge/gist.rst +++ b/docs/source/benchmarking/general_knowledge/gist.rst @@ -25,7 +25,10 @@ GIST Upper Ontology (GIST) ======================================================================================================== -Gist is Semantic Arts' minimalist upper ontology for the enterprise. It is designed to have the maximum coverage of typical business ontology concepts with the fewest number of primitives and the least amount of ambiguity. +GIST is Semantic Arts' minimalist upper ontology designed specifically for enterprise information systems, providing maximum coverage of typical business concepts with minimal primitives and minimal ambiguity. It emphasizes practical expressiveness and semantic clarity, avoiding unnecessary complexity while maintaining rigorous logical foundations. GIST covers essential business entities including agents (people, organizations), objects, events, measurements, and abstract concepts, with clearly defined relationships between them. The ontology is deliberately lightweight to facilitate adoption and integration into existing enterprise systems while providing sufficient semantic richness for sophisticated business logic representation. GIST supports both simple and complex semantic queries, reasoning, and knowledge graph construction for enterprise data integration and business intelligence applications. The ontology has been widely adopted in financial services, healthcare, and government sectors requiring reliable semantic foundations for data governance. + +**Example Usage**: +Design a healthcare enterprise ontology by extending GIST's Agent (to represent physicians, patients), Event (to represent treatments, procedures), and Object (to represent medications, medical devices) concepts to build a comprehensive healthcare knowledge graph for clinical decision support. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/general_knowledge/iao.rst b/docs/source/benchmarking/general_knowledge/iao.rst index c839c8f2..9cf08968 100644 --- a/docs/source/benchmarking/general_knowledge/iao.rst +++ b/docs/source/benchmarking/general_knowledge/iao.rst @@ -25,7 +25,9 @@ Information Artifact Ontology (IAO) ======================================================================================================== -The Information Artifact Ontology (IAO) is an ontology of information entities, originally driven by work by the OBI digital entity and realizable information entity branch. +The Information Artifact Ontology (IAO) is a comprehensive ontology for formal representation of information entities, information artifacts, and abstract information objects. It provides structured definitions of concepts such as documents, data items, information content, and the relationships between information artifacts and their physical realizations. IAO distinguishes between abstract information objects (the content) and their concrete realizations (documents, files, databases), enabling precise semantic representation of information resources. The ontology captures properties of information artifacts including authorship, creation date, version history, and relationships to the entities they describe or represent. IAO is widely used in biomedical informatics, scientific data management, and linked data applications for annotating information resources with formal semantic types. + +**Example Usage**: Annotate a scientific publication with IAO terms such as "document" for the artifact type, "author" relationships, and "has content" linking to abstract information objects representing the scientific claims and data presented. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/general_knowledge/prov.rst b/docs/source/benchmarking/general_knowledge/prov.rst index 355539c0..850d7452 100644 --- a/docs/source/benchmarking/general_knowledge/prov.rst +++ b/docs/source/benchmarking/general_knowledge/prov.rst @@ -25,7 +25,10 @@ PROV Ontology (PROV-O) ======================================================================================================== -The PROV Ontology (PROV-O) expresses the PROV Data Model [PROV-DM] using the OWL2 Web Ontology Language (OWL2) [OWL2-OVERVIEW]. It provides a set of classes, properties, and restrictions that can be used to represent and interchange provenance information generated in different systems and under different contexts. It can also be specialized to create new classes and properties to model provenance information for different applications and domains. The PROV Document Overview describes the overall state of PROV, and should be read before other PROV documents. +The PROV Ontology (PROV-O) expresses the PROV Data Model using the OWL2 Web Ontology Language, providing a set of classes, properties, and restrictions for representing and interchanging provenance information. It captures the complete lifecycle of entities, activities, and agents involved in generating data across different systems and contexts. PROV-O enables formal representation of who created what, when they created it, and under what circumstances, supporting accountability and reproducibility in data-intensive research. The ontology is designed to be generic enough for diverse applications while allowing specialization for specific domains through extension mechanisms. PROV-O facilitates automated provenance tracking and reasoning, enabling systems to verify data quality, authenticity, and compliance with policies. The ontology is widely used in scientific workflows, data management systems, and enterprise information governance. + +**Example Usage**: +Annotate a dataset's provenance with PROV-O terms to document that entity X was generated by activity Y using agent Z as input, with timestamps and qualifications about how the transformation occurred, enabling complete traceability and reproducibility of data. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/general_knowledge/ro.rst b/docs/source/benchmarking/general_knowledge/ro.rst index 2396291e..c83b3136 100644 --- a/docs/source/benchmarking/general_knowledge/ro.rst +++ b/docs/source/benchmarking/general_knowledge/ro.rst @@ -25,7 +25,10 @@ Relation Ontology (RO) ======================================================================================================== -The Relations Ontology (RO) is a collection of OWL relations (ObjectProperties) intended for use across a wide variety of biological ontologies. +The Relations Ontology (RO) is a comprehensive collection of formally defined OWL object properties designed for standardized representation of relationships across diverse biological ontologies. It provides a curated set of relations (part-of, derives-from, has-participant, immediately-precedes, etc.) with precise logical definitions ensuring semantic consistency across different biological knowledge domains. RO is built on foundational principles of formal ontology, ensuring that relations are unambiguous, logically sound, and applicable across multiple biological contexts. The ontology serves as the standard relation vocabulary for Open Biomedical Ontologies (OBO), enabling interoperable knowledge representation in genomics, proteomics, anatomy, and other life sciences. RO enables automated reasoning about biological entities and their complex relationships, supporting knowledge integration and discovery in biomedical research. The ontology is maintained collaboratively and has become the de facto standard for biological relationship representation in the semantic web community. + +**Example Usage**: +Define relationships in a biological ontology such as "protein A is part of complex B", "gene A has function X", or "disease A derives from mutation in gene B" using RO relations to enable automated reasoning about biological systems. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/general_knowledge/schemaorg.rst b/docs/source/benchmarking/general_knowledge/schemaorg.rst index 54a67fe4..dfd99447 100644 --- a/docs/source/benchmarking/general_knowledge/schemaorg.rst +++ b/docs/source/benchmarking/general_knowledge/schemaorg.rst @@ -25,7 +25,10 @@ Schema.org Ontology (SchemaOrg) ======================================================================================================== -Schema.org is a collaborative, community activity with a mission to create, maintain, and promote schemas for structured data on the Internet, on web pages, in email messages, and beyond. +Schema.org is a collaborative, community-driven initiative providing standardized schemas and ontologies for structured data markup across the entire web. It creates and maintains comprehensive vocabularies that enable webmasters, content creators, and developers to annotate web pages, email messages, and other digital resources with machine-readable semantic information. Schema.org covers diverse domains including products, organizations, events, publications, creative works, health and medical information, and many others. The schemas are designed for broad applicability across different industries while remaining flexible enough for domain-specific extensions. Schema.org markup is widely recognized and processed by major search engines, social media platforms, and aggregation services, directly impacting content discovery and presentation. The ontology facilitates knowledge graph construction, enhanced search results (rich snippets), and improved interoperability across web applications and services. + +**Example Usage**: +Annotate a restaurant website with Schema.org markup including Organization (restaurant details), LocalBusiness (address, phone, hours), and AggregateRating (customer reviews) to enable search engines to display rich snippets with ratings, location, and hours directly in search results. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/general_knowledge/umbel.rst b/docs/source/benchmarking/general_knowledge/umbel.rst index f8a8b90b..b1106ca0 100644 --- a/docs/source/benchmarking/general_knowledge/umbel.rst +++ b/docs/source/benchmarking/general_knowledge/umbel.rst @@ -25,7 +25,9 @@ Upper Mapping and Binding Exchange Layer Vocabulary (UMBEL) ======================================================================================================== -UMBEL is the Upper Mapping and Binding Exchange Layer, designed to help content interoperate on the Web. UMBEL provides two valuable functions: First, it is a broad, general reference structure of 34,000 concepts, which provides a scaffolding to link and interoperate other datasets and domain vocabularies. Second, it is a base vocabulary for the construction of other concept-based domain ontologies, also designed for interoperation. +UMBEL (Upper Mapping and Binding Exchange Layer) is a comprehensive reference ontology and interoperability framework designed to facilitate semantic integration and data exchange across diverse domain vocabularies and datasets on the Web. It provides a broad, general-purpose reference structure of approximately 34,000 concepts organized hierarchically, serving as a semantic scaffolding to link, align, and interoperate disparate datasets, domain ontologies, and knowledge bases. UMBEL acts as a bridge layer enabling concept mappings between specialized domain vocabularies, allowing data from different sources to be semantically related and integrated. The vocabulary is designed as a base framework for constructing concept-based domain ontologies that are explicitly designed for semantic interoperability, reducing fragmentation in knowledge representation. UMBEL supports linked data applications by providing standardized concept definitions and mappings that enable automated reasoning and knowledge discovery across heterogeneous information sources. + +**Example Usage**: Link concepts from domain-specific ontologies (e.g., medical terms from medical ontologies, product types from e-commerce vocabularies) to corresponding UMBEL concepts to enable cross-domain data integration and semantic reasoning. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/geography/geo.rst b/docs/source/benchmarking/geography/geo.rst index fd52b2f1..48be560a 100644 --- a/docs/source/benchmarking/geography/geo.rst +++ b/docs/source/benchmarking/geography/geo.rst @@ -25,7 +25,9 @@ Geographical Entities Ontology (GEO) ======================================================================================================== -Geographical Entities Ontology (GEO) is an inventory of geopolitical entities (such as sovereign states and their administrative subdivisions) as well as various geographical regions (including but not limited to the specific ones over which the governments have jurisdiction) +The Geographical Entities Ontology (GEO) provides a comprehensive inventory and formal representation of geopolitical and geographical entities, including sovereign states, administrative subdivisions, and various geographical regions. GEO distinguishes between political entities (countries, provinces, cities) and natural geographical features (mountains, rivers, seas), enabling precise semantic representation of territorial and regional concepts. The ontology uses hierarchical relationships to model administrative subdivisions and political jurisdictions at multiple levels (national, state, regional, local), supporting complex governance structures. GEO facilitates location-aware data annotation, enabling applications in geopolitical analysis, administrative reporting, and location-based services to unambiguously identify geographic and political entities. The ontology is designed for integration with other geographic and spatial ontologies, supporting linked data applications in geography, governance, and international relations. + +**Example Usage**: Annotate a political news article with GEO terms such as "France" (sovereign state) linked to its subdivisions "Île-de-France" and "Paris" (administrative entities) to enable geographic and political context discovery. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/geography/geonames.rst b/docs/source/benchmarking/geography/geonames.rst index c5de759a..e05085fa 100644 --- a/docs/source/benchmarking/geography/geonames.rst +++ b/docs/source/benchmarking/geography/geonames.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ GeoNames Ontology (GeoNames) ======================================================================================================== -The Geonames ontologies provides elements of description for geographical features, in particular those defined in the geonames.org database. +The GeoNames Ontology provides a structured vocabulary and semantic framework for describing geographical features, places, and administrative regions, particularly those defined in the geonames.org database. It enables the representation of place names, geographic coordinates, feature types (such as cities, rivers, mountains), and hierarchical relationships between locations (e.g., country, state, city). The ontology supports multilingual place names and alternative spellings, facilitating global interoperability and data integration. GeoNames is widely used in geographic information systems (GIS), linked data applications, and knowledge graphs to enable geospatial search, mapping, and data enrichment. By providing standardized identifiers and relationships, GeoNames enhances the discoverability and linking of geographic data across datasets and platforms. The ontology is maintained collaboratively and is continuously updated to reflect changes in geographic information and administrative boundaries. + +**Example Usage**: +Annotate a dataset of cultural heritage sites with GeoNames terms to specify their geographic coordinates, administrative regions, and feature types, enabling geospatial search and integration with mapping services. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/geography/gts.rst b/docs/source/benchmarking/geography/gts.rst index fff58794..f8e8b113 100644 --- a/docs/source/benchmarking/geography/gts.rst +++ b/docs/source/benchmarking/geography/gts.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Geologic Timescale model (GTS) ======================================================================================================== -This is an RDF/OWL representation of the GeoSciML Geologic Timescale model, which has been adapted from the model described in Cox, S.J.D, & Richard, S.M. (2005) A formal model for the geologic timescale and GSSP, compatible with geospatial information transfer standards, Geosphere, Geological Society of America. +The Geologic Timescale (GTS) is an RDF/OWL ontology representation of the standard geologic timescale model, adapted from the GeoSciML framework and compatible with geospatial information transfer standards. It provides a formal semantic model for representing geological time periods, epochs, eons, and their boundaries based on the Global Stratotype Section and Point (GSSP) framework established by the International Commission on Stratigraphy. GTS enables precise temporal annotation of geological data, enabling scientists to associate geological observations, samples, and events with specific time periods in Earth's history. The ontology supports hierarchical relationships between time divisions, enabling both broad geological age classification and detailed temporal analysis. GTS facilitates integration of paleontological, stratigraphic, and geological survey data across diverse research institutions and databases. + +**Example Usage**: +Annotate a rock sample or fossil record with GTS terms such as "Cretaceous" (era), "Campanian" (stage), or specific GSSP boundary ages to enable temporal querying and stratigraphic correlation. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/geography/juso.rst b/docs/source/benchmarking/geography/juso.rst index 568be738..3304169b 100644 --- a/docs/source/benchmarking/geography/juso.rst +++ b/docs/source/benchmarking/geography/juso.rst @@ -25,7 +25,9 @@ Juso Ontology (Juso) ======================================================================================================== -Juso Ontology is a Web vocabulary for describing geographical addresses and features. +The Juso Ontology is a comprehensive Web vocabulary for describing and classifying geographical addresses, locations, and geographical features with machine-readable semantic annotations. It provides a structured framework for representing address components including postal codes, street names, building numbers, and administrative hierarchies, enabling standardized address representation across diverse geographic regions. Juso supports multiple address formats and conventions, accommodating international addressing systems and local geographic naming practices. The ontology facilitates geocoding applications, location-based services, and geographic data integration by providing unambiguous semantic definitions of address components and spatial relationships. Juso integrates with broader geographic ontologies (GEO, GeoNames) to link address information with geographic entities and spatial contexts. + +**Example Usage**: Represent a complete address as a Juso address instance with properties for street name, building number, postal code, city, and country, enabling automated address validation and geographic lookup services. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/industry/auto.rst b/docs/source/benchmarking/industry/auto.rst index 66b4b358..06917522 100644 --- a/docs/source/benchmarking/industry/auto.rst +++ b/docs/source/benchmarking/industry/auto.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Automotive Ontology (AUTO) ======================================================================================================== -The AUTOMOTIVE ONTOLOGY (AUTO) defines the shared conceptual structures in the automotive industry. It is an OWL ontology. It is built upon the auto schema.org extension created by the W3C Automotive Ontology Community Group. AUTO's development process follows the best practices established by the EDMC FIBO Community. +The Automotive Ontology (AUTO) is a comprehensive OWL ontology that defines shared conceptual structures and relationships in the automotive industry. It provides a standardized vocabulary for describing vehicles, components, systems, manufacturing processes, and regulatory requirements. AUTO is built upon the auto schema.org extension created by the W3C Automotive Ontology Community Group and follows best practices established by the EDM Council's Financial Industry Business Ontology (FIBO) Community. The ontology supports interoperability between automotive data sources, enabling integration of information from manufacturers, suppliers, service providers, and regulatory bodies. AUTO facilitates advanced applications such as digital twins, predictive maintenance, supply chain optimization, and regulatory compliance. By providing a common semantic framework, AUTO enables automated reasoning, data analytics, and knowledge sharing across the automotive ecosystem. + +**Example Usage**: +Annotate a vehicle information system with AUTO terms to describe vehicle models, engine types, safety features, and maintenance schedules, enabling integration with manufacturer databases and regulatory reporting systems. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/industry/dbo.rst b/docs/source/benchmarking/industry/dbo.rst index c06c3389..3055cb7c 100644 --- a/docs/source/benchmarking/industry/dbo.rst +++ b/docs/source/benchmarking/industry/dbo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Digital Buildings Ontology (DBO) ======================================================================================================== -The Digital Buildings ontology (DBO) is used by Google to represent structured information about buildings and building-installed equipment. +The Digital Buildings Ontology (DBO) is a structured vocabulary developed by Google for representing information about buildings and building-installed equipment. DBO provides a semantic model for describing building assets, their locations, types, operational states, and relationships, supporting digital twins and smart building applications. The ontology enables integration of data from building management systems, IoT devices, and facility management platforms, facilitating automated monitoring, control, and analytics. DBO is designed to be extensible and interoperable, allowing organizations to adapt the ontology to their specific building types and operational requirements. By providing standardized terms and relationships, DBO supports data-driven decision-making, energy optimization, and predictive maintenance in digital buildings. The ontology is open source and maintained by a community of contributors, ensuring ongoing development and alignment with industry needs. + +**Example Usage**: +Annotate a smart building system with DBO terms to describe HVAC equipment, lighting systems, sensors, and their spatial locations, enabling automated control and integration with building management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/industry/doap.rst b/docs/source/benchmarking/industry/doap.rst index 3b4e2097..f7eee1a5 100644 --- a/docs/source/benchmarking/industry/doap.rst +++ b/docs/source/benchmarking/industry/doap.rst @@ -25,7 +25,9 @@ The Description of a Project vocabulary (DOAP) ======================================================================================================== -The Description of a Project vocabulary (DOAP), described using W3C RDF Schema and the Web Ontology Language to describe software projects, and in particular open source projects. +The Description of a Project (DOAP) vocabulary is an RDF/OWL-based ontology for machine-readable description of software projects, particularly open source initiatives. It models core project entities such as Project, Person, Revision, Repository, and License, capturing essential metadata about software development and distribution. DOAP enables representation of project attributes including name, description, homepage, version control systems (Git, SVN), issue tracking systems, programming languages, release history, and developer/maintainer information. The vocabulary facilitates integration of project data across diverse repositories, forges, and development platforms, supporting automated project discovery, dependency analysis, and ecosystem mapping. DOAP enables research on software development practices, project evolution, and open source community dynamics through structured, interoperable metadata. + +**Example Usage**: Describe an open source project with DOAP properties like foaf:name (project name), doap:repository (code repository URL), doap:programming-language (Python), doap:maintainer (developer agent), and doap:license (CC0 or Apache 2.0). Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/industry/iof.rst b/docs/source/benchmarking/industry/iof.rst index 1a4378b8..d39b526c 100644 --- a/docs/source/benchmarking/industry/iof.rst +++ b/docs/source/benchmarking/industry/iof.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Industrial Ontology Foundry (IOF) ======================================================================================================== -The IOF Core Ontology contains notions found to be common across multiple manufacturing domains. This file is an RDF implementation of these notions. The ontology utilizes the Basic Formal Ontology or BFO as a top-level ontology but also borrows terms from various domain-independent or mid-level ontologies. The purpose of the ontology is to serve as a foundation for ensuring consistency and interoperability across various domain-specific reference ontologies the IOF publishes. +The Industrial Ontology Foundry (IOF) Core Ontology is a foundational ontology for the manufacturing industry, capturing concepts and relationships common across multiple manufacturing domains. It is implemented in RDF and leverages the Basic Formal Ontology (BFO) as its upper-level framework, while also incorporating terms from other domain-independent and mid-level ontologies. IOF provides a standardized vocabulary for describing manufacturing processes, equipment, materials, products, and organizational structures. The ontology is designed to ensure consistency and interoperability across various domain-specific reference ontologies published by the IOF, supporting integration of manufacturing data from diverse sources. IOF enables advanced applications such as digital twins, smart manufacturing, supply chain optimization, and industrial automation. By providing a common semantic foundation, IOF facilitates data sharing, analytics, and knowledge management in the manufacturing sector. + +**Example Usage**: +Annotate a smart factory system with IOF terms to describe production lines, machines, materials, and process steps, enabling integration with enterprise resource planning (ERP) and manufacturing execution systems (MES). Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/industry/pko.rst b/docs/source/benchmarking/industry/pko.rst index dc385305..53ed315d 100644 --- a/docs/source/benchmarking/industry/pko.rst +++ b/docs/source/benchmarking/industry/pko.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -27,6 +25,11 @@ Provenance Knowledge Ontology (PKO) Procedural Knowledge (PK) is knowing how to perform some tasks, as opposed to descriptive/declarative knowledge, which is knowing what in terms of facts and notions. In industry, PK refers in general to structured processes to be followed, and can be related to both production (e.g., procedure on the production line in a plant) and services (e.g., procedure for troubleshooting during customer support); to specific technical expertise (e.g., procedure to set up a specific machine) and general regulations and best practices (e.g., safety procedures, activities to minimise environmental impact). +The Provenance Knowledge Ontology (PKO) is a domain ontology for representing procedural knowledge and provenance in industrial contexts. PKO formalizes structured processes, procedures, and best practices relevant to both production and services, capturing technical expertise, regulatory compliance, and operational workflows. The ontology enables the modeling of step-by-step procedures, their dependencies, required resources, and associated outcomes, supporting traceability and knowledge transfer. PKO is designed to facilitate the integration of procedural knowledge with provenance data, enabling organizations to track the execution of processes, ensure quality, and support continuous improvement. By providing a standardized vocabulary, PKO enhances interoperability between industrial information systems and supports automation, training, and compliance auditing. The ontology is maintained by industry experts and is aligned with broader provenance and process ontologies for maximum compatibility. + +**Example Usage**: +Annotate a manufacturing process with PKO terms to describe each procedural step, required equipment, responsible personnel, and resulting products, enabling automated tracking, quality assurance, and regulatory compliance. + Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/industry/pto.rst b/docs/source/benchmarking/industry/pto.rst index eac211bf..1c0c7723 100644 --- a/docs/source/benchmarking/industry/pto.rst +++ b/docs/source/benchmarking/industry/pto.rst @@ -25,7 +25,9 @@ Product Types Ontology (PTO) ======================================================================================================== -The Product Types Ontology is designed to be used in combination with GoodRelations, a standard vocabulary for the commercial aspects of offers. +The Product Types Ontology (PTO) is a comprehensive vocabulary for standardized classification and semantic description of commercial products and services designed to complement GoodRelations e-commerce vocabulary. PTO provides a hierarchical taxonomy of product categories, types, and subtypes covering diverse industries and market segments including consumer goods, electronics, fashion, books, and services. It enables detailed product classification through a fine-grained type hierarchy, facilitating product discovery, comparison shopping, and automated product recommendation systems. PTO is designed for web markup integration using microdata, RDFa, and JSON-LD formats, enabling product information embedded in HTML to be processed by search engines and aggregation platforms. By combining PTO product types with GoodRelations commercial properties (price, availability, shipping), organizations can create rich, machine-readable product descriptions for e-commerce applications. + +**Example Usage**: Annotate a product listing with PTO terms such as "Electronics > Smartphones > Android Phones" linked to GoodRelations Offering instances with price and availability information for automated product discovery and price comparison. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/industry/tubes.rst b/docs/source/benchmarking/industry/tubes.rst index 4e29d628..d4682b30 100644 --- a/docs/source/benchmarking/industry/tubes.rst +++ b/docs/source/benchmarking/industry/tubes.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ TUBES System Ontology (TUBES) ======================================================================================================== -The scope of the TUBES System Ontology is to explicitly define interconnected building service system in the AECO industry, their hierarchical subdivisions, structural and functional aspects, and links to spatial entities. As such, TSO supports the effort to represent linkable information in a future semantic web of building data. It has a strong alignment to other ontologies within the W3C community. +The TUBES System Ontology (TSO) is a domain ontology for the Architecture, Engineering, Construction, and Operations (AECO) industry, explicitly defining interconnected building service systems, their hierarchical subdivisions, structural and functional aspects, and links to spatial entities. TSO supports the semantic representation of building services such as HVAC, plumbing, and electrical systems, enabling detailed modeling of their components, relationships, and operational characteristics. The ontology is designed to facilitate the integration of building information across design, construction, and facility management processes, supporting digital twins and smart building applications. TSO aligns with other W3C community ontologies to ensure interoperability and data exchange in the semantic web of building data. By providing a standardized vocabulary, TSO enables automated reasoning, compliance checking, and lifecycle management of building services. The ontology is actively developed and adopted by industry stakeholders for advanced building information modeling (BIM) and digital transformation initiatives. + +**Example Usage**: +Annotate a BIM model with TUBES terms to describe the HVAC system, its components (air handling units, ducts, sensors), their spatial locations, and operational relationships, enabling automated analysis and integration with facility management systems. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/law/copyrightonto.rst b/docs/source/benchmarking/law/copyrightonto.rst index 632c0e6f..31d8d3a4 100644 --- a/docs/source/benchmarking/law/copyrightonto.rst +++ b/docs/source/benchmarking/law/copyrightonto.rst @@ -25,7 +25,13 @@ Copyright Ontology (CopyrightOnto) ======================================================================================================== -The Copyright Ontology tries to formalise the copyright domain as a way to facilitate automated (or computer-supported) copyright management through the whole content value chain, as it is shaped by copyright law. Therefore, it does not focus just on the last step, end-users permissions to consume content, like many rights languages and ontologies do. +The Copyright Ontology formalizes the copyright domain to support automated and computer-assisted management of rights, permissions, and obligations across the entire content value chain rather than focusing solely on end-user permissions. It models core legal and business entities such as Works, Authors/Rightsholders, Rights and Permissions, Licenses, Contracts, Transactions, Agents, and Provenance. The ontology captures temporal and jurisdictional dimensions of rights (grant periods, territorial scopes), the lifecycle of rights transfers and assignments, and links between digital artefacts and their legal metadata. + +Key characteristics include an emphasis on provenance and traceability (who granted which right, when, and under what conditions), expressivity for different permission types (reproduction, distribution, modification), and support for linking to external vocabularies (e.g., dcterms for metadata, FOAF/ORCID for agents, and license URIs such as Creative Commons). Its structure is suitable for encoding both normative legal information and operational licensing metadata used in publishing platforms, rights registries, and DRM-aware systems. + +Typical applications are automated rights clearance, license management, tracking provenance of digital content, and enabling marketplaces and repositories to reason about reuse conditions. + +**Example Usage**: describe a digital artwork as a CopyrightOnto Work with associated Rights statements linking to a License URI, a Rightsholder Agent, and validity dates to support automated permission checks. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/library_and_cultural_heritage/gnd.rst b/docs/source/benchmarking/library_and_cultural_heritage/gnd.rst index 0c3d5f25..6186fbcf 100644 --- a/docs/source/benchmarking/library_and_cultural_heritage/gnd.rst +++ b/docs/source/benchmarking/library_and_cultural_heritage/gnd.rst @@ -1,5 +1,6 @@ + .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +26,9 @@ Gemeinsame Normdatei (GND) ======================================================================================================== -GND stands for Gemeinsame Normdatei (Integrated Authority File) and offers a broad range of elements to describe authorities. The GND originates from the German library community and aims to solve the name ambiguity problem in the library world. +The Gemeinsame Normdatei (GND, Integrated Authority File) is a comprehensive semantic vocabulary and linked data resource developed by the German library and information community for describing and disambiguating authority data. GND provides standardized, machine-readable descriptions of persons, organizations, geographic locations, corporate bodies, and other entities to solve name ambiguity problems in library catalogs and information systems. The ontology offers a rich set of properties and relationships for describing biographical data, organizational hierarchies, place names, and subject matter authorities. GND is widely used in German-speaking libraries, archives, and cultural heritage institutions for authority control and semantic data linking. The vocabulary integrates with broader linked data ecosystems and international authority systems (VIAF, LCSH) to enable cross-institutional data integration and discovery. + +**Example Usage**: Link a library catalog record to GND authority entries for the author (as person), publisher (as corporate body), and subject headings (as topic authorities) to enable semantic discovery and disambiguation across German and international library systems. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/amontology.rst b/docs/source/benchmarking/materials_science_and_engineering/amontology.rst index afe8f8b8..e3663f39 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/amontology.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/amontology.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Additive Manufacturing Ontology (AMOntology) ======================================================================================================== -The AM ontology has been developed following two major milestones. The ontology developed within the first milestone includes AMProcessOntology, ModelOntology and AMOntology files. AMProcessOntology contains the set of entities used to capture knowledge about additive manufacturing processes. ModelOntology contains the set of entities used to capture knowledge about modeling concepts that represent (possibly) multi-physics multi-scale processes. AMOntology uses AMProcessOntology and ModelOntology files to describe entities that capture knowledge about characteristics of computational models for AM processes. +The Additive Manufacturing Ontology (AMOntology) is a domain ontology developed to represent knowledge about additive manufacturing (AM) processes, computational models, and their characteristics. It is structured around two main components: AMProcessOntology, which captures entities and relationships relevant to AM processes, and ModelOntology, which describes modeling concepts for multi-physics, multi-scale simulations. AMOntology integrates these components to provide a comprehensive framework for describing process parameters, material properties, equipment, and computational models in AM. The ontology supports semantic annotation of digital manufacturing workflows, enabling data integration, process optimization, and knowledge sharing across research and industry. By providing a standardized vocabulary, AMOntology facilitates interoperability between digital manufacturing systems, simulation tools, and research databases. The ontology is actively maintained and extended to support new developments in additive manufacturing and computational modeling. + +**Example Usage**: +Annotate an additive manufacturing workflow with AMOntology terms to specify process parameters (e.g., laser power, layer thickness), computational model characteristics, and material properties, enabling semantic search and integration with digital manufacturing platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/asmo.rst b/docs/source/benchmarking/materials_science_and_engineering/asmo.rst index da23a35e..f3d7e90d 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/asmo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/asmo.rst @@ -25,7 +25,9 @@ Atomistic Simulation Methods Ontology (ASMO) ======================================================================================================== -ASMO is an ontology that aims to define the concepts needed to describe commonly used atomic scale simulation methods, i.e. density functional theory, molecular dynamics, Monte Carlo methods, etc. ASMO uses the Provenance Ontology (PROV-O) to describe the simulation process. +ASMO is a comprehensive ontology that formalizes concepts and relationships needed to describe and classify atomic-scale computational simulation methods used in materials science and computational chemistry. It provides structured vocabulary for representing commonly used simulation methodologies including density functional theory (DFT), molecular dynamics (MD), Monte Carlo (MC) methods, and other first-principles and statistical mechanics techniques. ASMO captures essential properties of simulation methods such as theoretical foundations, computational complexity, applicable material systems, and resulting properties that can be predicted. The ontology uses the Provenance Ontology (PROV-O) to formally describe simulation processes, enabling tracking of computational workflows, software tools, and input parameters used in materials modeling. ASMO facilitates reproducibility and interoperability in computational materials science by providing standardized semantic representations of simulation methods and their parameters. + +**Example Usage**: Annotate a materials database entry with ASMO terms for the simulation method (e.g., DFT with specific functional), system size (number of atoms), computational details (basis sets, k-point meshes), and resulting properties (band gap, elastic constants). Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/atomistic.rst b/docs/source/benchmarking/materials_science_and_engineering/atomistic.rst index 71a6d065..359abaa8 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/atomistic.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/atomistic.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Atomistic Ontology (Atomistic) ======================================================================================================== -An EMMO-based domain ontology for atomistic and electronic modelling. +The Atomistic Ontology is an EMMO-based domain ontology designed for atomistic and electronic modeling in materials science. It provides a structured vocabulary for representing atomic-scale structures, electronic properties, and simulation methods. The ontology supports semantic annotation of computational models, enabling interoperability and data integration across materials modeling platforms. Atomistic Ontology facilitates detailed description of atomic configurations, electronic states, and interactions, supporting advanced materials design and analysis. The ontology is actively maintained and extended to incorporate new modeling techniques and scientific findings. By providing a standardized framework, Atomistic Ontology enhances reproducibility, data sharing, and collaborative research in atomistic simulations. + +**Example Usage**: +Annotate a computational materials science dataset with Atomistic Ontology terms to specify atomic structures, electronic properties, and simulation parameters, enabling semantic search and integration with modeling tools. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/battinfo.rst b/docs/source/benchmarking/materials_science_and_engineering/battinfo.rst index d60a11ff..0f7864a7 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/battinfo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/battinfo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Battery Interface Ontology (BattINFO) ======================================================================================================== -BattINFO is a foundational resource for harmonizing battery knowledge representation and enhancing data interoperability. The primary objective is to provide the necessary tools to create FAIR (Findable, Accessible, Interoperable, Reusable) battery data that can be integrated into the Semantic Web. +The Battery Interface Ontology (BattINFO) is a domain ontology developed to standardize and harmonize the representation of battery-related knowledge, data, and interfaces. BattINFO provides a structured vocabulary for describing battery materials, components, interfaces, processes, and performance metrics, supporting the annotation and integration of experimental and computational battery data. The ontology is designed to enable FAIR battery data practices, facilitating data sharing, interoperability, and reuse across research, industry, and digital platforms. BattINFO supports semantic annotation of battery experiments, manufacturing workflows, and simulation results, enabling advanced analytics, lifecycle assessment, and knowledge discovery. The ontology is extensible and can be aligned with other materials science and energy ontologies for broader compatibility. BattINFO is actively maintained and extended to incorporate new battery technologies, standards, and research requirements. + +**Example Usage**: +Annotate a battery research dataset with BattINFO terms to specify electrode materials, electrolyte composition, interface properties, and performance metrics (e.g., capacity, cycle life), enabling semantic search and integration with battery databases and digital twins. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/bmo.rst b/docs/source/benchmarking/materials_science_and_engineering/bmo.rst index bd1bb66e..2fb21ed4 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/bmo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/bmo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Building Material Ontology (BMO) ======================================================================================================== -Building Material Ontology defines the main concepts of building material, types, layers, and properties. +The Building Material Ontology (BMO) is a domain ontology designed to represent the main concepts, types, layers, and properties of building materials used in construction and civil engineering. BMO provides a structured vocabulary for describing material composition, physical and chemical properties, functional layers, and relationships between materials in building assemblies. The ontology supports semantic annotation of building material data, enabling interoperability between construction databases, digital twins, and building information modeling (BIM) systems. BMO is designed for extensibility, allowing integration with other ontologies and standards for sustainability, performance, and regulatory compliance. By providing a standardized framework, BMO facilitates advanced search, material selection, lifecycle analysis, and knowledge sharing in the construction industry. The ontology is actively maintained and extended to incorporate new materials, technologies, and industry requirements. + +**Example Usage**: +Annotate a BIM model with BMO terms to specify the material composition of a wall assembly, including layers (e.g., insulation, concrete, plaster), material properties (e.g., thermal conductivity, fire resistance), and sustainability attributes, enabling semantic search and integration with construction databases. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/bvco.rst b/docs/source/benchmarking/materials_science_and_engineering/bvco.rst index 3a4b9083..e9e5ebf7 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/bvco.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/bvco.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,11 @@ Battery Value Chain Ontology (BVCO) ======================================================================================================== -Basically, Battery Value Chain Ontology (BVCO) aims to model processes along the Battery value chain. Processes are holistic perspective elements that transform inputs/educts (matter, energy, information) into output/products (matter, energy, information) with the help of tools (devices, algorithms). They can be decomposed into sub-processes and have predecessor and successor processes. +The Battery Value Chain Ontology (BVCO) is a domain ontology developed to model processes, entities, and relationships along the battery value chain, from raw material extraction to recycling and disposal. BVCO provides a structured vocabulary for describing holistic processes that transform inputs (matter, energy, information) into outputs (products, byproducts, waste) using tools such as devices and algorithms. The ontology supports decomposition of processes into sub-processes, capturing predecessor and successor relationships, and enabling detailed modeling of manufacturing, logistics, usage, and end-of-life stages. BVCO facilitates semantic annotation of battery value chain data, supporting interoperability, data integration, and advanced analytics across research, industry, and regulatory platforms. By providing a standardized framework, BVCO enables lifecycle assessment, supply chain optimization, and sustainability analysis in the battery industry. The ontology is actively maintained and extended to incorporate new battery technologies, process innovations, and regulatory requirements. + +**Example Usage**: +Annotate a battery manufacturing workflow with BVCO terms to specify raw material sourcing, cell assembly, quality control, logistics, and recycling processes, enabling semantic search and integration with supply chain management systems. + Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/cdco.rst b/docs/source/benchmarking/materials_science_and_engineering/cdco.rst index bfe28c15..112c62a1 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/cdco.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/cdco.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Crystallographic Defect Core Ontology (CDCO) ======================================================================================================== -CDCO defines the common terminology shared across all types of crystallographic defects, providing a unified framework for data integration in materials science. +The Crystallographic Defect Core Ontology (CDCO) is a domain ontology designed to provide a unified framework for representing and integrating data about crystallographic defects in materials science. CDCO defines common terminology for various types of defects, including vacancies, interstitials, dislocations, grain boundaries, and stacking faults, as well as their properties and relationships. The ontology supports semantic annotation of experimental and computational data, enabling interoperability, data integration, and advanced analysis across materials databases and research platforms. CDCO is designed for extensibility, allowing researchers to describe new defect types, characterization methods, and material systems. By providing a standardized vocabulary, CDCO facilitates cross-study comparison, defect modeling, and knowledge sharing in materials science. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community. + +**Example Usage**: +Annotate a materials database with CDCO terms to specify the types of crystallographic defects present in a sample, their properties (e.g., density, energy), and relationships to material processing conditions, enabling semantic search and integration with defect modeling tools. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/chameo.rst b/docs/source/benchmarking/materials_science_and_engineering/chameo.rst index 7b7efe99..792d2752 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/chameo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/chameo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Characterisation Methodology Domain Ontology (CHAMEO) ======================================================================================================== -An ontology for materials characterization which represents the evolution of the CHADA template in an ontological form, allowing to generate FAIR documentation of Characterisation Experiments and that has been used as a basis for the development of a number of technique-specific or application-specific ontologies in the materials characterisation domain. CHAMEO has been used as a foundation for the definition of the new CHADA template during the CWA. +The Characterisation Methodology Domain Ontology (CHAMEO) is a domain ontology for materials characterization, representing the evolution of the CHADA template in an ontological form. CHAMEO enables the generation of FAIR documentation for characterization experiments and serves as a foundation for the development of technique-specific and application-specific ontologies in the materials characterization domain. The ontology provides a structured vocabulary for describing experimental setups, measurement techniques, sample properties, data acquisition, and analysis workflows. CHAMEO supports semantic annotation of characterization data, facilitating interoperability, data integration, and advanced analytics across research projects and digital platforms. By providing a standardized framework, CHAMEO enhances reproducibility, knowledge sharing, and cross-laboratory comparison in materials science. The ontology is actively maintained and extended to incorporate new characterization methods, standards, and community requirements. + +**Example Usage**: +Annotate a materials characterization experiment with CHAMEO terms to specify the measurement technique (e.g., X-ray diffraction), sample preparation, instrument configuration, and data analysis workflow, enabling semantic search and integration with materials databases. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/cifcore.rst b/docs/source/benchmarking/materials_science_and_engineering/cifcore.rst index e7a2bc14..beb0c3fd 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/cifcore.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/cifcore.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Crystallographic Information Framework Core Dictionary (CIFCore) ======================================================================================================== -(1) to explain the historical development of CIF dictionaries to define in a machine-actionable manner the contents of data files covering various aspects of crystallography and related structural sciences; (2) to demonstrate some of the more complex types of information that can be handled with this approach. +The Crystallographic Information Framework Core Dictionary (CIFCore) is a domain ontology developed to provide a machine-actionable representation of data files covering various aspects of crystallography and related structural sciences. CIFCore explains the historical development of CIF dictionaries and demonstrates the handling of complex information types in crystallographic data. The ontology supports semantic annotation of crystallographic datasets, enabling interoperability, data integration, and advanced analysis in structural biology, materials science, and chemistry. CIFCore facilitates the standardized description of crystal structures, symmetry operations, atomic coordinates, and experimental conditions. By providing a comprehensive vocabulary, CIFCore supports data sharing, reproducibility, and computational modeling in crystallography research. The ontology is actively maintained and extended to incorporate new crystallographic concepts and data standards. + +**Example Usage**: +Annotate a crystallographic dataset with CIFCore terms to specify crystal lattice parameters, atomic positions, symmetry groups, and experimental conditions, enabling semantic search and integration with structural databases. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/cmso.rst b/docs/source/benchmarking/materials_science_and_engineering/cmso.rst index f6c5bcb0..0340eced 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/cmso.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/cmso.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Computational Material Sample Ontology (CMSO) ======================================================================================================== -CMSO is an ontology that aims to describe computational materials science samples (or structures), including crystalline defects. Initially focusing on the description at the atomic scale. +The Computational Material Sample Ontology (CMSO) is a domain ontology developed to describe computational materials science samples (or structures), with an initial focus on atomic-scale representations and crystalline defects. CMSO provides a structured vocabulary for representing atomic configurations, simulation cells, boundary conditions, and various types of defects such as vacancies, interstitials, and dislocations. The ontology supports semantic annotation of computational models, simulation workflows, and results, enabling interoperability and data integration across materials modeling platforms. CMSO is designed for extensibility, allowing researchers to describe new sample types, simulation methods, and material systems. By providing a standardized framework, CMSO facilitates cross-study comparison, advanced analytics, and knowledge sharing in computational materials science. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community. + +**Example Usage**: +Annotate a computational materials science dataset with CMSO terms to specify the atomic structure, simulation cell parameters, defect types, and boundary conditions, enabling semantic search and integration with materials modeling databases. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/diso.rst b/docs/source/benchmarking/materials_science_and_engineering/diso.rst index 19d686d0..e1b0324c 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/diso.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/diso.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Dislocation Ontology (DISO) ======================================================================================================== -DISO is an ontology that defines the linear defect, in particular dislocation concepts and relations between them in crystalline materials. +DISO is a specialized ontology that formalizes the conceptualization and semantic representation of linear defects in crystalline materials, with particular focus on dislocations and their complex relationships. It provides structured vocabulary for describing dislocation types (edge, screw, mixed), dislocation properties (Burgers vector, line direction), and dislocation interactions (annihilation, multiplication, cross-slip). The ontology captures the geometric and topological properties of dislocations essential for understanding plastic deformation, work hardening, and material strength in metals and alloys. DISO enables precise annotation of experimental observations and computational simulations of dislocations in crystalline microstructures, supporting materials science research and industrial applications. DISO facilitates knowledge integration in materials databases and computational materials science by providing standardized semantic representations of linear defects. + +**Example Usage**: +Annotate a transmission electron microscopy (TEM) image or molecular dynamics simulation showing dislocations with DISO terms describing dislocation type (edge or screw), Burgers vector, crystal system context, and interactions with other dislocations or grain boundaries. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/dsim.rst b/docs/source/benchmarking/materials_science_and_engineering/dsim.rst index e93550b8..c7820b7c 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/dsim.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/dsim.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Dislocation Simulation and Model Ontology (DSIM) ======================================================================================================== -Dislocation simulation and model ontology (DSIM) is an ontology developed to model various concepts and relationships in the discrete dislocation dynamics domain and microscopy techniques used in the dislocation domain. The various concepts are the numerical representation of dislocation applied in the dislocation dynamic simulation and the pictorial concept of pixel applied in representing dislocation in the experimental image, eg., TEM image, SEM image, and FIM image. +The Dislocation Simulation and Model Ontology (DSIM) is a domain ontology developed to model concepts and relationships in the field of discrete dislocation dynamics and microscopy techniques used in dislocation research. DSIM provides a structured vocabulary for representing numerical representations of dislocations in simulations, as well as pictorial concepts such as pixels in experimental images (e.g., TEM, SEM, FIM). The ontology enables semantic annotation of simulation workflows, experimental setups, and image analysis procedures, supporting data integration and reproducibility in materials science. DSIM is designed for extensibility, allowing researchers to describe new simulation methods, image processing techniques, and dislocation phenomena. By providing a standardized framework, DSIM facilitates cross-study comparison, advanced analytics, and knowledge sharing in dislocation research. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community. + +**Example Usage**: +Annotate a dislocation dynamics simulation with DSIM terms to specify the simulation method, dislocation types, image analysis workflow, and experimental conditions, enabling semantic search and integration with microscopy data. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/emmo.rst b/docs/source/benchmarking/materials_science_and_engineering/emmo.rst index bf82dfcf..4d0a5a1c 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/emmo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/emmo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ The Elementary Multiperspective Material Ontology (EMMO) ======================================================================================================== -The Elementary Multiperspective Material Ontology (EMMO) is the result of a multidisciplinary effort within the EMMC, aimed at the development of a standard representational ontology framework based on current materials modelling and characterization knowledge. Instead of starting from general upper level concepts, as done by other ontologies, the EMMO development started from the very bottom level, using the actual picture of the physical world coming from applied sciences, and in particular from physics and material sciences. +The Elementary Multiperspective Material Ontology (EMMO) is a foundational ontology developed by the European Materials Modelling Council (EMMC) to provide a standard representational framework for materials science and engineering. EMMO is unique in that it starts from the bottom level, using the physical world as described by applied sciences, especially physics and materials science, rather than from abstract upper-level concepts. The ontology covers a wide range of concepts including materials, processes, properties, structures, and measurement techniques, supporting the semantic integration of materials modelling, characterization, and experimental data. EMMO is modular and extensible, enabling domain-specific extensions for specialized applications in materials research, digital twins, and manufacturing. By providing a rigorous semantic foundation, EMMO facilitates data interoperability, knowledge sharing, and advanced analytics across the materials science community. The ontology is actively maintained and extended to incorporate new concepts and requirements from ongoing research and industrial projects. + +**Example Usage**: +Annotate a materials database with EMMO terms to describe the composition, structure, and properties of a material sample, the experimental techniques used for characterization, and the modelling workflows applied, enabling semantic search and data integration across research projects. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/emmocrystallography.rst b/docs/source/benchmarking/materials_science_and_engineering/emmocrystallography.rst index 4ea25302..353473d9 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/emmocrystallography.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/emmocrystallography.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Crystallography Ontology (EMMOCrystallography) ======================================================================================================== -A crystallography domain ontology based on EMMO and the CIF core dictionary. It is implemented as a formal language. +The Crystallography Ontology (EMMOCrystallography) is a domain ontology based on the Elementary Multiperspective Material Ontology (EMMO) and the Crystallographic Information Framework (CIF) core dictionary. It provides a formal language for representing crystallographic concepts, including crystal structures, symmetry operations, atomic positions, and experimental conditions. EMMOCrystallography supports semantic annotation of crystallographic datasets, enabling interoperability, data integration, and advanced analysis in materials science and structural biology. The ontology facilitates the standardized description of crystallographic experiments, data processing workflows, and structural models. By providing a rigorous semantic framework, EMMOCrystallography enhances reproducibility, data sharing, and computational modeling in crystallography research. The ontology is actively maintained and extended to incorporate new crystallographic concepts and community requirements. + +**Example Usage**: +Annotate a crystallographic dataset with EMMOCrystallography terms to specify crystal lattice parameters, symmetry groups, atomic coordinates, and experimental metadata, enabling semantic search and integration with structural databases. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/fso.rst b/docs/source/benchmarking/materials_science_and_engineering/fso.rst index 26486af3..64f30a4d 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/fso.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/fso.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Flow Systems Ontology (FSO) ======================================================================================================== -The Flow Systems Ontology (FSO) is an ontology for describing interconnected systems with material or energy flow connections, and their components. +The Flow Systems Ontology (FSO) is a domain ontology developed to describe interconnected systems with material or energy flow connections and their components. FSO provides a structured vocabulary for representing flow systems, subsystems, flow paths, nodes, and the properties of materials or energy being transferred. The ontology supports semantic annotation of process engineering data, enabling interoperability, data integration, and advanced analysis in chemical engineering, energy systems, and process industries. FSO is designed for extensibility, allowing researchers and engineers to describe new flow system types, process configurations, and measurement techniques. By providing a standardized framework, FSO facilitates process modeling, simulation, optimization, and knowledge sharing across engineering domains. The ontology is actively maintained and extended to incorporate new concepts and requirements from the process engineering community. + +**Example Usage**: +Annotate a chemical process simulation with FSO terms to specify the flow system structure, material streams, energy flows, and process units, enabling semantic search and integration with process engineering databases and simulation tools. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/gpo.rst b/docs/source/benchmarking/materials_science_and_engineering/gpo.rst index 60de512e..e04b33bf 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/gpo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/gpo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ General Process Ontology (GPO) ======================================================================================================== -Basically, this ontology aims to model processes. Processes are holistic perspective elements that transform inputs/educts (matter, energy, information) into output/products (matter, energy, information) with the help of tools (devices, algorithms). They can be decomposed into sub-processes and have predecessor and successor processes. +The General Process Ontology (GPO) is a domain ontology developed to model processes in materials science and engineering, as well as other scientific and industrial domains. GPO provides a structured vocabulary for representing holistic processes that transform inputs (matter, energy, information) into outputs (products, byproducts, waste) using tools such as devices and algorithms. The ontology supports decomposition of processes into sub-processes, capturing predecessor and successor relationships, and enabling detailed modeling of workflows, manufacturing, and experimental procedures. GPO facilitates semantic annotation of process data, supporting interoperability, data integration, and advanced analytics across research, industry, and regulatory platforms. By providing a standardized framework, GPO enables lifecycle assessment, process optimization, and knowledge sharing in multidisciplinary projects. The ontology is actively maintained and extended to incorporate new process types, technologies, and application domains. + +**Example Usage**: +Annotate a manufacturing workflow with GPO terms to specify process steps, input and output materials, tools used, and process dependencies, enabling semantic search and integration with process management systems. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/hpont.rst b/docs/source/benchmarking/materials_science_and_engineering/hpont.rst index 82c107bf..13dafedb 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/hpont.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/hpont.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ The Heat Pump Ontology (HPOnt) ======================================================================================================== -The Heat Pump Ontology (HPOnt) aims to formalize and represent all the relevant information of Heat Pumps. The HPOnt has been developed as part of the REACT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395. +The Heat Pump Ontology (HPOnt) is a domain ontology developed to formalize and represent all relevant information about heat pumps, including their components, operational parameters, performance metrics, and integration with building energy systems. HPOnt provides a structured vocabulary for describing heat pump types, working fluids, control strategies, installation contexts, and maintenance procedures. The ontology supports semantic annotation of heat pump data, enabling interoperability between smart building systems, energy management platforms, and research databases. HPOnt is designed for extensibility and can be adapted to represent new heat pump technologies, regulatory requirements, and sustainability metrics. By providing a standardized framework, HPOnt facilitates advanced analytics, predictive maintenance, and optimization of heat pump operation in residential, commercial, and industrial settings. The ontology is actively maintained and extended as part of the REACT project and is aligned with European Union research and innovation initiatives. + +**Example Usage**: +Annotate a smart building energy management system with HPOnt terms to specify the types of heat pumps installed, their operational parameters (e.g., COP, setpoints), maintenance schedules, and integration with renewable energy sources, enabling semantic search and optimization of building energy performance. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/ldo.rst b/docs/source/benchmarking/materials_science_and_engineering/ldo.rst index c2d7a1df..bc90e839 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/ldo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/ldo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Line Defect Ontology (LDO) ======================================================================================================== -LDO is an ontology designed to describe line defects in crystalline materials, such as dislocations and disclinations. +The Line Defect Ontology (LDO) is a domain ontology developed to provide a comprehensive and standardized vocabulary for describing line defects in crystalline materials, such as dislocations and disclinations. LDO enables the semantic annotation of experimental and computational data related to line defects, supporting interoperability and data integration across materials science databases and research platforms. The ontology covers key concepts including defect types, geometric and topological properties, formation mechanisms, and interactions with other defects or microstructural features. LDO is designed for extensibility, allowing researchers to describe new line defect types, characterization methods, and material systems as the field evolves. By providing a rigorous semantic framework, LDO facilitates advanced analytics, defect modeling, and knowledge sharing in materials science and engineering. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community. + +**Example Usage**: +Annotate a transmission electron microscopy (TEM) dataset with LDO terms to specify the types of line defects observed, their Burgers vectors, line directions, and interactions with grain boundaries, enabling semantic search and integration with defect modeling tools. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/lpbfo.rst b/docs/source/benchmarking/materials_science_and_engineering/lpbfo.rst index 817fd4d8..6d3eff75 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/lpbfo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/lpbfo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Laser Powder Bed Fusion Ontology (LPBFO) ======================================================================================================== -The LPBF Ontology can be used to describe the additive manufacturing of a component via Laser Powder Bed Fusion (LPBF) / Selective Laser Melting (SLM). The ontology builds on BFO2.0 and BWMD_mid and has been developed to be used in conjunction with the digital workflows provided by Fraunhofer IWM. If possible, the terminology within this ontology was used as provided by ISO/ASTM 52900:2015. Recently, classes relevant for Life Cycle Analysis (LCA) were added that enable sustainability assessment. +The Laser Powder Bed Fusion Ontology (LPBFO) is a domain ontology for describing additive manufacturing processes, specifically Laser Powder Bed Fusion (LPBF) and Selective Laser Melting (SLM). LPBFO provides a structured vocabulary for representing process parameters, materials, equipment, component geometries, and quality attributes relevant to LPBF manufacturing. The ontology builds on BFO2.0 and BWMD_mid, and incorporates terminology from ISO/ASTM 52900:2015 to ensure alignment with industry standards. LPBFO supports semantic annotation of digital manufacturing workflows, enabling data integration, process optimization, and sustainability assessment through Life Cycle Analysis (LCA) classes. By providing a standardized framework, LPBFO facilitates interoperability between digital manufacturing systems, quality management, and research databases. The ontology is actively maintained and extended to support new developments in additive manufacturing and sustainability assessment. + +**Example Usage**: +Annotate an LPBF manufacturing workflow with LPBFO terms to specify process parameters (e.g., laser power, scan speed), material types, component geometry, and LCA attributes, enabling semantic search and integration with digital manufacturing platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/mambo.rst b/docs/source/benchmarking/materials_science_and_engineering/mambo.rst index 3ac639c4..949a1a75 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mambo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mambo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Molecules And Materials Basic Ontology (MAMBO) ======================================================================================================== -MAMBO (Molecules And Materials Basic Ontology) is a domain ontology for molecular materials. Its main targets are: Allowing the retrieval of structured information regarding molecular materials and related applications (i.e. devices based on molecular materials) Supporting the development of new, complex workflows for modelling systems based on molecular materials (computational modelling and data-driven techniques) Integrating data generated via computational simulations and empirical experiments. +MAMBO (Molecules And Materials Basic Ontology) is a domain ontology for molecular materials, designed to facilitate the retrieval and integration of structured information regarding molecular materials and their applications. The ontology provides a comprehensive framework for representing molecular structures, properties, and interactions, supporting both computational modeling and empirical research. MAMBO encompasses key entities such as molecules, materials, devices, and processes, and models relationships between these entities to capture the complexity of molecular systems. The ontology employs a class-based modeling approach, defining classes for different types of molecules, materials, and devices, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MAMBO supports the integration of data from computational simulations and experimental studies, promoting interoperability and data-driven research in materials science. Typical applications of MAMBO include the development of new materials for electronic devices, the optimization of molecular structures for specific applications, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MAMBO enhances collaboration and innovation in the field of molecular materials. + +**Example Usage**: +Annotate a dataset of molecular materials with MAMBO terms to specify molecular structures, properties, and interactions, enabling semantic search and integration with computational modeling tools and experimental databases. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/mat.rst b/docs/source/benchmarking/materials_science_and_engineering/mat.rst index 48abf5e3..6dc3737c 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mat.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mat.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,11 @@ Material Properties Ontology (MAT) ======================================================================================================== -The Material Properties Ontology aims to provide the vocabulary to describe the building components, materials, and their corresponding properties, relevant within the construction industry. More specifically, the building elements and properties covered in this ontology support applications focused on the design of building renovation projects. +The Material Properties Ontology (MAT) is designed to provide a comprehensive vocabulary for describing building components, materials, and their properties within the construction industry. It focuses on supporting applications related to building renovation projects by offering a structured framework for representing material characteristics, performance metrics, and relationships between different building elements. MAT encompasses key entities such as materials, components, and properties, and models relationships to capture the complexity of construction materials. + +The ontology employs a class-based modeling approach, defining classes for various material types and properties, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MAT supports the integration of data from design, construction, and renovation projects, promoting interoperability and data-driven research in the construction industry. + +Typical applications of MAT include the design and optimization of building materials for energy efficiency, the assessment of material properties for renovation projects, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MAT enhances collaboration and innovation in the field of construction materials. Metrics & Statistics -------------------------- @@ -134,3 +136,6 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +**Example Usage**: +Annotate a building renovation project with MAT terms to specify material types, properties, and performance metrics, enabling semantic search and integration with construction informatics platforms. diff --git a/docs/source/benchmarking/materials_science_and_engineering/materialinformation.rst b/docs/source/benchmarking/materials_science_and_engineering/materialinformation.rst index 1ca0616d..96fb9e64 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/materialinformation.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/materialinformation.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Material Information Ontology (MaterialInformation) ======================================================================================================== -The Material Information ontology is divided into smaller ontologies (partitions). The partitions are Environment, Geometry, Material Information, Manufacturing Process, Property, Substance, Unit Dimension, Structure, Equation and Physical Constant. +The Material Information Ontology is a comprehensive framework designed to represent various aspects of materials science, including environment, geometry, material information, manufacturing processes, properties, substances, unit dimensions, structures, equations, and physical constants. This ontology is divided into smaller partitions, each focusing on a specific domain, to facilitate detailed modeling and integration of materials data. + +The ontology employs a modular approach, defining classes and properties for each partition to capture the complexity of materials science. It supports semantic annotation of materials data, enabling interoperability, data integration, and advanced analysis across research databases and digital platforms. By providing a standardized framework, the Material Information Ontology facilitates cross-study comparison, materials selection, and knowledge sharing in materials research and industry. + +Typical applications include the integration of materials data from various sources, the development of new materials with specific properties, and the optimization of manufacturing processes. The ontology is actively maintained and extended to incorporate new materials, technologies, and research requirements. + +**Example Usage**: +Annotate a materials database with Material Information Ontology terms to specify material properties, manufacturing processes, and environmental conditions, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/matonto.rst b/docs/source/benchmarking/materials_science_and_engineering/matonto.rst index 38c356ce..2363e9da 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/matonto.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/matonto.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Material Ontology (MatOnto) ======================================================================================================== -The Material Ontology (MatOnto) is based on the upper level ontology, the BFO. +The Material Ontology (MatOnto) is a domain ontology based on the Basic Formal Ontology (BFO) designed to provide a structured vocabulary for representing materials, their properties, and relationships in materials science and engineering. MatOnto supports semantic annotation of materials data, enabling interoperability, data integration, and advanced analysis across research databases, digital twins, and manufacturing systems. The ontology covers key concepts such as material types, compositions, processing methods, properties, and applications. MatOnto is designed for extensibility, allowing researchers and engineers to describe new materials, characterization techniques, and performance metrics. By providing a standardized framework, MatOnto facilitates cross-study comparison, materials selection, and knowledge sharing in materials research and industry. The ontology is actively maintained and extended to incorporate new materials, technologies, and research requirements. + +**Example Usage**: +Annotate a materials database with MatOnto terms to specify material types (e.g., polymer, alloy), composition, processing methods, and properties (e.g., tensile strength, thermal conductivity), enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/matvoc.rst b/docs/source/benchmarking/materials_science_and_engineering/matvoc.rst index 05456f69..7cf4f0c8 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/matvoc.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/matvoc.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Materials Vocabulary (MatVoc) ======================================================================================================== -The official ontology produced in the context of the STREAM project. +The Materials Vocabulary (MatVoc) is an ontology developed within the STREAM project to provide a structured vocabulary for representing materials and their properties. It aims to facilitate the integration and retrieval of materials data across various domains, supporting applications in materials science, engineering, and related fields. MatVoc encompasses key entities such as materials, properties, and processes, and models relationships to capture the complexity of materials data. + +The ontology employs a class-based modeling approach, defining classes for different types of materials and properties, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MatVoc supports the integration of data from experimental studies, computational simulations, and industrial applications, promoting interoperability and data-driven research in materials science. + +Typical applications of MatVoc include the development of new materials with specific properties, the optimization of materials for industrial applications, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MatVoc enhances collaboration and innovation in the field of materials science. + +**Example Usage**: +Annotate a materials database with MatVoc terms to specify material types, properties, and processes, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/matwerk.rst b/docs/source/benchmarking/materials_science_and_engineering/matwerk.rst index 11baa8b6..7ff853e2 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/matwerk.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/matwerk.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ NFDI MatWerk Ontology (MatWerk) ======================================================================================================== -NFDI-MatWerk aims to establish a digital infrastructure for Materials Science and Engineering (MSE), fostering improved data sharing and collaboration. This repository provides comprehensive documentation for NFDI MatWerk Ontology (MWO) v3.0, a foundational framework designed to structure research data and enhance interoperability within the MSE community. To ensure compliance with top-level ontology standards, MWO v3.0 is aligned with the Basic Formal Ontology (BFO) and incorporates the modular approach of the NFDIcore mid-level ontology, enriching metadata through standardized classes and properties. The MWO addresses key aspects of MSE research data, including the NFDI-MatWerk community structure, covering task areas, infrastructure use cases, projects, researchers, and organizations. It also describes essential NFDI resources, such as software, workflows, ontologies, publications, datasets, metadata schemas, instruments, facilities, and educational materials. Additionally, MWO represents NFDI-MatWerk services, academic events, courses, and international collaborations. As the foundation for the MSE Knowledge Graph, MWO facilitates efficient data integration and retrieval, promoting collaboration and knowledge representation across MSE domains. This digital transformation enhances data discoverability, reusability, and accelerates scientific exchange, innovation, and discoveries by optimizing research data management and accessibility. +NFDI MatWerk Ontology (MWO) aims to establish a digital infrastructure for Materials Science and Engineering (MSE), fostering improved data sharing and collaboration. This ontology provides a comprehensive framework for structuring research data and enhancing interoperability within the MSE community. MWO is aligned with the Basic Formal Ontology (BFO) and incorporates the modular approach of the NFDIcore mid-level ontology, enriching metadata through standardized classes and properties. + +The ontology addresses key aspects of MSE research data, including the NFDI-MatWerk community structure, covering task areas, infrastructure use cases, projects, researchers, and organizations. It also describes essential NFDI resources, such as software, workflows, ontologies, publications, datasets, metadata schemas, instruments, facilities, and educational materials. Additionally, MWO represents NFDI-MatWerk services, academic events, courses, and international collaborations. + +As the foundation for the MSE Knowledge Graph, MWO facilitates efficient data integration and retrieval, promoting collaboration and knowledge representation across MSE domains. This digital transformation enhances data discoverability, reusability, and accelerates scientific exchange, innovation, and discoveries by optimizing research data management and accessibility. + +**Example Usage**: +Annotate a research project with MWO terms to specify task areas, infrastructure use cases, and resources, enabling semantic search and integration with the MSE Knowledge Graph. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/mdo.rst b/docs/source/benchmarking/materials_science_and_engineering/mdo.rst index f5c629a5..cbb45999 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mdo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mdo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,11 @@ Materials Design Ontology (MDO) ======================================================================================================== -MDO is an ontology for materials design field, representing the domain knowledge specifically related to solid-state physics and computational materials science. +The Materials Design Ontology (MDO) is a comprehensive framework developed to represent domain knowledge in the field of materials design, particularly focusing on solid-state physics and computational materials science. MDO provides a structured vocabulary for describing materials, their properties, and design processes, supporting both theoretical and experimental research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of materials, properties, and design processes, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MDO supports the integration of data from computational simulations and experimental studies, promoting interoperability and data-driven research in materials design. + +Typical applications of MDO include the development of new materials with specific properties, the optimization of materials for industrial applications, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MDO enhances collaboration and innovation in the field of materials design. Metrics & Statistics -------------------------- @@ -134,3 +136,6 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +**Example Usage**: +Annotate a materials design project with MDO terms to specify material types, design processes, and properties, enabling semantic search and integration with materials informatics platforms. diff --git a/docs/source/benchmarking/materials_science_and_engineering/mds.rst b/docs/source/benchmarking/materials_science_and_engineering/mds.rst index 795ab13c..d1491795 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mds.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mds.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Materials Data Science Ontology (MDS) ======================================================================================================== -Materials Data Science (MDS) is an ontology encompassing multiple domains relevant to materials science, chemical synthesis and characterizations, photovoltaics and geospatial datasets. The terms used for classes, subclasses and instances are mapped to PMDCo and BFO Ontologies. +The Materials Data Science Ontology (MDS) is a comprehensive framework designed to encompass multiple domains relevant to materials science, chemical synthesis, characterizations, photovoltaics, and geospatial datasets. MDS provides a structured vocabulary for representing data and processes in these domains, supporting both theoretical and experimental research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of materials, processes, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MDS supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. + +Typical applications of MDS include the integration of materials data from various sources, the development of new materials with specific properties, and the optimization of manufacturing processes. By providing a standardized vocabulary and framework, MDS enhances collaboration and innovation in the field of materials science. + +**Example Usage**: +Annotate a materials database with MDS terms to specify material properties, processes, and data, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/mdsonto.rst b/docs/source/benchmarking/materials_science_and_engineering/mdsonto.rst index f41ee48a..db0090b6 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mdsonto.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mdsonto.rst @@ -1,5 +1,3 @@ -The Modular Ontology for Materials and Data Science (MDS-Onto) -============================================================================== .. sidebar:: @@ -23,7 +21,18 @@ The Modular Ontology for Materials and Data Science (MDS-Onto) * - **Download** - `Download The Modular Ontology for Materials and Data Science (MDS-Onto) `_ -MDS-Onto is a domain (low) level ontology that describes terms in Materials Data Science. It is divided into six large modules: BuiltEnv, Exposure, Chemistry, Manufacture, Characterization, and Geospatial. Under each module, there are multiple sub-modules such as FTIR, AFM, Chem-Rxn, PV-Module, Accelerated Exposure, etc. +The Modular Ontology for Materials and Data Science (MDS-Onto) +============================================================================== + +MDS-Onto is a domain-level ontology that describes terms in Materials Data Science. It is divided into six large modules: BuiltEnv, Exposure, Chemistry, Manufacture, Characterization, and Geospatial. Each module contains multiple sub-modules such as FTIR, AFM, Chem-Rxn, PV-Module, and Accelerated Exposure, providing a comprehensive framework for representing data and processes in materials science. + +MDS-Onto employs a modular approach, defining classes and properties for each module to capture the complexity of materials data science. It supports semantic annotation of materials data, enabling interoperability, data integration, and advanced analysis across research databases and digital platforms. By providing a standardized framework, MDS-Onto facilitates cross-study comparison, materials selection, and knowledge sharing in materials research and industry. + +Typical applications include the integration of materials data from various sources, the development of new materials with specific properties, and the optimization of manufacturing processes. The ontology is actively maintained and extended to incorporate new materials, technologies, and research requirements. + +**Example Usage**: +Annotate a materials database with MDS-Onto terms to specify material properties, processes, and data, enabling semantic search and integration with materials informatics platforms. + Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/mechanicaltesting.rst b/docs/source/benchmarking/materials_science_and_engineering/mechanicaltesting.rst index a4ddc540..34e34b30 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mechanicaltesting.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mechanicaltesting.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Mechanical Testing Ontology (MechanicalTesting) ======================================================================================================== -A domain ontology for mechanical testing based on EMMO. +The Mechanical Testing Ontology (MechanicalTesting) is a domain ontology developed to represent knowledge in the field of mechanical testing, based on the Elementary Multiperspective Material Ontology (EMMO). It provides a structured vocabulary for describing mechanical testing methods, equipment, and results, supporting both experimental and computational research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of mechanical tests, equipment, and results, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MechanicalTesting supports the integration of data from experimental studies and simulations, promoting interoperability and data-driven research in mechanical testing. + +Typical applications of MechanicalTesting include the development of new testing methods, the optimization of testing procedures, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MechanicalTesting enhances collaboration and innovation in the field of mechanical testing. + +**Example Usage**: +Annotate a mechanical testing dataset with MechanicalTesting terms to specify test types, equipment, and results, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/microstructures.rst b/docs/source/benchmarking/materials_science_and_engineering/microstructures.rst index 74ee6ef0..95c21d51 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/microstructures.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/microstructures.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ EMMO-based ontology for microstructures (MicroStructures) ======================================================================================================== -This is intended to be a domain ontology for metallic microstructures, covering aspects like: composition, particles, both stable (primary) and metastable (precipitates), grains, subgrains, grain boundaries & particle free zones (PFZs), texture, dislocations. The aim is to support both microstructure modelling as well as characterisation. +The EMMO-based ontology for microstructures (MicroStructures) is a domain ontology designed to represent metallic microstructures, including their composition, particles (both stable and metastable), grains, subgrains, grain boundaries, particle free zones (PFZs), texture, and dislocations. The ontology provides a structured vocabulary for describing microstructure features, supporting both microstructure modeling and experimental characterization. MicroStructures enables semantic annotation of microstructural data, facilitating interoperability, data integration, and advanced analysis in materials science and engineering. The ontology is designed for extensibility, allowing researchers to describe new microstructure types, characterization techniques, and material systems. By providing a standardized framework, MicroStructures supports cross-study comparison, microstructure-property correlation, and knowledge sharing in materials research. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community. + +**Example Usage**: +Annotate a microstructure characterization dataset with MicroStructures terms to specify grain size distribution, particle types, texture, and dislocation density, enabling semantic search and integration with materials databases and modeling tools. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/mmo.rst b/docs/source/benchmarking/materials_science_and_engineering/mmo.rst index d03f707a..a2fdcb3d 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mmo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mmo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Materials Mechanics Ontology (MMO) ======================================================================================================== -The materials mechanics ontology is an application-level ontology that was created for supporting named entity recognition tasks for materials fatigue domain. The ontology covers some fairly general MSE concepts that could prospectively be merged into PMDco or other upper materials ontologies such as descriptions of crystallographic defects and microstructural entities. Furthermore, concepts related to the materials fatigue subdomain are also heavily incorporated. +The Materials Mechanics Ontology (MMO) is an application-level ontology developed to support named entity recognition tasks in the materials fatigue domain. It provides a structured vocabulary for describing concepts related to materials mechanics, including crystallographic defects, microstructural entities, and materials fatigue. MMO is designed to facilitate the integration of materials mechanics data, supporting both theoretical and experimental research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of materials, defects, and microstructures, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MMO supports the integration of data from various sources, promoting interoperability and data-driven research in materials mechanics. + +Typical applications of MMO include the development of new materials with specific mechanical properties, the optimization of materials for fatigue resistance, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MMO enhances collaboration and innovation in the field of materials mechanics. + +**Example Usage**: +Annotate a materials mechanics dataset with MMO terms to specify defect types, microstructural features, and fatigue properties, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/molbrinell.rst b/docs/source/benchmarking/materials_science_and_engineering/molbrinell.rst index 777fdbdc..b58d731b 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/molbrinell.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/molbrinell.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ MatoLab Brinell Test Ontology (MOL_BRINELL) ======================================================================================================== -An ontology for describing the Brinell hardness testing process, made in the Materials Open Lab Project. +The MatoLab Brinell Test Ontology (MOL_BRINELL) is a domain ontology developed to describe the Brinell hardness testing process. It provides a structured vocabulary for representing the testing methods, equipment, and results, supporting both experimental and computational research in materials testing. + +The ontology employs a class-based modeling approach, defining classes for different types of tests, equipment, and results, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MOL_BRINELL supports the integration of data from experimental studies and simulations, promoting interoperability and data-driven research in materials testing. + +Typical applications of MOL_BRINELL include the development of new testing methods, the optimization of testing procedures, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MOL_BRINELL enhances collaboration and innovation in the field of materials testing. + +**Example Usage**: +Annotate a Brinell hardness testing dataset with MOL_BRINELL terms to specify test types, equipment, and results, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/moltensile.rst b/docs/source/benchmarking/materials_science_and_engineering/moltensile.rst index e4697eb8..bf9d364f 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/moltensile.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/moltensile.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,12 @@ Matolab Tensile Test Ontology (MOL_TENSILE) ======================================================================================================== -An ontology for describing the tensile test process, made in the Materials Open Lab Project. + +The Matolab Tensile Test Ontology (MOL_TENSILE) is a domain ontology developed to describe the tensile test process. It provides a structured vocabulary for representing the testing methods, equipment, and results, supporting both experimental and computational research in materials testing. + +The ontology employs a class-based modeling approach, defining classes for different types of tests, equipment, and results, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MOL_TENSILE supports the integration of data from experimental studies and simulations, promoting interoperability and data-driven research in materials testing. + +Typical applications of MOL_TENSILE include the development of new testing methods, the optimization of testing procedures, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MOL_TENSILE enhances collaboration and innovation in the field of materials testing. Metrics & Statistics -------------------------- @@ -134,3 +137,5 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +Annotate a tensile testing dataset with MOL_TENSILE terms to specify test types, equipment, and results, enabling semantic search and integration with materials informatics platforms. diff --git a/docs/source/benchmarking/materials_science_and_engineering/mseo.rst b/docs/source/benchmarking/materials_science_and_engineering/mseo.rst index cf4b24c2..71936f97 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mseo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mseo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Materials Science and Engineering Ontology (MSEO) ======================================================================================================== -MSEO utilizes the IOF Ontology stack giving materials scientists and engineers the ability to represent their experiments and resulting data. The goal is to create machine and human readable sematic data which can be easily digested by other science domains. It is a product of the joint venture Materials Open Lab Project between the Bundesanstalt für Materialforschung und -prüfung (BAM) and the Fraunhofer Group MATERIALS and uses the BWMD ontology created by Fraunhofer IWM as a starting point. +The Materials Science and Engineering Ontology (MSEO) is a domain ontology developed to represent experiments and resulting data in materials science and engineering. It provides a structured vocabulary for describing materials, processes, and data, supporting both theoretical and experimental research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of materials, processes, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MSEO supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. + +Typical applications of MSEO include the integration of materials data from various sources, the development of new materials with specific properties, and the optimization of manufacturing processes. By providing a standardized vocabulary and framework, MSEO enhances collaboration and innovation in the field of materials science. + +**Example Usage**: +Annotate a materials science experiment with MSEO terms to specify material types, processes, and data, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/msle.rst b/docs/source/benchmarking/materials_science_and_engineering/msle.rst index 6a2a1879..d4b8ce2b 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/msle.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/msle.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Material Science Lab Equipment Ontology (MSLE) ======================================================================================================== -The current ontology describes Material Science Lab Equipment. +The Material Science Lab Equipment Ontology (MSLE) is a domain ontology developed to describe laboratory equipment used in materials science. It provides a structured vocabulary for representing equipment, processes, and data, supporting both experimental and computational research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of equipment, processes, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MSLE supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. + +Typical applications of MSLE include the integration of laboratory data from various sources, the development of new materials with specific properties, and the optimization of laboratory processes. By providing a standardized vocabulary and framework, MSLE enhances collaboration and innovation in the field of materials science. + +**Example Usage**: +Annotate a laboratory equipment dataset with MSLE terms to specify equipment types, processes, and data, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/nanomine.rst b/docs/source/benchmarking/materials_science_and_engineering/nanomine.rst index 2f074153..d6893b95 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/nanomine.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/nanomine.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ NanoMine Ontology (NanoMine) ======================================================================================================== -Polymer Nanocomposites based ontology which enable researchers to develop and test broad-reaching hypotheses about how inter-relationships between different materials processing methods and composition result in specific changes in material properties. +The NanoMine Ontology is a domain ontology developed to support research in polymer nanocomposites. It provides a structured vocabulary for representing the inter-relationships between different materials processing methods, compositions, and resulting material properties. NanoMine enables researchers to develop and test hypotheses about how these inter-relationships affect material performance, supporting both experimental and computational research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of materials, processes, and properties, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. NanoMine supports the integration of data from various sources, promoting interoperability and data-driven research in polymer nanocomposites. + +Typical applications of NanoMine include the development of new polymer nanocomposites with specific properties, the optimization of processing methods, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, NanoMine enhances collaboration and innovation in the field of polymer nanocomposites. + +**Example Usage**: +Annotate a polymer nanocomposite dataset with NanoMine terms to specify material types, processing methods, and properties, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/oiecharacterisation.rst b/docs/source/benchmarking/materials_science_and_engineering/oiecharacterisation.rst index 0c9ceca5..eac0198c 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/oiecharacterisation.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/oiecharacterisation.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Open Innovation Environment Characterisation (OIECharacterisation) ======================================================================================================== -EMMO-compliant, domain-level OIE ontology tackling the areas of characterization methods. +The Open Innovation Environment Characterisation (OIECharacterisation) ontology is an EMMO-compliant, domain-level ontology developed to represent characterization methods in materials science. It provides a structured vocabulary for describing characterization techniques, equipment, and data, supporting both experimental and computational research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of characterization methods, equipment, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OIECharacterisation supports the integration of data from various sources, promoting interoperability and data-driven research in materials characterization. + +Typical applications of OIECharacterisation include the development of new characterization methods, the optimization of characterization procedures, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OIECharacterisation enhances collaboration and innovation in the field of materials characterization. + +**Example Usage**: +Annotate a characterization dataset with OIECharacterisation terms to specify characterization methods, equipment, and data, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/oiemanufacturing.rst b/docs/source/benchmarking/materials_science_and_engineering/oiemanufacturing.rst index 7f9ba6ae..33430333 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/oiemanufacturing.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/oiemanufacturing.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Open Innovation Environment Manufacturing (OIEManufacturing) ======================================================================================================== -The manufacturing module populates the physicalistic perspective with manufacturing subclasses categorised according to modern applied physical sciences. +The Open Innovation Environment Manufacturing (OIEManufacturing) ontology is a domain-level ontology developed to represent manufacturing processes in materials science. It provides a structured vocabulary for describing manufacturing methods, equipment, and data, supporting both experimental and computational research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of manufacturing methods, equipment, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OIEManufacturing supports the integration of data from various sources, promoting interoperability and data-driven research in materials manufacturing. + +Typical applications of OIEManufacturing include the development of new manufacturing methods, the optimization of manufacturing processes, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OIEManufacturing enhances collaboration and innovation in the field of materials manufacturing. + +**Example Usage**: +Annotate a manufacturing dataset with OIEManufacturing terms to specify manufacturing methods, equipment, and data, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/oiematerials.rst b/docs/source/benchmarking/materials_science_and_engineering/oiematerials.rst index 1fe66de2..6225516b 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/oiematerials.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/oiematerials.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,15 @@ Open Innovation Environment Materials (OIEMaterials) ======================================================================================================== -The materials module populates the physicalistic perspective with materials subclasses categorised according to modern applied physical sciences. + +The Open Innovation Environment Materials (OIEMaterials) ontology is a domain-level ontology developed to represent materials in materials science. It provides a structured vocabulary for describing materials, their properties, and data, supporting both experimental and computational research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of materials, properties, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OIEMaterials supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. + +Typical applications of OIEMaterials include the development of new materials with specific properties, the optimization of material properties, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OIEMaterials enhances collaboration and innovation in the field of materials science. + +**Example Usage**: +Annotate a materials dataset with OIEMaterials terms to specify material types, properties, and data, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/oiemodels.rst b/docs/source/benchmarking/materials_science_and_engineering/oiemodels.rst index 76f3944b..fe931d11 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/oiemodels.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/oiemodels.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Open Innovation Environment Models (OIEModels) ======================================================================================================== -The models module defines models as semiotic signs that stands for an object by resembling or imitating it, in shape or by sharing a similar logical structure. +The Open Innovation Environment Models (OIEModels) ontology is a domain-level ontology developed to represent models in materials science. It provides a structured vocabulary for describing models, their properties, and data, supporting both experimental and computational research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of models, properties, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OIEModels supports the integration of data from various sources, promoting interoperability and data-driven research in materials modeling. + +Typical applications of OIEModels include the development of new models with specific properties, the optimization of model properties, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OIEModels enhances collaboration and innovation in the field of materials modeling. + +**Example Usage**: +Annotate a modeling dataset with OIEModels terms to specify model types, properties, and data, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/oiesoftware.rst b/docs/source/benchmarking/materials_science_and_engineering/oiesoftware.rst index 9af4b558..4271682f 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/oiesoftware.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/oiesoftware.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Open Innovation Environment Software (OIESoftware) ======================================================================================================== -EMMO-compliant, domain-level OIE ontology tackling the areas of software products. +The Open Innovation Environment Software Ontology (OIESoftware) is an EMMO-compliant, domain-level ontology designed to represent software products and their roles in materials science and engineering. OIESoftware provides a structured vocabulary for describing software tools, simulation codes, data analysis packages, and their relationships to computational workflows and experimental processes. The ontology supports semantic annotation of software metadata, enabling interoperability, data integration, and advanced analytics across research platforms and digital infrastructures. OIESoftware is designed for extensibility, allowing researchers and developers to describe new software products, functionalities, and application domains. By providing a standardized framework, OIESoftware facilitates software discovery, workflow automation, and knowledge sharing in computational materials science. The ontology is actively maintained and extended to incorporate new software technologies and research requirements. + +**Example Usage**: +Annotate a computational workflow with OIESoftware terms to specify the simulation codes used (e.g., "LAMMPS", "VASP"), their input/output formats, and their roles in the workflow, enabling semantic search and integration with research data management systems. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/ontocape.rst b/docs/source/benchmarking/materials_science_and_engineering/ontocape.rst index 3b2edf17..66ce42d1 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/ontocape.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/ontocape.rst @@ -72,3 +72,4 @@ Usage Example term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations +Annotate a process engineering project with OntoCAPE terms to specify process types, equipment, and data, enabling semantic search and integration with process informatics platforms. diff --git a/docs/source/benchmarking/materials_science_and_engineering/ontorule.rst b/docs/source/benchmarking/materials_science_and_engineering/ontorule.rst index 08f322ae..ebbc5239 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/ontorule.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/ontorule.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Ontology for the Steel Domain (ONTORULE) ======================================================================================================== -This deliverable consists of the ontology developed in ONTORULE for the steel industry use case. It is presented as an attachment to this document as an html document which was generated by SpecGen from the OWL file. The original OWL file is also included. This document describes the different concepts and attributes included in the ontology. For a better understanding of the decisions taken at the time of representing the knowledge in the ontology, the reader is encouraged to also read the document D5.4. +The Ontology for the Steel Domain (ONTORULE) is a domain ontology developed for the steel industry use case, providing a structured vocabulary for representing concepts, attributes, and relationships relevant to steel production and processing. ONTORULE supports the semantic annotation of steel industry data, including materials, processes, equipment, and quality attributes, enabling data integration and knowledge sharing across manufacturing systems. The ontology is designed for extensibility and can be adapted to represent new steel grades, production methods, and regulatory requirements. ONTORULE facilitates advanced analytics, process optimization, and compliance management in the steel industry by providing a standardized framework for knowledge representation. The ontology is documented with an HTML specification generated from the OWL file and is aligned with best practices in industrial ontology development. + +**Example Usage**: +Annotate a steel manufacturing process with ONTORULE terms to specify the materials used, process steps, equipment, and quality control attributes, enabling semantic search and integration with production and quality management systems. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/periodictable.rst b/docs/source/benchmarking/materials_science_and_engineering/periodictable.rst index 797ac8b3..ede36fb1 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/periodictable.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/periodictable.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Periodic Table of the Elements Ontology (PeriodicTable) ======================================================================================================== -PeriodicTable.owl is a representation of the Periodic Table of the Elements in the OWL Web Ontology Language. It provides reference data to support Semantic Web applications in chemistry and related disciplines. +The Periodic Table of the Elements Ontology (PeriodicTable) is a representation of the Periodic Table of the Elements in the OWL Web Ontology Language. It provides reference data to support Semantic Web applications in chemistry and related disciplines. The ontology offers a structured vocabulary for describing elements, their properties, and relationships, supporting both theoretical and experimental research in chemistry. + +The ontology employs a class-based modeling approach, defining classes for different types of elements, properties, and relationships, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. PeriodicTable supports the integration of data from various sources, promoting interoperability and data-driven research in chemistry. + +Typical applications of PeriodicTable include the development of new chemical compounds, the optimization of chemical processes, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, PeriodicTable enhances collaboration and innovation in the field of chemistry. + +**Example Usage**: +Annotate a chemical research project with PeriodicTable terms to specify element types, properties, and relationships, enabling semantic search and integration with chemical informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/photovoltaics.rst b/docs/source/benchmarking/materials_science_and_engineering/photovoltaics.rst index 6d609cd5..a1cad04d 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/photovoltaics.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/photovoltaics.rst @@ -25,7 +25,9 @@ EMMO Domain Ontology for Photovoltaics (Photovoltaics) ======================================================================================================== -This ontology is describing Perovskite solar cells. +The EMMO Domain Ontology for Photovoltaics is a specialized vocabulary built upon the Elementary Multiperspective Material Ontology (EMMO) framework for comprehensive semantic description of perovskite solar cells and photovoltaic systems. It provides structured vocabulary for describing photovoltaic device components including perovskite absorber layers, transport layers, electrodes, and contact materials, along with their properties and functions. The ontology captures essential photovoltaic device concepts such as optical properties (bandgap, absorption coefficients), electrical properties (carrier mobility, recombination), and performance metrics (efficiency, fill factor, open-circuit voltage). Photovoltaics ontology enables standardized annotation of experimental fabrication procedures, characterization techniques, and computational modeling of perovskite solar cells. The ontology facilitates knowledge integration in photovoltaics research by providing EMMO-compliant semantic representations compatible with broader materials science knowledge bases. + +**Example Usage**: Annotate a perovskite solar cell research study with Photovoltaics terms describing device configuration (n-i-p or p-i-n), perovskite composition (CsPbI3, MAPbI3), transport materials (TiO2, spiro-OMeTAD), performance parameters (PCE, Voc, Jsc), and fabrication methods (spin-coating, vapor deposition). Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/pldo.rst b/docs/source/benchmarking/materials_science_and_engineering/pldo.rst index 92e92979..e1250574 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/pldo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/pldo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Planar Defects Ontology (PLDO) ======================================================================================================== -PLDO is an ontology designed to describe planar defects in crystalline materials, such as grain boundaries and stacking faults, with a focus on their atomic-scale structure and properties. +The Planar Defects Ontology (PLDO) is an ontology designed to describe planar defects in crystalline materials, such as grain boundaries and stacking faults, with a focus on their atomic-scale structure and properties. PLDO provides a structured vocabulary for representing the types, properties, and interactions of planar defects, supporting both experimental and computational research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of planar defects, properties, and interactions, along with properties to describe their characteristics and effects on material properties. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. PLDO supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. + +Typical applications of PLDO include the development of new materials with specific defect properties, the optimization of material properties through defect engineering, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, PLDO enhances collaboration and innovation in the field of materials science. + +**Example Usage**: +Annotate a materials science study with PLDO terms to specify planar defect types, properties, and interactions, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/pmdco.rst b/docs/source/benchmarking/materials_science_and_engineering/pmdco.rst index 5ec404e5..5b67dbea 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/pmdco.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/pmdco.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,13 @@ The Platform MaterialDigital core ontology (PMDco) ======================================================================================================== -The PMD Core Ontology (PMDco) is a comprehensive framework for representing knowledge that encompasses fundamental concepts from the domains of materials science and engineering (MSE). The PMDco has been designed as a mid-level ontology to establish a connection between specific MSE application ontologies and the domain neutral concepts found in established top-level ontologies. The primary goal of the PMDco is to promote interoperability between diverse domains. + +The ontology employs a class-based modeling approach, defining classes for different types of materials, processes, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. PMDco supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. + +Typical applications of PMDco include the development of new materials with specific properties, the optimization of materials for industrial applications, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, PMDco enhances collaboration and innovation in the field of materials science. + +**Example Usage**: +Annotate a materials science project with PMDco terms to specify material types, processes, and data, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/podo.rst b/docs/source/benchmarking/materials_science_and_engineering/podo.rst index 662e926c..572c5c76 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/podo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/podo.rst @@ -25,7 +25,9 @@ Point Defects Ontology (PODO) ======================================================================================================== -PODO focuses on the description of point defects in crystalline materials. +PODO is a specialized ontology that formalizes the conceptualization and semantic representation of point defects in crystalline materials, enabling precise description and classification of atomic-scale defects. It provides structured vocabulary for describing different point defect types including vacancies, interstitials, substitutional defects, and antisite defects, along with their properties and formation mechanisms. PODO captures essential characteristics of point defects such as charge state, migration energy, binding interactions with other defects, and effects on material properties. The ontology enables systematic annotation of experimental observations (X-ray diffraction, electron microscopy) and computational predictions (first-principles calculations) of point defects in various crystal structures. PODO facilitates knowledge integration in materials informatics and computational materials databases by providing standardized semantic representations of point defect phenomena. + +**Example Usage**: Annotate a first-principles DFT study of point defects in semiconductors with PODO terms describing defect type (e.g., oxygen vacancy in TiO2), charge state (+2, neutral, -2), formation energy, charge transition levels, and effects on band structure and electrical properties. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/prima.rst b/docs/source/benchmarking/materials_science_and_engineering/prima.rst index 9330a404..82059b09 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/prima.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/prima.rst @@ -25,7 +25,9 @@ PRovenance Information in MAterials science (PRIMA) ======================================================================================================== -An ontology that captures the provenance information in the materials science domain. +PRIMA is a comprehensive ontology that formalizes and captures provenance information essential for understanding the complete lifecycle and traceability of materials science data, experiments, and computational workflows. It provides structured vocabulary for describing the origins, history, transformations, and chain of custody of materials science information from experimental synthesis through characterization to computational modeling. PRIMA enables documentation of experimental procedures, computational methods, instruments used, software tools employed, and human researchers involved in materials science investigations. The ontology captures temporal information (when experiments were conducted), spatial context (where work was performed), and relationships between different research activities and resulting data. PRIMA facilitates reproducibility, data authentication, and knowledge integration in materials science by providing standardized provenance metadata compatible with linked data standards. + +**Example Usage**: Annotate a materials science dataset with PRIMA terms describing experimental origin (synthesis method, instrument, date), processing steps applied, computational analyses performed, and final data product generation, enabling complete traceability and reproducibility of materials discovery workflows. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/ssn.rst b/docs/source/benchmarking/materials_science_and_engineering/ssn.rst index 62307f6d..307554b3 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/ssn.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/ssn.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Semantic Sensor Network Ontology (SSN) ======================================================================================================== -The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators. SSN follows a horizontal and vertical modularization architecture by including a lightweight but self-contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) for its elementary classes and properties. With their different scope and different degrees of axiomatization, SSN and SOSA are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things. Both ontologies are described below, and examples of their usage are given. +The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators. SSN follows a horizontal and vertical modularization architecture by including a lightweight but self-contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) for its elementary classes and properties. With their different scope and different degrees of axiomatization, SSN and SOSA are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things. Both ontologies are described below, and examples of their usage are given. + +The ontology employs a class-based modeling approach, defining classes for different types of sensors, observations, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SSN supports the integration of data from various sources, promoting interoperability and data-driven research in sensor networks. + +Typical applications of SSN include the development of new sensor network methods, the optimization of sensor data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SSN enhances collaboration and innovation in the field of sensor networks. + +**Example Usage**: +Annotate a sensor network dataset with SSN terms to specify sensor types, observations, and related data, enabling semantic search and integration with sensor network management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/systemcapabilities.rst b/docs/source/benchmarking/materials_science_and_engineering/systemcapabilities.rst index 24f804d1..fc4827d1 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/systemcapabilities.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/systemcapabilities.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ System Capabilities Ontology (SystemCapabilities) ======================================================================================================== -This ontology describes system capabilities, operating ranges, and survival ranges. +The System Capabilities Ontology (SystemCapabilities) is designed to describe system capabilities, operating ranges, and survival ranges in materials science and engineering. It provides a structured vocabulary for representing the capabilities and limitations of systems, supporting both theoretical and experimental research in materials science. + +The ontology employs a class-based modeling approach, defining classes for different types of systems, capabilities, and ranges, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SystemCapabilities supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. + +Typical applications of SystemCapabilities include the development of new systems with specific capabilities, the optimization of system performance, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SystemCapabilities enhances collaboration and innovation in the field of materials science. + +**Example Usage**: +Annotate a materials science project with SystemCapabilities terms to specify system types, capabilities, and ranges, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/materials_science_and_engineering/vimmp.rst b/docs/source/benchmarking/materials_science_and_engineering/vimmp.rst index 88e9373f..f95157e2 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/vimmp.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/vimmp.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Virtual Materials Marketplace Ontologies (VIMMP) ======================================================================================================== -The Virtual Materials Marketplace (VIMMP) project is developing an open platform for providing and accessing services related to materials modelling. Within VIMMP, a system of marketplace-level ontologies is developed to characterize services, models, and interactions between users; the European Materials and Modelling Ontology (EMMO, recently renamed while keeping the original acronym) is employed as a top-level ontology. The ontologies are used to annotate data that are stored in the ZONTAL Space component of VIMMP and to support the ingest and retrieval of data and metadata at the VIMMP marketplace front-end. +The Virtual Materials Marketplace Ontologies (VIMMP) are developed as part of the Virtual Materials Marketplace project, which aims to provide an open platform for accessing services related to materials modeling. VIMMP employs a system of marketplace-level ontologies to characterize services, models, and interactions between users, using the European Materials and Modelling Ontology (EMMO) as a top-level ontology. + +The ontologies provide a structured vocabulary for describing services, models, and interactions, supporting both theoretical and experimental research in materials modeling. VIMMP ontologies enable the annotation of data stored in the ZONTAL Space component and support the ingest and retrieval of data and metadata at the VIMMP marketplace front-end. + +Typical applications of VIMMP include the development of new materials modeling services, the optimization of modeling workflows, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, VIMMP enhances collaboration and innovation in the field of materials modeling. + +**Example Usage**: +Annotate a materials modeling project with VIMMP terms to specify service types, models, and interactions, enabling semantic search and integration with materials informatics platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/medicine/bto.rst b/docs/source/benchmarking/medicine/bto.rst index 8d59d203..bdd470e8 100644 --- a/docs/source/benchmarking/medicine/bto.rst +++ b/docs/source/benchmarking/medicine/bto.rst @@ -25,7 +25,9 @@ BRENDA Tissue Ontology (BTO) ======================================================================================================== -A structured controlled vocabulary for the source of an enzyme comprising tissues, cell lines, cell types and cell cultures. +The BRENDA Tissue Ontology (BTO) is a structured controlled vocabulary for systematically describing and classifying the tissues, cell lines, cell types, and cell cultures that serve as sources for enzymatic and biochemical studies. Developed as part of the BRENDA (BRaunschweig ENzyme DAtabase) project, BTO provides standardized terminology for identifying experimental sample origins and biological sources in biochemistry and molecular biology research. The ontology includes comprehensive hierarchical classifications of human tissues, animal tissues, plant tissues, and microbial cell types, enabling precise semantic annotation of biological materials used in enzyme research and biotechnology. BTO supports data integration across biochemical databases and enables accurate searches for enzymes studied in specific tissue contexts. + +**Example Usage**: Annotate an enzyme assay result with a BTO term like "BTO:0000079" (liver) or "BTO:0000142" (kidney) to indicate the tissue source of the enzyme sample. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/medicine/deb.rst b/docs/source/benchmarking/medicine/deb.rst index 2c2bc027..17b558eb 100644 --- a/docs/source/benchmarking/medicine/deb.rst +++ b/docs/source/benchmarking/medicine/deb.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Devices, Experimental scaffolds and Biomaterials Ontology (DEB) ======================================================================================================== -The devices, experimental scaffolds, and biomaterials ontology (DEB) is an open resource for organizing information about biomaterials, their design, manufacture, and biological testing. It was developed using text analysis for identifying ontology terms from a biomaterials gold standard corpus, systematically curated to represent the domain's lexicon. Topics covered were validated by members of the biomaterials research community. +The Devices, Experimental scaffolds and Biomaterials Ontology (DEB) is an open, community-driven ontology for organizing information about biomaterials, their design, manufacture, and biological testing. DEB provides a structured vocabulary for describing biomaterial types, experimental scaffolds, fabrication methods, and the biological assays used to evaluate them. The ontology was developed using text analysis of a biomaterials gold standard corpus and systematically curated to represent the domain's lexicon, with validation by biomaterials research experts. DEB supports semantic annotation of biomaterials research data, enabling interoperability, data integration, and advanced queries across experimental studies and databases. By providing a standardized framework, DEB facilitates reproducibility, knowledge sharing, and meta-analysis in biomaterials science and tissue engineering. The ontology is actively maintained and extended to incorporate new materials, experimental techniques, and biological endpoints as the field evolves. + +**Example Usage**: +Annotate a biomaterials experiment with DEB terms to specify the scaffold material (e.g., "collagen hydrogel"), fabrication method (e.g., "electrospinning"), and biological assay (e.g., "cell viability test"), enabling cross-study comparison and data integration. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/medicine/doid.rst b/docs/source/benchmarking/medicine/doid.rst index 6273c453..262b4fa4 100644 --- a/docs/source/benchmarking/medicine/doid.rst +++ b/docs/source/benchmarking/medicine/doid.rst @@ -25,7 +25,9 @@ Human Disease Ontology (DOID) ======================================================================================================== -The Disease Ontology has been developed as a standardized ontology for human disease with the purpose of providing the biomedical community with consistent, reusable and sustainable descriptions of human disease terms, phenotype characteristics and related medical vocabulary disease concepts. +The Disease Ontology (DOID) is a standardized, machine-readable ontology that provides consistent, reusable and sustainable descriptions of human diseases, medical conditions, and disease-related phenotypic characteristics. Developed collaboratively by the biomedical research community, DOID comprehensively covers disease classifications across diverse medical domains including infectious diseases, genetic disorders, cancers, cardiovascular diseases, and mental health conditions. The ontology employs hierarchical relationships to organize diseases from general categories to specific disease subtypes, enabling both broad and fine-grained disease annotation. DOID integrates with other biomedical ontologies (e.g., phenotype ontologies, gene ontologies) to link disease concepts with associated genes, symptoms, and environmental factors. The ontology is widely used in biomedical databases, genomics research, and clinical informatics for disease annotation and knowledge integration. + +**Example Usage**: Annotate a disease research paper or dataset with DOID terms such as "DOID:2841 (lymphoma)" or "DOID:1816 (diabetes mellitus)" to enable automated discovery of disease-related research and clinical data. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/medicine/enm.rst b/docs/source/benchmarking/medicine/enm.rst index 63a3a2dc..1c463bce 100644 --- a/docs/source/benchmarking/medicine/enm.rst +++ b/docs/source/benchmarking/medicine/enm.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Environmental Noise Measurement Ontology (ENM) ======================================================================================================== -The eNanoMapper project (https://www.enanomapper.net/), NanoCommons project (https://www.nanocommons.eu/) and ACEnano project (http://acenano-project.eu/) are creating a pan-European computational infrastructure for toxicological data management for ENMs, based on semantic web standards and ontologies. This ontology is an application ontology targeting the full domain of nanomaterial safety assessment. It re-uses several other ontologies including the NPO, CHEMINF, ChEBI, and ENVO. +The Environmental Noise Measurement Ontology (ENM) is an application ontology developed as part of the eNanoMapper (https://www.enanomapper.net/), NanoCommons (https://www.nanocommons.eu/), and ACEnano (http://acenano-project.eu/) projects to support toxicological data management for engineered nanomaterials (ENMs). ENM provides a comprehensive vocabulary for describing nanomaterial safety assessment, including experimental procedures, measurement techniques, material properties, and exposure scenarios. The ontology reuses and integrates terms from several established ontologies, such as the Nanoparticle Ontology (NPO), Chemical Information Ontology (CHEMINF), Chemical Entities of Biological Interest (ChEBI), and Environment Ontology (ENVO), to ensure semantic interoperability and data integration. ENM enables standardized annotation of nanomaterial safety data, facilitating data sharing, regulatory compliance, and advanced analysis across research projects and databases. By providing a common framework, ENM supports the development of computational infrastructure for toxicology, risk assessment, and environmental health studies. The ontology is actively maintained and extended to incorporate new concepts and requirements from the nanomaterial safety community. + +**Example Usage**: +Annotate a nanotoxicology study with ENM terms to specify the nanomaterial type, measurement methods, exposure conditions, and observed biological effects, enabling cross-study comparison and regulatory reporting. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/medicine/mfoem.rst b/docs/source/benchmarking/medicine/mfoem.rst index 2c7b03b8..bf328631 100644 --- a/docs/source/benchmarking/medicine/mfoem.rst +++ b/docs/source/benchmarking/medicine/mfoem.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Mental Functioning Ontology of Emotions - Emotion Module (MFOEM) ======================================================================================================== -The Mental Functioning Ontology - Emotion Module (MFOEM) aims to include all relevant aspects of affective phenomena including their bearers, the different types of emotions, moods, etc., their different parts and dimensions of variation, their facial and vocal expressions, and the role of emotions and affective phenomena in general in influencing human behavior.This class processes Mental Functioning Ontology of Emotions (MFOEM) using default behavior. +The Mental Functioning Ontology of Emotions - Emotion Module (MFOEM) is a domain ontology designed to comprehensively represent affective phenomena, including emotions, moods, and their various dimensions and expressions. MFOEM provides a structured vocabulary for describing the bearers of emotions, types of emotions, their parts, and the dimensions along which they vary (such as intensity, duration, and valence). The ontology also covers facial and vocal expressions of emotions and the influence of affective phenomena on human behavior. MFOEM supports semantic annotation of psychological and neuroscientific data, enabling interoperability, data integration, and advanced analysis in affective science research. By providing a standardized framework, MFOEM facilitates cross-study comparison, meta-analysis, and the development of emotion-aware applications in artificial intelligence and human-computer interaction. The ontology is actively maintained and extended to incorporate new findings and requirements from the affective science community. + +**Example Usage**: +Annotate a psychological study with MFOEM terms to specify the types of emotions measured (e.g., "fear," "joy"), their intensity, and observed facial expressions, enabling semantic integration and comparison across emotion research datasets. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/medicine/obi.rst b/docs/source/benchmarking/medicine/obi.rst index c3a198b4..a115e0ad 100644 --- a/docs/source/benchmarking/medicine/obi.rst +++ b/docs/source/benchmarking/medicine/obi.rst @@ -25,7 +25,9 @@ Ontology for Biomedical Investigations (OBI) ======================================================================================================== -The Ontology for Biomedical Investigations (OBI) helps you communicate clearly about scientific investigations by defining more than 2500 terms for assays, devices, objectives, and more. +The Ontology for Biomedical Investigations (OBI) provides a comprehensive vocabulary for describing scientific investigations, experimental designs, and biomedical research methodology. It defines over 2500 terms covering assays, devices, protocols, objectives, materials, measurements, and outcomes used in biomedical and life sciences research. OBI enables standardized semantic annotation of experimental workflows, making research methodologies transparent, reproducible, and interoperable across databases and computational systems. The ontology captures hierarchical relationships between experimental concepts, allowing researchers to precisely describe complex experimental designs and data collection procedures. OBI integrates with other biological ontologies (BFO, ChEBI, CHEBI) to provide comprehensive semantic representation of biomedical investigations. + +**Example Usage**: Annotate a microarray experiment with OBI terms such as "assay" for the type of investigation, "microarray device" for equipment, "RNA extraction" for material preparation steps, and measurement-related terms for results. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/news_and_media/bbc.rst b/docs/source/benchmarking/news_and_media/bbc.rst index 895f526b..7510752a 100644 --- a/docs/source/benchmarking/news_and_media/bbc.rst +++ b/docs/source/benchmarking/news_and_media/bbc.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ BBC Ontology (BBC) ======================================================================================================== -The BBC ontology codifies the logic that connects web documents, BBC products and platforms for which content is available. Currently, there are 10 major products in Future Media which produce content for BBC online. The majority of those contain more products dedicated in thematic areas, for example Education propositions are part of the K&L (Knowledge and Learning) product portfolio. +The BBC Ontology (BBC) is a domain ontology developed to represent the structure, relationships, and metadata of BBC web documents, products, and platforms. It provides a standardized vocabulary for describing content types, thematic areas, editorial products, and the connections between them. The ontology enables semantic annotation of BBC content, supporting advanced search, personalized recommendations, and content integration across BBC's digital ecosystem. BBC is designed for extensibility and interoperability, allowing integration with other BBC ontologies and external vocabularies for broader data linking. By providing a common semantic framework, the BBC Ontology facilitates content discovery, analytics, and knowledge management for editorial, educational, and entertainment products. The ontology is maintained as an open resource and is actively extended to support new content types, platforms, and user experiences. + +**Example Usage**: +Annotate a BBC web page with BBC Ontology terms to specify its content type (e.g., "NewsArticle"), associated product (e.g., "BBC News"), thematic area (e.g., "Education"), and relationships to related articles or platforms, enabling semantic search and cross-platform content integration. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/news_and_media/bbcbusiness.rst b/docs/source/benchmarking/news_and_media/bbcbusiness.rst index 9574830e..1925f044 100644 --- a/docs/source/benchmarking/news_and_media/bbcbusiness.rst +++ b/docs/source/benchmarking/news_and_media/bbcbusiness.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ BBC Business News Ontology (BBCBusiness) ======================================================================================================== -The Business News Ontology describes the concepts that occur in BBC business news. +The BBC Business News Ontology (BBCBusiness) is a domain-specific ontology designed to represent concepts, entities, and relationships relevant to business news coverage. It provides a structured vocabulary for describing companies, markets, financial instruments, economic indicators, business events, and regulatory developments. BBCBusiness enables semantic annotation of business news articles, supporting advanced search, aggregation, and analytics across news platforms. The ontology is designed for interoperability with other BBC ontologies and external vocabularies, facilitating integration with financial data sources and knowledge graphs. By providing a standardized framework, BBCBusiness supports automated content tagging, trend analysis, and personalized news delivery in business journalism. The ontology is actively maintained and extended to reflect changes in the global business landscape and emerging topics in finance and economics. + +**Example Usage**: +Annotate a business news article with BBCBusiness terms to specify the companies involved, market sectors, financial events (e.g., mergers, IPOs), and economic indicators, enabling semantic search and cross-platform business news analysis. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/news_and_media/bbccms.rst b/docs/source/benchmarking/news_and_media/bbccms.rst index 121360a6..cea33af4 100644 --- a/docs/source/benchmarking/news_and_media/bbccms.rst +++ b/docs/source/benchmarking/news_and_media/bbccms.rst @@ -25,7 +25,9 @@ BBC CMS Ontology (BBCCMS) ======================================================================================================== -The Content Management Systems ontology defines the terms that LDP needs to interact with systems that produce content. The linked data platform contain semantic metadata for the creative content and also the things the BBC produces content about. The CMS ontology defines how these things and content are associated with other BBC instances of the same thing. +The BBC CMS Ontology is a specialized vocabulary for representing and managing relationships between content management systems, creative content, and the entities that BBC produces content about. It defines standardized terms and structures for Content Management Systems (CMS) to interact with linked data platforms, enabling semantic representation of how content relates to real-world concepts. The ontology captures associations between different instances of the same concept across multiple BBC systems, ensuring consistency and linkage of related entities (people, places, organizations, events) referenced in content. BBCCMS facilitates content integration and semantic linking across BBC's diverse content production systems and publishing platforms. The ontology enables sophisticated content discovery and recommendations by providing explicit relationships between content and the concepts it addresses. + +**Example Usage**: Link a BBC news article or program to BBC Core Concepts (people, organizations, places, events) with BBCCMS terms that establish how the same entity is referenced across different content pieces and editorial domains. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/news_and_media/bbccoreconcepts.rst b/docs/source/benchmarking/news_and_media/bbccoreconcepts.rst index 36bea74f..3ecf2afc 100644 --- a/docs/source/benchmarking/news_and_media/bbccoreconcepts.rst +++ b/docs/source/benchmarking/news_and_media/bbccoreconcepts.rst @@ -25,7 +25,9 @@ BBC Core Concepts Ontology (BBCCoreConcepts) ======================================================================================================== -The generic BBC ontology for people, places, events, organisations, themes which represent things that make sense across the BBC. This model is meant to be generic enough, and allow clients (domain experts) link their own concepts e.g., athletes or politicians using rdfs:sublClassOf the particular concept. +The BBC Core Concepts Ontology is a foundational vocabulary defining generic concepts that are universally relevant across BBC's diverse content domains including people, places, events, organizations, and themes. It provides a shared semantic model for representing real-world entities that frequently appear in BBC content, enabling consistent annotation and discovery across multiple media types and editorial departments. BBCCoreConcepts is designed to be sufficiently generic to serve as a base ontology, allowing domain experts and specialized ontologies to extend it for specific use cases (e.g., athletes for sports content, politicians for news content) through rdfs:subClassOf relationships. The ontology enables semantic interoperability across BBC's content production systems and linked data platforms by providing standardized definitions of common entities. BBCCoreConcepts facilitates sophisticated content linking and discovery by establishing shared semantic representations of entities referenced across diverse BBC programs and services. + +**Example Usage**: Define domain-specific concepts by creating athlete, musician, or politician subclasses of the generic "Person" concept in BBC Core Concepts, enabling both generic searches and domain-specific searches for related content. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/news_and_media/bbccreativework.rst b/docs/source/benchmarking/news_and_media/bbccreativework.rst index 3e6fc5e0..d468a4cb 100644 --- a/docs/source/benchmarking/news_and_media/bbccreativework.rst +++ b/docs/source/benchmarking/news_and_media/bbccreativework.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ BBC Creative Work Ontology (BBCCreativeWork) ======================================================================================================== -This ontology defines the terms required to describe the creative works produced by the BBC and their associated metadata. This ontology powers reading and writing creative works in the triplestore using tags associated with them (about) their more specific types (BlogPost, NewsItem, Programme) and audiences (audience). +The BBC Creative Work Ontology (BBCCreativeWork) is a domain ontology designed to represent creative works produced by the BBC, such as articles, blog posts, news items, programmes, and their associated metadata. It provides a structured vocabulary for describing creative works, their types, audiences, tags, and relationships to other works and entities. BBCCreativeWork supports semantic annotation of content, enabling advanced search, recommendation, and content management across BBC platforms. The ontology is designed for extensibility, allowing integration with other BBC ontologies and external vocabularies for broader data interoperability. By providing standardized terms and relationships, BBCCreativeWork facilitates content linking, audience targeting, and analytics in digital publishing. The ontology is maintained as an open resource and is actively extended to support new content types and publishing requirements. + +**Example Usage**: +Annotate a BBC news article with BBCCreativeWork terms to specify its type (e.g., "NewsItem"), associated tags (e.g., "climate change"), intended audience (e.g., "general public"), and relationships to related programmes or blog posts, enabling semantic search and personalized content recommendations. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/news_and_media/bbcfood.rst b/docs/source/benchmarking/news_and_media/bbcfood.rst index b070090c..0548a7ce 100644 --- a/docs/source/benchmarking/news_and_media/bbcfood.rst +++ b/docs/source/benchmarking/news_and_media/bbcfood.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ BBC Food Ontology (BBCFood) ======================================================================================================== -The Food Ontology is a simple lightweight ontology for publishing data about recipes, including the foods they are made from and the foods they create as well as the diets, menus, seasons, courses and occasions they may be suitable for. Whilst it originates in a specific BBC use case, the Food Ontology should be applicable to a wide range of recipe data publishing across the web. +The BBC Food Ontology (BBCFood) is a lightweight ontology designed to represent recipes, ingredients, and culinary concepts for publishing food-related data on the web. It provides a structured vocabulary for describing foods, their components, preparation methods, dietary suitability, menus, courses, seasons, and occasions. BBCFood supports semantic annotation of recipes and food content, enabling interoperability and data integration across recipe websites, nutrition databases, and culinary applications. The ontology is designed for extensibility and can be adapted to a wide range of food data publishing use cases beyond the original BBC context. By providing standardized terms and relationships, BBCFood facilitates advanced search, recommendation, and personalized meal planning. The ontology is maintained as an open resource and is compatible with other food and nutrition ontologies for broader data integration. + +**Example Usage**: +Annotate a recipe database with BBCFood terms to specify ingredients, preparation steps, dietary restrictions (e.g., "vegetarian"), and suitable occasions (e.g., "holiday dinner"), enabling semantic search and personalized recipe recommendations. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/news_and_media/bbcpolitics.rst b/docs/source/benchmarking/news_and_media/bbcpolitics.rst index 5c899977..a3b22375 100644 --- a/docs/source/benchmarking/news_and_media/bbcpolitics.rst +++ b/docs/source/benchmarking/news_and_media/bbcpolitics.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,9 @@ BBC Politics News Ontology (BBCPolitics) ======================================================================================================== -The Politics Ontology describes the concepts that occur in BBC politics news. +The BBC Politics Ontology provides a formal vocabulary for describing and classifying political concepts, entities, and events as they appear in BBC news coverage. It models key political domain concepts including politicians, political parties, government institutions, legislative processes, electoral systems, and political ideologies. The ontology captures relationships between political entities (e.g., politicians affiliated with parties, parties contesting elections) and enables semantic annotation of news articles, television programs, and online content related to politics. It supports content discovery and automated linking of related political news stories across BBC's diverse platforms and archives. The ontology follows BBC's linked data principles and integrates with other BBC ontologies (People, Places, Organizations) to provide comprehensive semantic context. + +**Example Usage**: Annotate a political news article with BBCPolitics terms to identify mentioned politicians (as foaf:Person instances), their party affiliations, relevant legislation being discussed, and electoral contexts. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/news_and_media/bbcprogrammes.rst b/docs/source/benchmarking/news_and_media/bbcprogrammes.rst index c44ac7de..dfb934b6 100644 --- a/docs/source/benchmarking/news_and_media/bbcprogrammes.rst +++ b/docs/source/benchmarking/news_and_media/bbcprogrammes.rst @@ -25,7 +25,9 @@ BBC Programmes Ontology (BBCProgrammes) ======================================================================================================== -This ontology aims at providing a simple vocabulary for describing programmes. It covers brands, series (seasons), episodes, broadcast events, broadcast services,etc. Its development was funded by the BBC, and is heavily grounded on previous programmes data modelling work done there. +The BBC Programmes Ontology is a specialized vocabulary for describing television and radio programmes, their structure, and broadcast distribution across BBC platforms. It provides standardized terminology for representing programme hierarchies including brands (show franchises), series/seasons (annual or thematic groupings), episodes (individual content pieces), broadcast events (scheduled transmissions), and broadcast services (channels, stations). BBCProgrammes enables precise semantic annotation of broadcast content metadata including airing dates, durations, genres, and relationships between programmes. The ontology facilitates content discovery and programme management across BBC's television and radio platforms by providing standardized semantic structures. BBCProgrammes development was informed by extensive BBC programme data modelling experience, ensuring practical applicability in production systems. + +**Example Usage**: Represent a television series using BBCProgrammes terms with brand (Doctor Who), seasons/series (Series 10, Series 11), individual episodes with episode numbers and air dates, and associated broadcast events showing transmission times across different BBC channels. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/news_and_media/bbcprovenance.rst b/docs/source/benchmarking/news_and_media/bbcprovenance.rst index df58cc65..47ed233f 100644 --- a/docs/source/benchmarking/news_and_media/bbcprovenance.rst +++ b/docs/source/benchmarking/news_and_media/bbcprovenance.rst @@ -25,7 +25,9 @@ BBC Provenance News Ontology (BBCProvenance) ======================================================================================================== -An ontology to capture data about the provenance of data in an RDF Triple Store. This provenance is focused on the immediate providers and not the ultimate source, so for example, this would record that geodata was provided by the BBC Locator team, and not geonames. In the Linked Data Platform, this data is applied to contexts or named graphs. A named graph is, in effect, a 'fourth part' to a triple, hence the term 'quad store'. +The BBC Provenance News Ontology is a specialized vocabulary for capturing and representing provenance metadata about data quality, source attribution, and immediate data providers in RDF knowledge graphs and semantic web applications. It focuses on recording immediate data providers (e.g., which internal BBC team or external service provided data) rather than ultimate sources, enabling accountability and quality tracking in linked data systems. BBCProvenance applies provenance annotations to named graphs (quads in RDF stores), providing a fourth dimension to triples that enables context-aware data management and versioning. The ontology supports data governance and management in linked data platforms by providing standardized provenance metadata structures. BBCProvenance facilitates trust and quality assessment in semantic web applications by enabling transparent tracking of data origins and transformations. + +**Example Usage**: Annotate geodata in a named graph with BBCProvenance terms indicating that the data was provided by the BBC Locator team (immediate provider) on a specific date, with specific quality assurance level, enabling proper attribution and version tracking. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/news_and_media/bbcsport.rst b/docs/source/benchmarking/news_and_media/bbcsport.rst index 9a16f7ee..b1cd1b4b 100644 --- a/docs/source/benchmarking/news_and_media/bbcsport.rst +++ b/docs/source/benchmarking/news_and_media/bbcsport.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,7 @@ BBC Sport Ontology (BBCSport) ======================================================================================================== -The Sport Ontology is a simple lightweight ontology for publishing data about competitive sports events. The terms in this ontology allow data to be published about: The structure of sports tournaments as a series of eventsThe competing of agents in a competitionThe type of discipline a event involvesThe award associated with the competition and how received it...etc Whilst it originates in a specific BBC use case, the Sport Ontology should be applicable to a wide range of competitive sporting events data publishing use cases. Care has been taken to try and ensure interoperability with more general ontologies in use. In particular, it draws heavily upon the events ontology. +The BBC Sport Ontology (BBCSport) is a lightweight ontology designed to represent competitive sports events, tournaments, participants, disciplines, and awards. It provides a structured vocabulary for describing the structure of sports tournaments as a series of events, the participation of agents (teams, athletes), the type of discipline involved, and the awards associated with competitions. BBCSport is designed for interoperability with general event ontologies and supports semantic annotation of sports data for publishing, search, and analytics. The ontology enables integration of sports data across platforms, facilitating advanced queries, personalized recommendations, and automated content generation for sports journalism. BBCSport is extensible and can be adapted to a wide range of sports and event types, supporting both professional and amateur competitions. The ontology is maintained as an open resource and is compatible with other BBC and external ontologies for broader data integration. Metrics & Statistics -------------------------- @@ -134,3 +132,6 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +**Example Usage**: +Annotate a sports tournament database with BBCSport terms to specify the structure of the tournament, participating teams and athletes, event types (e.g., "final match"), and awards (e.g., "gold medal"), enabling semantic search and cross-platform sports analytics. diff --git a/docs/source/benchmarking/news_and_media/bbcstoryline.rst b/docs/source/benchmarking/news_and_media/bbcstoryline.rst index 26bc56d4..957308f2 100644 --- a/docs/source/benchmarking/news_and_media/bbcstoryline.rst +++ b/docs/source/benchmarking/news_and_media/bbcstoryline.rst @@ -25,7 +25,9 @@ BBC Storyline Ontology (BBCStoryline) ======================================================================================================== -The News Storyline Ontology is a generic model for describing and organising the stories news organisations tell. The ontology is intended to be flexible to support any given news or media publisher's approach to handling news stories. At the heart of the ontology, is the concept of Storyline. As a nuance of the English language the word 'story' has multiple meanings. In news organisations, a story can be an individual piece of content, such as an article or news report. It can also be the editorial view on events occurring in the world. +The BBC Storyline Ontology is a generic semantic framework for describing, organizing, and managing news stories and narrative content across diverse media publishing platforms. It models the core concept of "Storyline" to distinguish between individual news content pieces (articles, reports, videos) and broader editorial narratives representing events or topics in the world. The ontology captures relationships between stories, events, entities (people, places, organizations), and time periods, enabling sophisticated content organization and discovery. BBCStoryline is designed to be flexible and adaptable, supporting different news publishers' approaches to story organization while providing standardized semantic structures. The ontology facilitates semantic linking of content across media platforms and enables advanced search, recommendation, and editorial workflow systems. + +**Example Usage**: Represent a news story about a major political event using BBCStoryline terms to link individual articles (multiple reporters' perspectives), associated entities (politicians, organizations), timeline (chronological events), and related stories covering different aspects of the broader storyline. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/news_and_media/bbcwildlife.rst b/docs/source/benchmarking/news_and_media/bbcwildlife.rst index 5655cbaa..1e71fb2a 100644 --- a/docs/source/benchmarking/news_and_media/bbcwildlife.rst +++ b/docs/source/benchmarking/news_and_media/bbcwildlife.rst @@ -25,7 +25,7 @@ BBC Wildlife Ontology (BBCWildlife) ======================================================================================================== -A simple vocabulary for describing biological species and related taxa. The vocabulary defines terms for describing the names and ranking of taxa, as well as providing support for describing their habitats, conservation status, and behavioural characteristics, etc. +The BBC Wildlife Ontology is a specialized vocabulary for describing and classifying biological species, taxa, and their ecological characteristics in a machine-readable format. It provides standardized terminology for representing biological classification hierarchies including species names, taxonomic ranks (kingdom, phylum, class, order, family, genus, species), and relationships between taxa. The ontology enables semantic annotation of wildlife information including habitat descriptions, conservation status, behavioral characteristics, geographic distribution, and ecological roles. BBCWildlife supports wildlife content discovery and semantic linking across BBC media platforms and broader biodiversity databases. The ontology facilitates knowledge integration in natural history and biodiversity research by providing standardized semantic representations of organisms and their ecological contexts. **Example Usage**: Annotate a wildlife documentary or encyclopedia entry about a species (e.g., tiger, African elephant) with BBCWildlife terms for taxonomic classification, habitat (forest, savanna, wetland), conservation status (endangered, vulnerable), behavior (predatory, migratory), and geographic distribution. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/aiiso.rst b/docs/source/benchmarking/scholarly_knowledge/aiiso.rst index bd945481..19abe0c0 100644 --- a/docs/source/benchmarking/scholarly_knowledge/aiiso.rst +++ b/docs/source/benchmarking/scholarly_knowledge/aiiso.rst @@ -25,7 +25,9 @@ Academic Institution Internal Structure Ontology (AIISO) ======================================================================================================== -The Academic Institution Internal Structure Ontology (AIISO) provides classes and properties to describe the internal organizational structure of an academic institution. AIISO is designed to work in partnership with Participation (http://purl.org/vocab/participation/schema), FOAF (http://xmlns.com/foaf/0.1/) and aiiso-roles (http://purl.org/vocab/aiiso-roles/schema) to describe the roles that people play within an institution. +AIISO is a specialized ontology for describing and formalizing the internal organizational structures, hierarchies, and administrative units of academic institutions including universities, colleges, and research centers. It provides standardized vocabulary for representing academic departments, faculties, schools, research groups, and their relationships within an institution's organizational hierarchy. AIISO is designed for integration with complementary ontologies including Participation (for describing role participation), FOAF (for person information), and aiiso-roles (for person roles within institutions). The ontology enables semantic representation of academic organizational information, supporting institutional data integration, organizational mapping, and staff/student management systems. AIISO facilitates interoperability in academic information systems and institutional repositories by providing standardized semantic definitions of academic structures. + +**Example Usage**: Represent an academic institution's structure with AIISO terms for faculties (Faculty of Science, Faculty of Engineering), departments (Computer Science Department, Physics Department), research groups, and their organizational relationships and hierarchies. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/cito.rst b/docs/source/benchmarking/scholarly_knowledge/cito.rst index a569aa84..43b54b15 100644 --- a/docs/source/benchmarking/scholarly_knowledge/cito.rst +++ b/docs/source/benchmarking/scholarly_knowledge/cito.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Citation Typing Ontology (CiTO) ======================================================================================================== -The Citation Typing Ontology (CiTO) is an ontology that enables characterization of the nature or type of citations, both factually and rhetorically. +The Citation Typing Ontology (CiTO) is an ontology that enables characterization of the nature or type of citations, both factually and rhetorically. It provides a structured vocabulary for describing citation types, relationships, and contexts, supporting both theoretical and experimental research in scholarly communication. + +The ontology employs a class-based modeling approach, defining classes for different types of citations, relationships, and contexts, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. CiTO supports the integration of data from various sources, promoting interoperability and data-driven research in scholarly communication. + +Typical applications of CiTO include the development of new citation analysis methods, the optimization of citation practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, CiTO enhances collaboration and innovation in the field of scholarly communication. + +**Example Usage**: +Annotate a research paper with CiTO terms to specify citation types, relationships, and contexts, enabling semantic search and integration with scholarly communication platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/cso.rst b/docs/source/benchmarking/scholarly_knowledge/cso.rst index e2b08716..43e19200 100644 --- a/docs/source/benchmarking/scholarly_knowledge/cso.rst +++ b/docs/source/benchmarking/scholarly_knowledge/cso.rst @@ -25,7 +25,9 @@ Computer Science Ontology (CSO) ======================================================================================================== -The Computer Science Ontology (CSO) is a large-scale ontology of research areas in computer science. It provides a comprehensive vocabulary of research topics in computing, organized in a hierarchical structure. This class processes the Computer Science Ontology (CSO) with custom hooks for: - Topic-based class detection - superTopicOf relationships - contributesTo relationships +The Computer Science Ontology (CSO) is a large-scale semantic resource that provides a comprehensive vocabulary of research areas, topics, and concepts in computer science organized in a hierarchical taxonomy. It covers diverse computing domains including artificial intelligence, software engineering, networking, databases, human-computer interaction, and emerging areas, enabling precise semantic annotation of research contributions. CSO supports sophisticated relationship modeling including superTopicOf (for topic hierarchies), contributesTo (linking topics to solutions), and other domain-relevant relationships enabling knowledge discovery and research mapping. The ontology enables automated research classification, literature organization, and expertise matching by providing standardized semantic definitions of computer science research areas. CSO facilitates semantic interoperability in scholarly information systems, research management platforms, and academic recommendation systems. + +**Example Usage**: Annotate a research paper or researcher profile with CSO terms such as "Machine Learning" (main topic), "Deep Learning" (subtopic), "Natural Language Processing" (related topic), and associated research methods and applications to enable semantic discovery of related research. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/datacite.rst b/docs/source/benchmarking/scholarly_knowledge/datacite.rst index 86d96ac3..def878c1 100644 --- a/docs/source/benchmarking/scholarly_knowledge/datacite.rst +++ b/docs/source/benchmarking/scholarly_knowledge/datacite.rst @@ -25,7 +25,9 @@ DataCite Ontology (DataCite) ======================================================================================================== -The DataCite Ontology (DataCite) is an ontology that enables the metadata properties of the DataCite Metadata Schema Specification (i.e., a list of metadata properties for the accurate and consistent identification of a resource for citation and retrieval purposes) to be described in RDF. +The DataCite Ontology is an RDF-based representation of the DataCite Metadata Schema, providing standardized vocabulary and semantic structure for describing research data and digital objects with comprehensive citation and identification metadata. It enables formal representation of essential dataset properties including creators, titles, publication dates, contributors, funding information, and relationships to other scholarly resources. DataCite provides machine-readable definitions of metadata properties for accurate and consistent identification, retrieval, and citation of diverse digital resources including datasets, software, and research outputs. The ontology supports FAIR data principles by enabling standardized, interoperable representation of dataset metadata in linked data formats (RDF, JSON-LD). DataCite facilitates data discovery, citation tracking, and research impact assessment by providing standardized semantic metadata structures. + +**Example Usage**: Represent a published research dataset with DataCite ontology terms including persistent identifier (DOI), creators and contributors (with ORCID), publication date, subject areas, funding information, and related publications to enable proper citation and discovery across research repositories. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/dcat.rst b/docs/source/benchmarking/scholarly_knowledge/dcat.rst index 8318b910..566fb231 100644 --- a/docs/source/benchmarking/scholarly_knowledge/dcat.rst +++ b/docs/source/benchmarking/scholarly_knowledge/dcat.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Data Catalog Vocabulary (DCAT) ======================================================================================================== -Data Catalog Vocabulary (DCAT) is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web. This document defines the schema and provides examples for its use. DCAT enables a publisher to describe datasets and data services in a catalog using a standard model and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs. This can increase the discoverability of datasets and data services. It also makes it possible to have a decentralized approach to publishing data catalogs and makes federated search for datasets across catalogs in multiple sites possible using the same query mechanism and structure. Aggregated DCAT metadata can serve as a manifest file as part of the digital preservation process. +The Data Catalog Vocabulary (DCAT) is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web. It provides a structured vocabulary for describing datasets, data services, and catalogs, supporting both theoretical and experimental research in data management. + +The ontology employs a class-based modeling approach, defining classes for different types of datasets, data services, and catalogs, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. DCAT supports the integration of data from various sources, promoting interoperability and data-driven research in data management. + +Typical applications of DCAT include the development of new data cataloging methods, the optimization of data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, DCAT enhances collaboration and innovation in the field of data management. + +**Example Usage**: +Annotate a data catalog with DCAT terms to specify dataset types, data services, and catalog structures, enabling semantic search and integration with data management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/duo.rst b/docs/source/benchmarking/scholarly_knowledge/duo.rst index f1a7fb8f..d625ae80 100644 --- a/docs/source/benchmarking/scholarly_knowledge/duo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/duo.rst @@ -25,7 +25,9 @@ Data Use Ontology (DUO) ======================================================================================================== -DUO is an ontology which represent data use conditions. +The Data Use Ontology (DUO) is a comprehensive vocabulary for formally representing and managing data use restrictions, permissions, and conditions that govern access to and usage of biomedical and life sciences research data. It provides standardized definitions of data use constraints including disease-specific research restrictions, commercial use prohibitions, publication acknowledgment requirements, and ethical/policy-based limitations. DUO enables data stewards and repositories to precisely specify how datasets can be used, facilitating automated enforcement of data use agreements and supporting secondary data reuse in compliant ways. The ontology bridges the gap between legal/ethical restrictions and technical implementations by providing machine-readable representations of complex data use policies. DUO supports FAIR data principles by enabling discoverable, interoperable, and reusable data through clear expression of usage conditions. + +**Example Usage**: Annotate a biomedical research dataset with DUO terms to specify permissions (medical research only), restrictions (no commercial use), and requirements (publication acknowledgment, return of results to participants) enabling automated compliance checking and appropriate data sharing decisions. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/eurio.rst b/docs/source/benchmarking/scholarly_knowledge/eurio.rst index 4b154ebc..099090c9 100644 --- a/docs/source/benchmarking/scholarly_knowledge/eurio.rst +++ b/docs/source/benchmarking/scholarly_knowledge/eurio.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ EUropean Research Information Ontology (EURIO) ======================================================================================================== -EURIO (EUropean Research Information Ontology) conceptualises, formally encodes and makes available in an open, structured and machine-readable format data about resarch projects funded by the EU's framework programmes for research and innovation. +The EUropean Research Information Ontology (EURIO) conceptualizes, formally encodes, and makes available in an open, structured, and machine-readable format data about research projects funded by the EU's framework programmes for research and innovation. It provides a structured vocabulary for representing research projects, funding information, and related data, supporting both theoretical and experimental research in research information management. + +The ontology employs a class-based modeling approach, defining classes for different types of research projects, funding information, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. EURIO supports the integration of data from various sources, promoting interoperability and data-driven research in research information management. + +Typical applications of EURIO include the development of new research information management methods, the optimization of research project management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, EURIO enhances collaboration and innovation in the field of research information management. + +**Example Usage**: +Annotate a research project with EURIO terms to specify project types, funding information, and related data, enabling semantic search and integration with research information management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/expo.rst b/docs/source/benchmarking/scholarly_knowledge/expo.rst index 5663841b..85683c5e 100644 --- a/docs/source/benchmarking/scholarly_knowledge/expo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/expo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Ontology of Scientific Experiments (EXPO) ======================================================================================================== -Formalise generic knowledge about scientific experimental design, methodology, and results representation. +The Ontology of Scientific Experiments (EXPO) formalizes generic knowledge about scientific experimental design, methodology, and results representation. It provides a structured vocabulary for representing scientific experiments, methodologies, and results, supporting both theoretical and experimental research in scientific experimentation. + +The ontology employs a class-based modeling approach, defining classes for different types of experiments, methodologies, and results, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. EXPO supports the integration of data from various sources, promoting interoperability and data-driven research in scientific experimentation. + +Typical applications of EXPO include the development of new experimental design methods, the optimization of experimental methodologies, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, EXPO enhances collaboration and innovation in the field of scientific experimentation. + +**Example Usage**: +Annotate a scientific experiment with EXPO terms to specify experiment types, methodologies, and results, enabling semantic search and integration with scientific experimentation platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/framester.rst b/docs/source/benchmarking/scholarly_knowledge/framester.rst index 150b2018..735e9819 100644 --- a/docs/source/benchmarking/scholarly_knowledge/framester.rst +++ b/docs/source/benchmarking/scholarly_knowledge/framester.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Framester Ontology (Framester) ======================================================================================================== -Framester is a a frame-based ontological resource acting as a hub between linguistic resources such as FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, and leveraging this wealth of links to create an interoperable predicate space formalized according to frame semantics and semiotics. Framester uses WordNet and FrameNet at its core, expands it to other resources transitively, and represents them in a formal version of frame semantics. +Framester is a frame-based ontological resource acting as a hub between linguistic resources such as FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, and leveraging this wealth of links to create an interoperable predicate space formalized according to frame semantics and semiotics. Framester uses WordNet and FrameNet at its core, expands it to other resources transitively, and represents them in a formal version of frame semantics. It provides a structured vocabulary for representing linguistic resources, supporting both theoretical and experimental research in linguistics. + +The ontology employs a class-based modeling approach, defining classes for different types of linguistic resources, frames, and relationships, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. Framester supports the integration of data from various sources, promoting interoperability and data-driven research in linguistics. + +Typical applications of Framester include the development of new linguistic resource integration methods, the optimization of linguistic data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, Framester enhances collaboration and innovation in the field of linguistics. + +**Example Usage**: +Annotate a linguistic dataset with Framester terms to specify linguistic resources, frames, and relationships, enabling semantic search and integration with linguistic information management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/frapo.rst b/docs/source/benchmarking/scholarly_knowledge/frapo.rst index 99b72a81..86ed9473 100644 --- a/docs/source/benchmarking/scholarly_knowledge/frapo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/frapo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Funding, Research Administration and Projects Ontology (FRAPO) ======================================================================================================== -The Funding, Research Administration and Projects Ontology (FRAPO) is an ontology for describing the administrative information of research projects, e.g., grant applications, funding bodies, project partners, etc. +The Funding, Research Administration and Projects Ontology (FRAPO) is an ontology for describing the administrative information of research projects, e.g., grant applications, funding bodies, project partners, etc. It provides a structured vocabulary for representing research administration, funding, and project information, supporting both theoretical and experimental research in research administration. + +The ontology employs a class-based modeling approach, defining classes for different types of research administration, funding, and project information, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. FRAPO supports the integration of data from various sources, promoting interoperability and data-driven research in research administration. + +Typical applications of FRAPO include the development of new research administration methods, the optimization of research project management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, FRAPO enhances collaboration and innovation in the field of research administration. + +**Example Usage**: +Annotate a research project with FRAPO terms to specify administrative information, funding details, and project partners, enabling semantic search and integration with research administration platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/frbroo.rst b/docs/source/benchmarking/scholarly_knowledge/frbroo.rst index e7b6e7fd..f598fb45 100644 --- a/docs/source/benchmarking/scholarly_knowledge/frbroo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/frbroo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Functional Requirements for Bibliographic Records - object-oriented (FRBRoo) ======================================================================================================== -The FRBRoo (Functional Requirements for Bibliographic Records - object-oriented) initiative is a joint effort of the CIDOC Conceptual Reference Model and Functional Requirements for Bibliographic Records international working groups to establish a formal ontology intended to capture and represent the underlying semantics of bibliographic information and to facilitate the integration, mediation, and interchange of bibliographic and museum information. +The FRBRoo (Functional Requirements for Bibliographic Records - object-oriented) initiative is a joint effort of the CIDOC Conceptual Reference Model and Functional Requirements for Bibliographic Records international working groups to establish a formal ontology intended to capture and represent the underlying semantics of bibliographic information and to facilitate the integration, mediation, and interchange of bibliographic and museum information. It provides a structured vocabulary for representing bibliographic records, concepts, and relationships, supporting both theoretical and experimental research in bibliographic information management. + +The ontology employs a class-based modeling approach, defining classes for different types of bibliographic records, concepts, and relationships, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. FRBRoo supports the integration of data from various sources, promoting interoperability and data-driven research in bibliographic information management. + +Typical applications of FRBRoo include the development of new bibliographic information management methods, the optimization of bibliographic record management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, FRBRoo enhances collaboration and innovation in the field of bibliographic information management. + +**Example Usage**: +Annotate a bibliographic record with FRBRoo terms to specify record types, concepts, and relationships, enabling semantic search and integration with bibliographic information management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/lexinfo.rst b/docs/source/benchmarking/scholarly_knowledge/lexinfo.rst index e067a234..6943222a 100644 --- a/docs/source/benchmarking/scholarly_knowledge/lexinfo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/lexinfo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ LexInfo (LexInfo) ======================================================================================================== -LexInfo allows us to associate linguistic information to elements in an ontology with respect to any level of linguistic description and expressivity. LexInfo has been implemented as an OWL ontology and is available together with an API. +LexInfo allows us to associate linguistic information to elements in an ontology with respect to any level of linguistic description and expressivity. It provides a structured vocabulary for representing linguistic information, supporting both theoretical and experimental research in linguistics. + +The ontology employs a class-based modeling approach, defining classes for different types of linguistic information, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. LexInfo supports the integration of data from various sources, promoting interoperability and data-driven research in linguistics. + +Typical applications of LexInfo include the development of new linguistic information management methods, the optimization of linguistic data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, LexInfo enhances collaboration and innovation in the field of linguistics. + +**Example Usage**: +Annotate an ontology with LexInfo terms to specify linguistic information, enabling semantic search and integration with linguistic information management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/metadata4ing.rst b/docs/source/benchmarking/scholarly_knowledge/metadata4ing.rst index 9c5ca096..af359c6a 100644 --- a/docs/source/benchmarking/scholarly_knowledge/metadata4ing.rst +++ b/docs/source/benchmarking/scholarly_knowledge/metadata4ing.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Metadata for Intelligent Engineering (Metadata4Ing) ======================================================================================================== -The ontology Metadata4Ing provides a framework for the semantic description of research data and of the whole data generation process, embracing the object of investigation, all sample and data manipulation methods and tools, the data files themselves, and the roles of persons and institutions. The structure and application of the ontology are based on the principles of modularity and inheritance. +The ontology Metadata4Ing provides a framework for the semantic description of research data and of the whole data generation process, embracing the object of investigation, all sample and data manipulation methods and tools, the data files themselves, and the roles of persons and institutions. It provides a structured vocabulary for representing research data, processes, and roles, supporting both theoretical and experimental research in intelligent engineering. + +The ontology employs a class-based modeling approach, defining classes for different types of research data, processes, and roles, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. Metadata4Ing supports the integration of data from various sources, promoting interoperability and data-driven research in intelligent engineering. + +Typical applications of Metadata4Ing include the development of new research data management methods, the optimization of research data generation processes, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, Metadata4Ing enhances collaboration and innovation in the field of intelligent engineering. + +**Example Usage**: +Annotate a research dataset with Metadata4Ing terms to specify data types, processes, and roles, enabling semantic search and integration with research data management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/nfdicore.rst b/docs/source/benchmarking/scholarly_knowledge/nfdicore.rst index 78ee1409..f975ace1 100644 --- a/docs/source/benchmarking/scholarly_knowledge/nfdicore.rst +++ b/docs/source/benchmarking/scholarly_knowledge/nfdicore.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ National Research Data Infrastructure Ontology (NFDIcore) ======================================================================================================== -The National Research Data Infrastructure (NFDI) initiative has led to the formation of various consortia, each focused on developing a research data infrastructure tailored to its specific domain. To ensure interoperability across these consortia, the NFDIcore ontology has been developed as a mid-level ontology for representing metadata related to NFDI resources, including individuals, organizations, projects, data portals, and more. +The National Research Data Infrastructure (NFDI) initiative has led to the formation of various consortia, each focused on developing a research data infrastructure tailored to its specific domain. To ensure interoperability across these consortia, the NFDIcore ontology has been developed as a mid-level ontology for representing metadata related to NFDI resources, including individuals, organizations, projects, data portals, and more. It provides a structured vocabulary for representing research data infrastructure, supporting both theoretical and experimental research in research data management. + +The ontology employs a class-based modeling approach, defining classes for different types of research data infrastructure, metadata, and related entities, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. NFDIcore supports the integration of data from various sources, promoting interoperability and data-driven research in research data management. + +Typical applications of NFDIcore include the development of new research data infrastructure methods, the optimization of research data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, NFDIcore enhances collaboration and innovation in the field of research data management. + +**Example Usage**: +Annotate a research data infrastructure project with NFDIcore terms to specify infrastructure types, metadata, and related entities, enabling semantic search and integration with research data management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/oboe.rst b/docs/source/benchmarking/scholarly_knowledge/oboe.rst index 59ba63ef..e63e15e0 100644 --- a/docs/source/benchmarking/scholarly_knowledge/oboe.rst +++ b/docs/source/benchmarking/scholarly_knowledge/oboe.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Extensible Observation Ontology (OBOE) ======================================================================================================== -The Extensible Observation Ontology (OBOE) is a formal ontology for capturing the semantics of scientific observation and measurement. The ontology supports researchers to add detailed semantic annotations to scientific data, thereby clarifying the inherent meaning of scientific observations. +The Extensible Observation Ontology (OBOE) is a formal ontology for capturing the semantics of scientific observation and measurement. The ontology supports researchers to add detailed semantic annotations to scientific data, thereby clarifying the inherent meaning of scientific observations. It provides a structured vocabulary for representing scientific observations, measurements, and related data, supporting both theoretical and experimental research in scientific observation. + +The ontology employs a class-based modeling approach, defining classes for different types of observations, measurements, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OBOE supports the integration of data from various sources, promoting interoperability and data-driven research in scientific observation. + +Typical applications of OBOE include the development of new observation and measurement methods, the optimization of scientific observation practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OBOE enhances collaboration and innovation in the field of scientific observation. + +**Example Usage**: +Annotate a scientific observation dataset with OBOE terms to specify observation types, measurements, and related data, enabling semantic search and integration with scientific observation platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/opmw.rst b/docs/source/benchmarking/scholarly_knowledge/opmw.rst index f13c26bd..2eb6f253 100644 --- a/docs/source/benchmarking/scholarly_knowledge/opmw.rst +++ b/docs/source/benchmarking/scholarly_knowledge/opmw.rst @@ -25,7 +25,9 @@ Open Provenance Model for Workflows (OPMW) ======================================================================================================== -The Open Provenance Model for Workflows (OPMW) is an ontology for describing workflow traces and their templates based on the Open Provenance Model. It has been designed as a profile for OPM, extending and reusing OPM's core ontologies OPMV (OPM-Vocabulary) and OPMO (OPM-Ontology). +OPMW is a specialized ontology for comprehensive semantic description of computational workflow traces, executions, and their templates based on the Open Provenance Model (OPM) framework. It provides vocabulary for describing workflow components including processes, inputs/outputs, agents, and execution steps, along with provenance information tracking data flow and transformations. OPMW is designed as an OPM profile, extending and reusing OPM's core ontologies OPMV (OPM-Vocabulary) and OPMO (OPM-Ontology) to provide workflow-specific semantics. The ontology enables systematic documentation and sharing of scientific workflows, supporting reproducibility and reuse in data-intensive research disciplines. OPMW facilitates workflow management systems and scientific computing platforms by providing standardized provenance representations. + +**Example Usage**: Annotate a bioinformatics workflow with OPMW terms to describe workflow steps (sequence alignment, variant calling), inputs (raw sequencing data), outputs (VCF files), and provenance tracking which software tools were used, parameter settings, and intermediate data transformations. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/pplan.rst b/docs/source/benchmarking/scholarly_knowledge/pplan.rst index d1a81a41..fa49fe6f 100644 --- a/docs/source/benchmarking/scholarly_knowledge/pplan.rst +++ b/docs/source/benchmarking/scholarly_knowledge/pplan.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Ontology for Provenance and Plans (P-Plan) ======================================================================================================== -The Ontology for Provenance and Plans (P-Plan) is an extension of the PROV-O ontology [PROV-O] created to represent the plans that guided the execution of scientific processes. P-Plan describes how the plans are composed and their correspondence to provenance records that describe the execution itself. +The Ontology for Provenance and Plans (P-Plan) is an extension of the PROV-O ontology [PROV-O] created to represent the plans that guided the execution of scientific processes. P-Plan describes how the plans are composed and their correspondence to provenance records that describe the execution itself. It provides a structured vocabulary for representing plans, provenance, and related data, supporting both theoretical and experimental research in provenance and planning. + +The ontology employs a class-based modeling approach, defining classes for different types of plans, provenance, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. P-Plan supports the integration of data from various sources, promoting interoperability and data-driven research in provenance and planning. + +Typical applications of P-Plan include the development of new provenance and planning methods, the optimization of scientific process execution, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, P-Plan enhances collaboration and innovation in the field of provenance and planning. + +**Example Usage**: +Annotate a scientific process with P-Plan terms to specify plans, provenance, and related data, enabling semantic search and integration with provenance and planning platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/premon.rst b/docs/source/benchmarking/scholarly_knowledge/premon.rst index 3362a671..c75fe656 100644 --- a/docs/source/benchmarking/scholarly_knowledge/premon.rst +++ b/docs/source/benchmarking/scholarly_knowledge/premon.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Pre-Modern Ontology (PreMOn) ======================================================================================================== -The PreMOn Ontology is an extension of lemon (W3C Ontology Lexicon Community Group, 2015) for representing predicate models and their mappings. The Core Module of the PreMOn Ontology defines the main abstractions for modelling semantic classes with their semantic roles, mappings between different predicate models, and annotations. +The Pre-Modern Ontology (PreMOn) is an extension of lemon (W3C Ontology Lexicon Community Group, 2015) for representing predicate models and their mappings. The Core Module of the PreMOn Ontology defines the main abstractions for modelling semantic classes with their semantic roles, mappings between different predicate models, and annotations. It provides a structured vocabulary for representing predicate models, supporting both theoretical and experimental research in linguistics. + +The ontology employs a class-based modeling approach, defining classes for different types of predicate models, semantic roles, and annotations, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. PreMOn supports the integration of data from various sources, promoting interoperability and data-driven research in linguistics. + +Typical applications of PreMOn include the development of new predicate model representation methods, the optimization of linguistic data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, PreMOn enhances collaboration and innovation in the field of linguistics. + +**Example Usage**: +Annotate a linguistic dataset with PreMOn terms to specify predicate models, semantic roles, and annotations, enabling semantic search and integration with linguistic information management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/sepio.rst b/docs/source/benchmarking/scholarly_knowledge/sepio.rst index 941c1709..ead28d94 100644 --- a/docs/source/benchmarking/scholarly_knowledge/sepio.rst +++ b/docs/source/benchmarking/scholarly_knowledge/sepio.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Scientific Evidence and Provenance Information Ontology (SEPIO) ======================================================================================================== -The SEPIO ontology is in its early stages of development, undergoing iterative refinement as new requirements emerge and alignment with existing standards is explored. The SEPIO core file imports two files which can be resolved at the URLs below: IAO ontology-metadata import: https://raw.githubusercontent.com/monarch-initiative/SEPIO-ontology/master/src/ontology/imports/ontology-metadata.owl bfo mireot: https://raw.githubusercontent.com/monarch-initiative/SEPIO-ontology/master/src/ontology/mireots/bfo-mireot.owl +The Scientific Evidence and Provenance Information Ontology (SEPIO) is in its early stages of development, undergoing iterative refinement as new requirements emerge and alignment with existing standards is explored. SEPIO provides a structured vocabulary for representing scientific evidence, provenance, and related data, supporting both theoretical and experimental research in scientific evidence and provenance. + +The ontology employs a class-based modeling approach, defining classes for different types of scientific evidence, provenance, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SEPIO supports the integration of data from various sources, promoting interoperability and data-driven research in scientific evidence and provenance. + +Typical applications of SEPIO include the development of new scientific evidence and provenance representation methods, the optimization of scientific evidence management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SEPIO enhances collaboration and innovation in the field of scientific evidence and provenance. + +**Example Usage**: +Annotate a scientific study with SEPIO terms to specify scientific evidence, provenance, and related data, enabling semantic search and integration with scientific evidence and provenance platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/spdocument.rst b/docs/source/benchmarking/scholarly_knowledge/spdocument.rst index b7f2b6ce..ab9399a4 100644 --- a/docs/source/benchmarking/scholarly_knowledge/spdocument.rst +++ b/docs/source/benchmarking/scholarly_knowledge/spdocument.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ SMART Protocols Ontology: Document Module (SP-Document) ======================================================================================================== -SMART Protocols Ontology: Document Module is an ontology designed to represent metadata used to report an experimental protocol. +SMART Protocols Ontology: Document Module is an ontology designed to represent metadata used to report an experimental protocol. It provides a structured vocabulary for representing experimental protocols, metadata, and related data, supporting both theoretical and experimental research in experimental protocol documentation. + +The ontology employs a class-based modeling approach, defining classes for different types of experimental protocols, metadata, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SP-Document supports the integration of data from various sources, promoting interoperability and data-driven research in experimental protocol documentation. + +Typical applications of SP-Document include the development of new experimental protocol documentation methods, the optimization of experimental protocol management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SP-Document enhances collaboration and innovation in the field of experimental protocol documentation. + +**Example Usage**: +Annotate an experimental protocol with SP-Document terms to specify protocol types, metadata, and related data, enabling semantic search and integration with experimental protocol documentation platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/spworkflow.rst b/docs/source/benchmarking/scholarly_knowledge/spworkflow.rst index 374d5a69..03eb805c 100644 --- a/docs/source/benchmarking/scholarly_knowledge/spworkflow.rst +++ b/docs/source/benchmarking/scholarly_knowledge/spworkflow.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ SMART Protocols Ontology: Workflow Module (SP-Workflow) ======================================================================================================== -SP-Workflow module represents: i) the executable elements of a protocol; ii) the experimental actions and material entities that participates in instructions (sample/specimen, organisms, reagents, instruments); and iii) the order of execution of the instructions. +SP-Workflow module represents: i) the executable elements of a protocol; ii) the experimental actions and material entities that participate in instructions (sample/specimen, organisms, reagents, instruments); and iii) the order of execution of the instructions. It provides a structured vocabulary for representing workflows, experimental actions, and related data, supporting both theoretical and experimental research in workflow management. + +The ontology employs a class-based modeling approach, defining classes for different types of workflows, experimental actions, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SP-Workflow supports the integration of data from various sources, promoting interoperability and data-driven research in workflow management. + +Typical applications of SP-Workflow include the development of new workflow management methods, the optimization of experimental workflows, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SP-Workflow enhances collaboration and innovation in the field of workflow management. + +**Example Usage**: +Annotate an experimental workflow with SP-Workflow terms to specify workflow steps, experimental actions, and related data, enabling semantic search and integration with workflow management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/swo.rst b/docs/source/benchmarking/scholarly_knowledge/swo.rst index 7c90cd1c..af35f5d8 100644 --- a/docs/source/benchmarking/scholarly_knowledge/swo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/swo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Software Ontology (SWO) ======================================================================================================== -The Software Ontology (SWO) is a resource for describing software tools, their types, tasks, versions, provenance and associated data. It contains detailed information on licensing and formats as well as software applications themselves, mainly (but not limited) to the bioinformatics community. +The Software Ontology (SWO) is a resource for describing software tools, their types, tasks, versions, provenance, and associated data. It contains detailed information on licensing and formats as well as software applications themselves, mainly (but not limited) to the bioinformatics community. It provides a structured vocabulary for representing software tools, supporting both theoretical and experimental research in software management. + +The ontology employs a class-based modeling approach, defining classes for different types of software tools, tasks, and associated data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SWO supports the integration of data from various sources, promoting interoperability and data-driven research in software management. + +Typical applications of SWO include the development of new software management methods, the optimization of software tool usage, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SWO enhances collaboration and innovation in the field of software management. + +**Example Usage**: +Annotate a software tool with SWO terms to specify tool types, tasks, and associated data, enabling semantic search and integration with software management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/tribain.rst b/docs/source/benchmarking/scholarly_knowledge/tribain.rst index ee43b8f1..f59d0a7c 100644 --- a/docs/source/benchmarking/scholarly_knowledge/tribain.rst +++ b/docs/source/benchmarking/scholarly_knowledge/tribain.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Tribology and Artificial Intelligence Ontology (TribAIn) ======================================================================================================== -TribAIn is an ontology for the description of tribological experiments and their results. It is designed to be used in the context of the TribAIn project, which aims to develop a knowledge-based system for the design of tribological systems. +TribAIn is an ontology for the description of tribological experiments and their results. It is designed to be used in the context of the TribAIn project, which aims to develop a knowledge-based system for the design of tribological systems. It provides a structured vocabulary for representing tribological experiments, results, and related data, supporting both theoretical and experimental research in tribology. + +The ontology employs a class-based modeling approach, defining classes for different types of tribological experiments, results, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. TribAIn supports the integration of data from various sources, promoting interoperability and data-driven research in tribology. + +Typical applications of TribAIn include the development of new tribological experiment methods, the optimization of tribological system design, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, TribAIn enhances collaboration and innovation in the field of tribology. + +**Example Usage**: +Annotate a tribological experiment with TribAIn terms to specify experiment types, results, and related data, enabling semantic search and integration with tribology research platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/voaf.rst b/docs/source/benchmarking/scholarly_knowledge/voaf.rst index 5e4786f5..106bdd4d 100644 --- a/docs/source/benchmarking/scholarly_knowledge/voaf.rst +++ b/docs/source/benchmarking/scholarly_knowledge/voaf.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Vocabulary of a Friend (VOAF) ======================================================================================================== -The Vocabulary of a Friend (VOAF) is a vocabulary specification providing elements allowing the description of vocabularies (RDFS vocabularies or OWL ontologies). It is based on Dublin Core and VOID. +The Vocabulary of a Friend (VOAF) is a vocabulary specification providing elements allowing the description of vocabularies (RDFS vocabularies or OWL ontologies). It is based on Dublin Core and VOID. VOAF provides a structured vocabulary for representing vocabularies, supporting both theoretical and experimental research in vocabulary management. + +The ontology employs a class-based modeling approach, defining classes for different types of vocabularies, elements, and relationships, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. VOAF supports the integration of data from various sources, promoting interoperability and data-driven research in vocabulary management. + +Typical applications of VOAF include the development of new vocabulary management methods, the optimization of vocabulary usage, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, VOAF enhances collaboration and innovation in the field of vocabulary management. + +**Example Usage**: +Annotate a vocabulary with VOAF terms to specify vocabulary types, elements, and relationships, enabling semantic search and integration with vocabulary management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/scholarly_knowledge/wild.rst b/docs/source/benchmarking/scholarly_knowledge/wild.rst index 626f29ba..24f0b3bd 100644 --- a/docs/source/benchmarking/scholarly_knowledge/wild.rst +++ b/docs/source/benchmarking/scholarly_knowledge/wild.rst @@ -25,7 +25,9 @@ Workflows in Linked Data (WiLD) ======================================================================================================== -Ontology to describe Workflows in Linked Data. +WiLD is a specialized ontology for formal semantic description and representation of computational workflows and scientific data processing pipelines using linked data and semantic web technologies. It provides standardized vocabulary for describing workflow components including workflow steps, data flows, tool/service invocations, and their interdependencies in a machine-readable format. WiLD enables interoperable workflow specification and sharing across diverse scientific computing platforms and workflow management systems by providing uniform semantic representations. The ontology captures essential workflow metadata including execution parameters, input/output specifications, and constraints, supporting workflow automation and optimization. WiLD facilitates reproducible science by enabling systematic documentation of computational methods and data processing procedures in linked data formats. + +**Example Usage**: Represent a bioinformatics data processing pipeline using WiLD terms to describe sequential workflow steps (quality control, alignment, variant calling), input datasets, tool invocations (with parameters), and output data products to enable workflow reproducibility and reuse. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/social_sciences/as2.rst b/docs/source/benchmarking/social_sciences/as2.rst index 4b711ff1..78d0c399 100644 --- a/docs/source/benchmarking/social_sciences/as2.rst +++ b/docs/source/benchmarking/social_sciences/as2.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Activity Streams 2.0 Ontology (AS2) ======================================================================================================== -The Activity Streams 2.0 ontology is a vocabulary for describing social activities and actions. It is based on the Activity Streams 2.0 specification and provides a set of classes and properties for describing activities on the web. +The Activity Streams 2.0 Ontology (AS2) is a W3C standard vocabulary for describing social activities, actions, and interactions on the web. It provides a set of classes and properties for modeling activities such as posting, liking, sharing, following, and commenting, as well as the actors, objects, and targets involved. AS2 enables interoperability between social networking platforms, federated social web applications, and activity tracking systems by providing a common semantic framework. The ontology supports extensibility, allowing developers to define custom activity types and properties for domain-specific use cases. AS2 is widely used in decentralized social networks, content syndication, and social analytics, enabling rich representation and exchange of activity data. By standardizing the description of social actions, AS2 facilitates data integration, aggregation, and analysis across diverse social platforms. + +**Example Usage**: +Annotate a social networking application with AS2 terms to describe user activities such as posting a status update, liking a photo, or following another user, enabling interoperability with other platforms and activity streams consumers. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/social_sciences/bio.rst b/docs/source/benchmarking/social_sciences/bio.rst index 30bb2507..539b07ce 100644 --- a/docs/source/benchmarking/social_sciences/bio.rst +++ b/docs/source/benchmarking/social_sciences/bio.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ BIO: A vocabulary for biographical information (BIO) ======================================================================================================== -The BIO vocabulary contains terms useful for finding out more about people and their backgrounds and has some cross-over into genealogical information. The approach taken is to describe a person's life as a series of interconnected key events, around which other information can be woven. This vocabulary defines the event framework and supplies a set of core event types that cover many use cases, but it is expected that it will be extended in other vocabularies to suit their needs. The intention of this vocabulary is to describe biographical events of people and this intention carries through to the definitions of the properties and classes which are person-centric rather than neutral. For example the Employment event puts the person being employed as the principal agent in the event rather than the employer. +The BIO vocabulary is a domain ontology for describing biographical information about people, their backgrounds, and genealogical relationships. It models a person's life as a series of interconnected key events (birth, marriage, employment, death, etc.), providing a framework for weaving together biographical and genealogical data. BIO defines an event-centric approach, supplying a set of core event types and properties that are person-centric, making it suitable for representing life histories, family trees, and career trajectories. The ontology is designed for extensibility, allowing other vocabularies to build upon its event framework for specialized use cases. BIO is widely used in digital humanities, genealogy platforms, and social history research to enable semantic annotation, data integration, and advanced queries on biographical datasets. By providing a standardized vocabulary, BIO facilitates interoperability and knowledge sharing across biographical and genealogical information systems. + +**Example Usage**: +Annotate a genealogy database with BIO terms to describe a person's birth, marriage, and employment events, linking them to dates, places, and related individuals for semantic search and family history visualization. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/social_sciences/contact.rst b/docs/source/benchmarking/social_sciences/contact.rst index 622846ed..c8ae362d 100644 --- a/docs/source/benchmarking/social_sciences/contact.rst +++ b/docs/source/benchmarking/social_sciences/contact.rst @@ -25,7 +25,9 @@ Contact Ontology (Contact) ======================================================================================================== -Ontology to capture concepts related to contact information (addresses, phone numbers). Reuses the iContact Ontology developed by the Enterprise Integration Lab in Toronto. The iContact ontology is extended to introduce a specialized definition of Hours of Operation, defined as a subclass of both the iContact definition of hours of operation, and a subclass of the Recurring Event class defined in the iCity Recurring Event ontology. The Contact ontology also extends the definition of address to include an associated location. +The Contact Ontology is a specialized vocabulary for representing and managing contact information and related communication metadata such as addresses, phone numbers, email addresses, and physical locations. It reuses and extends the iContact Ontology developed by the Enterprise Integration Lab, incorporating specialized definitions while maintaining backward compatibility. The ontology extends the iContact model by introducing Hours of Operation as a specialized concept defined as both a subclass of iContact hours of operation and the Recurring Event class from the iCity Recurring Event ontology. Contact Ontology enriches address representation by associating address data with spatial location information, enabling geocoding and location-aware applications. The ontology supports business and organizational applications including customer relationship management (CRM), location-based services, and facility management systems. + +**Example Usage**: Represent a business location with Contact terms including street address, postal code, phone number, email, hours of operation (business hours), and associated geographic coordinates for location mapping and service discovery. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/social_sciences/foaf.rst b/docs/source/benchmarking/social_sciences/foaf.rst index 17bb988b..b9e9c701 100644 --- a/docs/source/benchmarking/social_sciences/foaf.rst +++ b/docs/source/benchmarking/social_sciences/foaf.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Friend of a Friend (FOAF) ======================================================================================================== -FOAF is a project devoted to linking people and information using the Web. Regardless of whether information is in people's heads, in physical or digital documents, or in the form of factual data, it can be linked. +FOAF (Friend of a Friend) is a widely adopted ontology for describing people, their relationships, and the information that links them on the web. It provides a standardized vocabulary for representing personal profiles, social connections, organizations, projects, and online accounts. FOAF enables the creation of machine-readable social networks, supporting interoperability between social platforms, personal websites, and linked data applications. The ontology is designed for extensibility, allowing integration with other vocabularies and domain-specific ontologies. FOAF is used in digital identity management, social media analytics, and knowledge graph construction to enable semantic search, data integration, and discovery of social connections. By providing a common framework, FOAF facilitates the linking of people and information across the decentralized web. + +**Example Usage**: +Annotate a personal website or social network profile with FOAF terms to describe a person's name, email, homepage, friends, and memberships in organizations, enabling semantic search and cross-platform social data integration. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/social_sciences/sioc.rst b/docs/source/benchmarking/social_sciences/sioc.rst index a49b427d..8ef9a16c 100644 --- a/docs/source/benchmarking/social_sciences/sioc.rst +++ b/docs/source/benchmarking/social_sciences/sioc.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Semantically-Interlinked Online Communities (SIOC) ======================================================================================================== -The SIOC (Semantically-Interlinked Online Communities) Ontology is an ontology for describing the information in online communities. This includes sites that support online discussions, blogging, file sharing, photo sharing, social networking, etc. +The SIOC (Semantically-Interlinked Online Communities) Ontology is a widely used ontology for describing the information and structure of online communities. It provides a standardized vocabulary for representing discussion forums, blogs, wikis, social networks, and other collaborative platforms. SIOC enables the modeling of users, posts, threads, topics, and relationships between community members and content. By providing a common framework, SIOC facilitates interoperability between social platforms, supports data integration, and enables advanced queries and analytics on social data. The ontology is used in social media mining, digital humanities, and knowledge graph construction to link and analyze user-generated content across platforms. SIOC is actively maintained and extended to support emerging social web technologies and applications. + +**Example Usage**: +Annotate a forum or blog platform with SIOC terms to describe users, posts, threads, and relationships, enabling semantic search and cross-platform analysis of online community interactions. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/units_and_measurements/om.rst b/docs/source/benchmarking/units_and_measurements/om.rst index b8d720b1..c2ba1db1 100644 --- a/docs/source/benchmarking/units_and_measurements/om.rst +++ b/docs/source/benchmarking/units_and_measurements/om.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,14 @@ Ontology of Units of Measure (OM) ======================================================================================================== -The Ontology of units of Measure (OM) models concepts and relations important to scientific research. It has a strong focus on units, quantities, measurements, and dimensions. It includes, for instance, common units such as the SI units metre and kilogram, but also units from other systems of units such as the mile or nautical mile. For many application areas it includes more specific units and quantities, such as the unit of the Hubble constant or the quantity vaselife. The following application areas are supported by OM: Geometry; Mechanics; Thermodynamics; Electromagnetism; Fluid mechanics; Chemical physics; Photometry; Radiometry and Radiobiology; Nuclear physics; Astronomy and Astrophysics; Cosmology; Earth science; Meteorology; Material science; Microbiology; Economics; Information technology and Typography. +The Ontology of units of Measure (OM) models concepts and relations important to scientific research. It has a strong focus on units, quantities, measurements, and dimensions. It includes, for instance, common units such as the SI units metre and kilogram, but also units from other systems of units such as the mile or nautical mile. For many application areas, it includes more specific units and quantities, such as the unit of the Hubble constant or the quantity vaselife. The following application areas are supported by OM: Geometry; Mechanics; Thermodynamics; Electromagnetism; Fluid mechanics; Chemical physics; Photometry; Radiometry and Radiobiology; Nuclear physics; Astronomy and Astrophysics; Cosmology; Earth science; Meteorology; Material science; Microbiology; Economics; Information technology and Typography. + +The ontology employs a class-based modeling approach, defining classes for different types of units, quantities, and measurements, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OM supports the integration of data from various sources, promoting interoperability and data-driven research in units and measurements. + +Typical applications of OM include the development of new measurement methods, the optimization of measurement practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OM enhances collaboration and innovation in the field of units and measurements. + +**Example Usage**: +Annotate a scientific dataset with OM terms to specify units, quantities, and measurements, enabling semantic search and integration with measurement management platforms. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/units_and_measurements/owltime.rst b/docs/source/benchmarking/units_and_measurements/owltime.rst index c56e920e..59e6e20f 100644 --- a/docs/source/benchmarking/units_and_measurements/owltime.rst +++ b/docs/source/benchmarking/units_and_measurements/owltime.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Time Ontology in OWL (OWL-Time) ======================================================================================================== -OWL-Time is an OWL-2 DL ontology of temporal concepts, for describing the temporal properties of resources in the world or described in Web pages. The ontology provides a vocabulary for expressing facts about topological (ordering) relations among instants and intervals, together with information about durations, and about temporal position including date-time information. Time positions and durations may be expressed using either the conventional (Gregorian) calendar and clock, or using another temporal reference system such as Unix-time, geologic time, or different calendars. +The Time Ontology in OWL (OWL-Time) is a comprehensive ontology for representing temporal concepts, relationships, and properties in semantic web and linked data applications. It provides a standardized vocabulary for describing time instants, intervals, durations, temporal positions, and topological relations (e.g., before, after, during) among temporal entities. OWL-Time supports multiple temporal reference systems, including the Gregorian calendar, Unix time, geologic time, and custom calendars, enabling flexible modeling of temporal data. The ontology is widely used for annotating temporal aspects of resources in scientific datasets, event logs, web pages, and knowledge graphs. By providing a common framework, OWL-Time facilitates temporal reasoning, event sequencing, and integration of time-based data across domains. The ontology is maintained by the World Wide Web Consortium (W3C) and is continuously updated to support new temporal modeling requirements. + +**Example Usage**: +Annotate an event dataset with OWL-Time terms to specify event start and end times, durations, and temporal relationships (e.g., "event A before event B"), enabling temporal reasoning and timeline visualization in knowledge graphs. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/units_and_measurements/qudt.rst b/docs/source/benchmarking/units_and_measurements/qudt.rst index 5f3d4267..a8d5d66a 100644 --- a/docs/source/benchmarking/units_and_measurements/qudt.rst +++ b/docs/source/benchmarking/units_and_measurements/qudt.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Quantities, Units, Dimensions and Data Types (QUDT) ======================================================================================================== -QUDT is an advocate for the development and implementation of standards to quantify data expressed in RDF and JSON. +The Quantities, Units, Dimensions and Data Types (QUDT) ontology is a comprehensive framework for representing quantities, units, dimensions, and data types in scientific, engineering, and technical domains. QUDT provides a standardized vocabulary for describing measurement units, physical quantities, conversion factors, and dimensional analysis, supporting data expressed in RDF and JSON. The ontology is widely used for semantic annotation of scientific datasets, IoT data streams, and engineering models, enabling automated unit conversion, validation, and interoperability across diverse systems. QUDT is maintained by NASA Ames Research Center and is continuously updated to reflect new standards and measurement systems. By providing a common semantic foundation, QUDT facilitates data integration, analytics, and knowledge sharing in multidisciplinary projects. The ontology is extensible and can be aligned with other units and measurements ontologies for broader compatibility. + +**Example Usage**: +Annotate a scientific dataset with QUDT terms to specify the quantities measured (e.g., "temperature", "pressure"), their units (e.g., "degree Celsius", "pascal"), and conversion factors, enabling automated unit conversion and semantic search across datasets. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/units_and_measurements/qudv.rst b/docs/source/benchmarking/units_and_measurements/qudv.rst index 47f57212..f7db8fa9 100644 --- a/docs/source/benchmarking/units_and_measurements/qudv.rst +++ b/docs/source/benchmarking/units_and_measurements/qudv.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Quantities, Units, Dimensions and Values (QUDV) ======================================================================================================== -The SysML QUDV (Quantities, Units, Dimensions and Values) modelLibrary is specified in a UML/SysML class/block diagram. In order to generalize its potential usage and alignment with other standardization efforts concerning quantities and units, it is of interest to verify that the QUDV model can also be represented in the form of an ontology using a formal ontology definition language. +The Quantities, Units, Dimensions and Values (QUDV) ontology is a formal representation of the SysML QUDV modelLibrary, providing a standardized vocabulary for describing quantities, units, dimensions, and values in scientific and engineering domains. QUDV is specified in UML/SysML class/block diagrams and is designed to support interoperability and alignment with other standardization efforts concerning quantities and units. The ontology enables the semantic annotation of measurement data, supporting unit conversion, dimensional analysis, and data integration across diverse systems. QUDV is used in systems engineering, modeling and simulation, and scientific data management to ensure consistency and comparability of quantitative information. By providing a common framework, QUDV facilitates automated reasoning, validation, and knowledge sharing in multidisciplinary projects. The ontology is maintained by the SysML community and is aligned with other units and measurements ontologies for broader compatibility. + +**Example Usage**: +Annotate a systems engineering model with QUDV terms to specify the quantities measured (e.g., "length", "mass"), their units (e.g., "meter", "kilogram"), and dimensions, enabling automated unit conversion and validation across engineering tools. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/units_and_measurements/uo.rst b/docs/source/benchmarking/units_and_measurements/uo.rst index 5034e226..b4e35e83 100644 --- a/docs/source/benchmarking/units_and_measurements/uo.rst +++ b/docs/source/benchmarking/units_and_measurements/uo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,62 +23,10 @@ Units of Measurement Ontology (UO) ======================================================================================================== -Metrical units for use in conjunction with PATO. - -Metrics & Statistics --------------------------- - -.. tab:: Graph - - - .. list-table:: Graph Statistics - :widths: 50 50 - :header-rows: 0 - - * - **Total Nodes** - - 2284 - * - **Total Edges** - - 5354 - * - **Root Nodes** - - 6 - * - **Leaf Nodes** - - 754 - :: - - -.. tab:: Coverage - - - .. list-table:: Knowledge Coverage Statistics - :widths: 50 50 - :header-rows: 0 - - * - **Classes** - - 928 - * - **Individuals** - - 0 - * - **Properties** - - 2 - - :: - -.. tab:: Hierarchy - - - .. list-table:: Hierarchical Metrics - :widths: 50 50 - :header-rows: 0 - - * - **Maximum Depth** - - 4 - * - **Minimum Depth** - - 0 - * - **Average Depth** - - 1.14 - * - **Depth Variance** - - 1.12 - :: +The Units of Measurement Ontology (UO) is a domain ontology designed to provide a standardized vocabulary for metrical units, particularly for use in conjunction with the Phenotype and Trait Ontology (PATO) and other biomedical ontologies. UO covers a wide range of units, including SI units, derived units, and commonly used non-SI units, supporting the annotation of quantitative data in biological, medical, and scientific research. The ontology enables semantic interoperability and data integration by providing unique identifiers and relationships for units, quantities, and measurement systems. UO is widely used in bioinformatics, clinical informatics, and data repositories to ensure consistency and comparability of measurement data. By providing a common framework, UO facilitates automated data analysis, unit conversion, and cross-study comparison. The ontology is actively maintained and extended to incorporate new units and measurement concepts as research needs evolve. +**Example Usage**: +Annotate a biomedical dataset with UO terms to specify measurement units for phenotypic traits (e.g., "UO:0000016 (centimeter)", "UO:0000021 (gram)") and enable automated unit conversion and semantic search across datasets. .. tab:: Breadth diff --git a/docs/source/benchmarking/upper_ontology/bfo.rst b/docs/source/benchmarking/upper_ontology/bfo.rst index 9c0e7166..54e44c8b 100644 --- a/docs/source/benchmarking/upper_ontology/bfo.rst +++ b/docs/source/benchmarking/upper_ontology/bfo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Basic Formal Ontology (BFO) ======================================================================================================== -The Basic Formal Ontology (BFO) is a small, upper-level ontology that describes the basic types of entities in the world and how they relate to each other. +The Basic Formal Ontology (BFO) is a small, upper-level ontology that describes the basic types of entities in the world and how they relate to each other. BFO provides a rigorous, domain-neutral framework for organizing and integrating domain ontologies across scientific and technical disciplines. It distinguishes between continuants (entities that persist through time, such as objects and qualities) and occurrents (entities that unfold over time, such as processes and events), supporting precise modeling of reality. BFO is widely adopted in the biomedical, engineering, and environmental sciences as the upper ontology for the Open Biomedical Ontologies (OBO) Foundry and other ontology initiatives. Its formal structure enables automated reasoning, semantic interoperability, and data integration across heterogeneous knowledge bases. BFO is actively maintained and extended by an international community of ontology experts and is recognized as an ISO standard (ISO/IEC 21838-2:2021). + +**Example Usage**: +Use BFO as the upper ontology for a biomedical ontology, classifying entities such as "cell" (object, continuant), "cell division" (process, occurrent), and "cell membrane" (object part), enabling semantic integration with other OBO ontologies. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/upper_ontology/dolce.rst b/docs/source/benchmarking/upper_ontology/dolce.rst index b736cf9f..1f1101da 100644 --- a/docs/source/benchmarking/upper_ontology/dolce.rst +++ b/docs/source/benchmarking/upper_ontology/dolce.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) ======================================================================================================== -The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) is a foundational ontology that provides a conceptual framework for the formalization of domain ontologies. +The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) is a foundational ontology that provides a conceptual framework for the formalization of domain ontologies. DOLCE is designed to capture the ontological categories underlying natural language and human common sense, supporting the modeling of linguistic, cognitive, and social phenomena. It distinguishes between endurants (entities persisting through time), perdurants (events and processes), qualities, and abstract entities, enabling nuanced representation of reality. DOLCE is widely used in linguistics, cognitive science, artificial intelligence, and knowledge engineering to support semantic interoperability and reasoning. Its modular structure allows for extensions and customization for specific domains, making it a popular choice for building interoperable ontologies. DOLCE has influenced the development of many domain ontologies and is recognized for its rigorous formal foundations and alignment with human conceptualization. + +**Example Usage**: +Use DOLCE as the upper ontology for a linguistic ontology, classifying entities such as "utterance" (perdurant), "speaker" (endurant), and "meaning" (abstract), enabling semantic integration with other cognitive and linguistic resources. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/upper_ontology/fair.rst b/docs/source/benchmarking/upper_ontology/fair.rst index c4d4daad..428b3eab 100644 --- a/docs/source/benchmarking/upper_ontology/fair.rst +++ b/docs/source/benchmarking/upper_ontology/fair.rst @@ -25,7 +25,9 @@ FAIR Vocabulary (FAIR) ======================================================================================================== -This is the formal vocabulary (ontology) describing the FAIR principles. +The FAIR Vocabulary is a formal ontology that provides machine-readable definitions and semantic representation of the FAIR Data Principles (Findability, Accessibility, Interoperability, Reusability). It formalizes key concepts underlying FAIR data management including data discoverability mechanisms, access protocols, interoperability standards, and reusability conditions. The ontology enables automated assessment and verification of FAIR compliance, supporting the development of FAIR data management tools and services. FAIR vocabulary terms can be applied to dataset descriptions, data repositories, and digital object properties to formally declare their FAIR characteristics and compliance levels. The vocabulary facilitates communication of FAIR principles within research organizations, funding agencies, and data management communities by providing standardized semantic definitions. + +**Example Usage**: Annotate a dataset in a repository with FAIR terms to indicate its Findability (via persistent identifiers like DOIs), Accessibility (through standard protocols), Interoperability (using standard formats and ontologies), and Reusability (via clear licensing and metadata). Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/upper_ontology/gfo.rst b/docs/source/benchmarking/upper_ontology/gfo.rst index ff215fa5..cf1084be 100644 --- a/docs/source/benchmarking/upper_ontology/gfo.rst +++ b/docs/source/benchmarking/upper_ontology/gfo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ General Formal Ontology (GFO) ======================================================================================================== -The General Formal Ontology is a top-level ontology for conceptual modeling, which is being constantly further developed by Onto-Med. It includes elaborations of categories like objects, processes, time and space, properties, relations, roles, functions, facts, and situations. Moreover, we are working on an integration with the notion of levels of reality in order to more appropriately capture entities in the material, mental, and social areas. +The General Formal Ontology (GFO) is a top-level, foundational ontology developed by Onto-Med for conceptual modeling across scientific, technical, and philosophical domains. GFO provides a rigorous framework for representing fundamental categories such as objects, processes, time, space, properties, relations, roles, functions, facts, and situations. It is designed to support the integration of material, mental, and social entities by incorporating the notion of levels of reality, enabling nuanced modeling of complex systems. GFO is modular and extensible, allowing domain ontologies to build upon its core categories for specialized applications. The ontology is used in knowledge engineering, biomedical informatics, cognitive science, and information systems to ensure semantic interoperability and logical consistency. GFO is actively maintained and extended to address emerging requirements in conceptual modeling and ontology integration. + +**Example Usage**: +Use GFO as the upper ontology for a biomedical ontology, classifying entities such as "disease" (situation), "patient" (object), and "treatment process" (process), enabling semantic integration and reasoning across clinical and research data. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/upper_ontology/sio.rst b/docs/source/benchmarking/upper_ontology/sio.rst index 2fe51b4f..49592fa2 100644 --- a/docs/source/benchmarking/upper_ontology/sio.rst +++ b/docs/source/benchmarking/upper_ontology/sio.rst @@ -25,7 +25,9 @@ Semanticscience Integrated Ontology (SIO) ======================================================================================================== -The semanticscience integrated ontology (SIO) provides a simple, integrated upper level ontology (types, relations) for consistent knowledge representation across physical, processual and informational entities. This project provides foundational support for the Bio2RDF (http://bio2rdf.org) and SADI (http://sadiframework.org) projects. +The Semanticscience Integrated Ontology (SIO) is a simple yet comprehensive upper-level ontology that provides foundational types and relations for consistent semantic knowledge representation across physical entities, processes, and information constructs. SIO defines core abstract concepts such as objects, processes, attributes, and information entities, enabling researchers to build domain-specific ontologies with consistent semantic foundations. The ontology supports interconnection of diverse knowledge domains through its generalized entity and relationship types, facilitating interoperability across heterogeneous biological and biomedical ontologies. SIO serves as the semantic backbone for major linked data projects including Bio2RDF (which integrates biological databases) and SADI (Semantic Automated Discovery and Integration framework). The ontology enables semantic reasoning and automated knowledge discovery by providing explicit typing and relationship hierarchies that computationally systems can interpret. + +**Example Usage**: Use SIO classes to type entities in a knowledge graph (e.g., SIO:Protein, SIO:Gene, SIO:Organism) and relate them via SIO properties (e.g., SIO:is-derived-from, SIO:encodes, SIO:has-function) for automated biomedical data integration and discovery. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/upper_ontology/sumo.rst b/docs/source/benchmarking/upper_ontology/sumo.rst index 4f73787d..b7ebd7b0 100644 --- a/docs/source/benchmarking/upper_ontology/sumo.rst +++ b/docs/source/benchmarking/upper_ontology/sumo.rst @@ -1,5 +1,3 @@ - - .. sidebar:: .. list-table:: **Ontology Card** @@ -25,7 +23,10 @@ Suggested Upper Merged Ontology (SUMO) ======================================================================================================== -The Suggested Upper Merged Ontology (SUMO) and its domain ontologies form the largest formal public ontology in existence today. They are being used for research and applications in search, linguistics and reasoning. SUMO is the only formal ontology that has been mapped to all of the WordNet lexicon. +The Suggested Upper Merged Ontology (SUMO) is one of the largest and most widely used formal upper ontologies, providing a comprehensive framework for representing general concepts and relationships across all domains of knowledge. SUMO and its domain ontologies are used for research and applications in search, linguistics, automated reasoning, and artificial intelligence. SUMO is unique in being formally mapped to the entire WordNet lexicon, enabling semantic integration between natural language and formal knowledge representation. The ontology covers abstract and concrete entities, processes, attributes, relations, and events, supporting logical inference and knowledge discovery. SUMO is open source and maintained by an active community, with ongoing extensions and domain-specific modules for specialized applications. By providing a rigorous semantic foundation, SUMO facilitates interoperability, data integration, and advanced reasoning in knowledge-based systems. + +**Example Usage**: +Use SUMO as the upper ontology for a knowledge graph, mapping domain-specific concepts (e.g., "vehicle," "disease," "financial transaction") to SUMO classes and leveraging its logical axioms for automated reasoning and semantic search. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/web_and_internet/hydra.rst b/docs/source/benchmarking/web_and_internet/hydra.rst index 7ed02f19..1b8750f4 100644 --- a/docs/source/benchmarking/web_and_internet/hydra.rst +++ b/docs/source/benchmarking/web_and_internet/hydra.rst @@ -25,7 +25,9 @@ Hydra Ontology (Hydra) ======================================================================================================== -Hydra is a lightweight vocabulary to create hypermedia-driven Web APIs. By specifying a number of concepts commonly used in Web APIs it enables the creation of generic API clients. +Hydra is a lightweight vocabulary and ontology for creating hypermedia-driven REST APIs that are self-describing and machine-actionable through semantic web technologies. It enables developers to create generic API clients that can automatically discover and interact with APIs by interpreting hypermedia controls and semantic metadata embedded in API responses. Hydra defines core concepts commonly used in Web APIs such as operations, properties, classes, and relationships, providing a standardized way to describe API structure and functionality. The vocabulary enables APIs to be self-documenting and interoperable, allowing clients to dynamically adapt to API changes without hardcoded endpoint knowledge. Hydra supports linked data and semantic web principles, enabling APIs to contribute to the broader linked open data ecosystem. + +**Example Usage**: Define a REST API endpoint for a resource collection using Hydra vocabularies to describe available operations (GET, POST, DELETE), supported classes, properties with their types, and hypermedia links to related resources, enabling automated client discovery and interaction. Metrics & Statistics -------------------------- diff --git a/docs/source/benchmarking/web_and_internet/saref.rst b/docs/source/benchmarking/web_and_internet/saref.rst index 6321337f..75660377 100644 --- a/docs/source/benchmarking/web_and_internet/saref.rst +++ b/docs/source/benchmarking/web_and_internet/saref.rst @@ -25,7 +25,9 @@ Smart Applications REFerence ontology (SAREF) ======================================================================================================== -The Smart Applications REFerence (SAREF) suite of ontologies forms a shared model of consensus intended to enable semantic interoperability between solutions from different providers and among various activity sectors in the Internet of Things (IoT), thus contributing to the development of data spaces. SAREF is published as a set of open standards produced by ETSI Technical Committee Smart Machine-to-Machine communications (SmartM2M). +SAREF is a comprehensive suite of interrelated ontologies that defines a shared model of consensus for enabling semantic interoperability across IoT solutions and smart applications from diverse providers and industry sectors. It provides standardized vocabulary for describing smart devices, their capabilities, interactions, and services in Internet of Things (IoT) and smart environments (smart homes, smart buildings, smart cities). SAREF captures essential IoT concepts including devices, sensors, actuators, services, properties, and the relationships between them, facilitating machine-to-machine communication and automation. The SAREF suite is published as open standards by ETSI Technical Committee SmartM2M (Smart Machine-to-Machine communications), ensuring broad industry adoption and compatibility. SAREF enables semantic data spaces by providing common terminology that allows data and services from different IoT platforms and manufacturers to be integrated and understood. + +**Example Usage**: Annotate an IoT device deployment with SAREF terms describing the device type (smart thermostat, light bulb, motion sensor), its capabilities (temperature measurement, brightness control), services offered (remote adjustment, scheduling), and relationships to control systems and user preferences. Metrics & Statistics -------------------------- diff --git a/docs/source/index.rst b/docs/source/index.rst index 15d65208..db235ce8 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -48,7 +48,7 @@ Usage See the `Quickstart `_ for more quick information on how to use OntoLearner. -Working with OntoLearner s straightforward: +Working with OntoLearner is straightforward: .. sidebar:: Installation diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 237e036a..a2e2e4ed 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -9,7 +9,7 @@ Install with pip .. sidebar:: Verify the installation - Once the isntallation is done, verify the installation by: + Once the installation is done, verify the installation by: .. code-block:: python @@ -27,7 +27,7 @@ Install with pip .. tab:: From GitHub - The following pip install will installs the latest version of OntoLearner from the `main` branch of the OntoLearner at GitHub using `pip`. + The following pip install will install the latest version of OntoLearner from the `main` branch of the OntoLearner at GitHub using `pip`. :: @@ -63,4 +63,4 @@ You can install OntoLearner directly from source to take advantage of the bleedi Install PyTorch with CUDA support -------------------------------------------- -To use a GPU/CUDA for learners, you must install PyTorch with CUDA support. Follow `PyTorch - Get Started`_ for installation steps. +To use a GPU/CUDA for learners, you must install PyTorch with CUDA support. Follow `PyTorch - Get Started `_ for installation steps. diff --git a/docs/source/learners/llm.rst b/docs/source/learners/llm.rst index f0fbc1c0..2d732560 100644 --- a/docs/source/learners/llm.rst +++ b/docs/source/learners/llm.rst @@ -145,7 +145,7 @@ For this, you can extend the ``AutoLLM`` class and implement the required ``load`` and ``generate`` methods. Basic requirements are: 1. Inherit from ``AutoLLM`` -2. Implement ``load(model_id)``, if your loging model is different (as an example `mistralai/Mistral-Small-3.2-24B-Instruct-2506 `_ uses different type of loading) +2. Implement ``load(model_id)``, if your model loading is different (for example `mistralai/Mistral-Small-3.2-24B-Instruct-2506 `_ uses different type of loading) 3. Implement ``generate(inputs, max_new_tokens)`` to encodes prompts, performs generation, decodes outputs, and maps them to labels. diff --git a/docs/source/learners/rag.rst b/docs/source/learners/rag.rst index a2edc5b4..010d453f 100644 --- a/docs/source/learners/rag.rst +++ b/docs/source/learners/rag.rst @@ -8,7 +8,7 @@ Retrieval Augmented Generation **Retrieval Augmented Generation (RAG)** learners combine the strengths of both retrieval models and LLMs to perform ontology learning tasks. This methodology is a hybrid approach designed to enhance ontology learning by addressing the limitations of using LLMs alone. -RAG learners operate in two main steps: 1) **Retrieval** component that finds the most relevant examples from the training data based on similarity to the input query. 2) **Generation** component that uses retrieved examples as context to generate a response. The methodology behind RAG learners combines vector retrieval with generative language modeling to enhance ontology learning tasks. This hybrid approach addresses the limitations of using LLMs alone by grounding the model's responses in specific ontological examples from the training data. By encoding ontological elements into a vector space, the retriever can identify semantically similar concepts, relations, or taxonomic structures. These retrieved examples serve as few-shot demonstrations that provide the LLM with domain-specific context, enabling more accurate and consistent ontological inferences. This approach is particularly effective for specialized domains where the model's pre-trained knowledge may be insufficient or where precise ontological alignments are critical. +RAG learners operate in two main steps: 1) **Retrieval** component that finds the most relevant examples from the training data based on similarity to the input query. 2) **Generation** component that uses retrieved examples as context to generate a response. The methodology behind RAG learners combines vector retrieval with generative language modeling to enhance ontology learning tasks. This hybrid approach addresses the limitations of using LLMs alone by grounding the model's responses in specific ontological examples from the training data. By encoding ontological elements into a vector space, the retriever can identify semantically similar concepts, relations, or taxonomic structures. These retrieved examples serve as few-shot demonstrations that provide the LLM with domain-specific context, enabling more accurate and consistent ontological inferences. This approach is particularly effective for specialized domains where the model's pre-trained knowledge may be insufficient or where precise ontological alignments are critical. Loading Ontological Data ---------------------------- @@ -99,7 +99,7 @@ To build a RAG model, you first initialize its constituent parts: an LLM learner Pipeline Usage --------------------- -Similar to LLM and Retrieval learner, RAG Learner is also callable via streamlined ``LearnerPipeline`` class that simplifies the entire process learning. +Similar to LLM and Retrieval learner, RAG Learner is also callable via streamlined ``LearnerPipeline`` class that simplifies the entire learning process. You initialize the ``LearnerPipeline`` by directly providing the ``retriever_id``, ``llm_id``, and other parameters like ``hf_token``, ``batch_size``, and ``top_k`` (number of top retrievals to include in RAG prompting). Then, you simply call the ``pipeline`` instance with your ``train_data``, ``test_data``, specify ``evaluate=True`` to compute metrics, and define the ``task`` (e.g., `'term-typing'`). diff --git a/docs/source/learners/retrieval.rst b/docs/source/learners/retrieval.rst index 5c74e93a..f2bd41ce 100644 --- a/docs/source/learners/retrieval.rst +++ b/docs/source/learners/retrieval.rst @@ -7,7 +7,7 @@ Retrieval Models * Retrieval Learner Pipeline Usage Example: `retriever_learner_pipeline_usage.py `_ -Retriever-only learners use embedding-based retrieval models to perform ontology learning tasks without using LLMs. Retriever-only learners operate by: 1) Indexing: Creating embeddings for all examples in the training data, 2) Retrieval: Finding the most similar examples to the input query based on embedding similarity. The methodology behind retriever-only learners is founded on vector representations of ontological elements, allowing for semantic comparison in a high-dimensional space. +Retriever-only learners use embedding-based retrieval models to perform ontology learning tasks without using LLMs. Retriever-only learners operate by: 1) Indexing: Creating embeddings for all examples in the training data, 2) Retrieval: Finding the most similar examples to the input query based on embedding similarity. The methodology behind retriever-only learners is founded on vector representations of ontological elements, allowing for semantic comparison in a high-dimensional space. By transforming terms, concepts, and relationships into embeddings through pre-trained language models, the system can measure semantic similarity between ontological components without relying on explicit linguistic patterns or rules. This approach leverages the distributional hypothesis—that semantically similar terms appear in similar contexts—to identify relationships between entities. The system then employs deterministic aggregation methods like majority voting or weighted consensus to derive predictions from the retrieved examples. This methodology is computationally efficient compared to LLM-based approaches and particularly effective for tasks with well-defined patterns across domain-specific ontologies. diff --git a/docs/source/learning_tasks/learning_tasks.rst b/docs/source/learning_tasks/learning_tasks.rst index 3e9fb511..abc46900 100644 --- a/docs/source/learning_tasks/learning_tasks.rst +++ b/docs/source/learning_tasks/learning_tasks.rst @@ -13,7 +13,7 @@ Ontology Learning Tasks * `LLMs4OL Paradigm Tasks `_ * `Text2Onto `_. -Within the OntoLearner framework, the modularized ontologies are extended with OL capabilities to effectively support ontology engineering tasks. This extension is guided by the **LLMs4OL paradigm** tasks, which leverages LLMs to automate the key ontology learning process. The LLMs4OL paradigm is structured around three primary tasks essential for developing a primitive ontology: 1) Term Typing, 2) Taxonomy Discovery, and 3) Non-Taxonomic Relationship Extraction. By incorporating these tasks, OntoLearner ensures that its modularized ontologies through Ontologizer are not only structured and reusable but also capable of continuous learning and enhancement through automated ontology learning techniques. +Within the OntoLearner framework, the modularized ontologies are extended with OL capabilities to effectively support ontology engineering tasks. This extension is guided by the **LLMs4OL paradigm** tasks, which leverages LLMs to automate the key ontology learning process. The LLMs4OL paradigm is structured around three primary tasks essential for developing a primitive ontology: 1) Term Typing, 2) Taxonomy Discovery, and 3) Non-Taxonomic Relationship Extraction. By incorporating these tasks, OntoLearner ensures that its modularized ontologies through Ontologizer are not only structured and reusable but also capable of continuous learning and enhancement through automated ontology learning techniques. Additionally, OntoLearner incorporates a **Text2Onto**, which focuses on extracting ontological terms and types directly from raw text. Notably, Text2Onto is designed to function independently of the LLMs4OL pipeline, and Ontologizer. Users can import or load LLMs4OL tasks as inputs to Text2Onto, enabling flexible and extensible data extraction workflows for OL. diff --git a/docs/source/learning_tasks/llms4ol.rst b/docs/source/learning_tasks/llms4ol.rst index aac7f8d4..24822782 100644 --- a/docs/source/learning_tasks/llms4ol.rst +++ b/docs/source/learning_tasks/llms4ol.rst @@ -133,4 +133,4 @@ To perform machine learning tasks, the first step after extracting the dataset i ontology.load() data = ontology.extract() - train_data, test_data = train_test_split(data test_size=0.2, random_state=42) + train_data, test_data = train_test_split(data, test_size=0.2, random_state=42) diff --git a/docs/source/learning_tasks/text2onto.rst b/docs/source/learning_tasks/text2onto.rst index 550e7699..66d620e8 100644 --- a/docs/source/learning_tasks/text2onto.rst +++ b/docs/source/learning_tasks/text2onto.rst @@ -3,7 +3,7 @@ Text2Onto Data Generator ----------------------------------------- -OntoLearner library can be used to generate synthatic data for evaluating the task of term and type extraction from natural language text. It will generate a text corpus of documents aligned with a given ontology. +OntoLearner library can be used to generate synthetic data for evaluating the task of term and type extraction from natural language text. It will generate a text corpus of documents aligned with a given ontology. The first step is to load the ontology data from the selected ontology. @@ -25,7 +25,7 @@ or host an LLM locally using tools such as Ollama or vLLM. .. note:: - More details about all provides supported by ``DSPy`` (through *LiteLLM*) can be found in `this link `_. + More details about all providers supported by ``DSPy`` (through *LiteLLM*) can be found in `this link `_. Information about the LLM is provided in a ``.env`` file similar to the following. @@ -66,7 +66,7 @@ Then you can configure DSPy to use the provided LLM and generate the synthetic t Data Splitter ------------------------ -You can split the generated synthetic data using for training, hyperparameter optimization (validation), and testing purposes. +You can split the generated synthetic data for training, hyperparameter optimization (validation), and testing purposes. .. code-block:: python diff --git a/docs/source/ontologizer/new_ontologies.rst b/docs/source/ontologizer/new_ontologies.rst index 3ff0c76b..e1a09b5b 100644 --- a/docs/source/ontologizer/new_ontologies.rst +++ b/docs/source/ontologizer/new_ontologies.rst @@ -7,7 +7,7 @@ New Ontologies .. code-block:: bash - pip install -u OntoLearner + pip install -U OntoLearner This guide explains how to add new ontologies to the OntoLearner. Adding a new ontology involves creating a Ontologizer (class for the ontology to be imported Programmatically), implementing the necessary methods, and to process and benchmark the ontology. @@ -124,11 +124,11 @@ To process the ontology and generate benchmarks, you will need to use a dedicate You can also access the processed ontology via: -.. code-block:: python +.. code-block:: python processed_ontology = processor.get_processed_ontology() - # The processed_ontology` is dictionary with following key values: + # The processed_ontology is a dictionary with the following key-value pairs: # "ontology": ontology # "metrics": {...} # "ontology_id": "MyNewOntology" diff --git a/docs/source/ontologizer/ontology_hosting.rst b/docs/source/ontologizer/ontology_hosting.rst index df8e7ac6..3fca4d66 100644 --- a/docs/source/ontologizer/ontology_hosting.rst +++ b/docs/source/ontologizer/ontology_hosting.rst @@ -4,7 +4,7 @@ Ontology Hosting Ontology Repositories ----------------------- -OntoLearner provides seamless integration with HuggingFace, allowing you to easily download ontologies and use. OntoLearner follows a systematic methodology for processing and distributing ontologies through Hugging Face. Each ontology undergoes a comprehensive processing pipeline that includes: (1) loading and parsing the original ontology file, (2) extracting structured learning datasets including term typings, taxonomic relations, and non-taxonomic relations, (3) computing detailed metrics about the ontology's topology and content, (4) generating comprehensive documentation, and (5) organizing the processed data by domain and uploading to dedicated Hugging Face repositories. This approach ensures that researchers have access to both the original ontologies and ready-to-use datasets optimized for machine learning applications, while maintaining full traceability and documentation of the processing methodology. +OntoLearner provides seamless integration with HuggingFace, allowing you to easily download ontologies and use them. OntoLearner follows a systematic methodology for processing and distributing ontologies through Hugging Face. Each ontology undergoes a comprehensive processing pipeline that includes: (1) loading and parsing the original ontology file, (2) extracting structured learning datasets including term typings, taxonomic relations, and non-taxonomic relations, (3) computing detailed metrics about the ontology's topology and content, (4) generating comprehensive documentation, and (5) organizing the processed data by domain and uploading to dedicated Hugging Face repositories. This approach ensures that researchers have access to both the original ontologies and ready-to-use datasets optimized for machine learning applications, while maintaining full traceability and documentation of the processing methodology. OntoLearner maintains a set of default repositories for each domain under the `SciKnowOrg `_ organization. These repositories follow the naming pattern ``SciKnowOrg/ontolearner-{domain}`` and contain pre-processed ontology data. @@ -25,7 +25,7 @@ The complete list of OntoLearner domain repositories is shown below: :widths: 20 40 40 * - 🏷️ Domain - - 🤗 HuggignFace Repository + - 🤗 Hugging Face Repository - 📝 Description * - `Agriculture `_ - ``SciKnowOrg/ontolearner-agriculture`` diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 40b3e44a..2f7544ed 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -45,7 +45,7 @@ Below is an example using the `Agronomy Ontology -As a presented in the above figure, core ontology learning tasks within OntoLearner are: +As presented in the above figure, core ontology learning tasks within OntoLearner are: .. list-table:: :widths: 20 80 @@ -193,6 +193,6 @@ What's Next? --------------- * :doc:`ontologizer/ontology_modularization` - How Ontologizer works. -* :doc:`ontologizer/ontology_hosting` - Checkout avaiable domains and their repositories. +* :doc:`ontologizer/ontology_hosting` - Check out available domains and their repositories. * :doc:`learning_tasks/learning_tasks` - Deep dive into all three tasks * :doc:`learning_tasks/text2onto` - How Text2Onto works? diff --git a/metadata/ontolearner-metadata.rdf b/metadata/ontolearner-metadata.rdf index 6fde85d4..39f2033e 100644 --- a/metadata/ontolearner-metadata.rdf +++ b/metadata/ontolearner-metadata.rdf @@ -5,571 +5,56 @@ This Dublin Core metadata collection describes ontologies benchmarked in OntoLearner. It includes information such as title, creator, format, license, and version. OntoLearner Team MIT License - 1.4.7 + 1.5.0 - - IAO - Information Artifact Ontology (IAO) - The Information Artifact Ontology (IAO) is an ontology of information entities, originally driven by work by the OBI digital entity and realizable information entity branch. - OWL - 2022-11-07 - Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/IAO - General Knowledge - Information, Data, Knowledge - - - PreMOn - Pre-Modern Ontology (PreMOn) - The PreMOn Ontology is an extension of lemon (W3C Ontology Lexicon Community Group, 2015) for representing predicate models and their mappings. The Core Module of the PreMOn Ontology defines the main abstractions for modelling semantic classes with their semantic roles, mappings between different predicate models, and annotations. - Francesco Corcoglioniti, Marco Rospocher <https://dkm.fbk.eu/rospocher> - OWL - 2018-02-15 - Creative Commons 4.0 - https://premon.fbk.eu/ontology/core# - Scholarly Knowledge - Linguistics - 2018a - - - BBCCoreConcepts - BBC Core Concepts Ontology (BBCCoreConcepts) - The generic BBC ontology for people, places, events, organisations, themes which represent things that make sense across the BBC. This model is meant to be generic enough, and allow clients (domain experts) link their own concepts e.g., athletes or politicians using rdfs:sublClassOf the particular concept. - jeremy.tarling@bbc.co.uk, tom.hodgkinson@bbc.co.uk - TTL - 2019-11-21 - Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/core-concepts-ontology - News and Media - Core Concepts - 1.30 - - - BBCCreativeWork - BBC Creative Work Ontology (BBCCreativeWork) - This ontology defines the terms required to describe the creative works produced by the BBC and their associated metadata. This ontology powers reading and writing creative works in the triplestore using tags associated with them (about) their more specific types (BlogPost, NewsItem, Programme) and audiences (audience). - LinkedData@bbc.co.uk - TTL - 2012-12-01 - Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/creative-work-ontology - News and Media - Creative Work - 1.19 - - - BBCStoryline - BBC Storyline Ontology (BBCStoryline) - The News Storyline Ontology is a generic model for describing and organising the stories news organisations tell. The ontology is intended to be flexible to support any given news or media publisher's approach to handling news stories. At the heart of the ontology, is the concept of Storyline. As a nuance of the English language the word 'story' has multiple meanings. In news organisations, a story can be an individual piece of content, such as an article or news report. It can also be the editorial view on events occurring in the world. - http://uk.linkedin.com/in/paulwilton, http://www.bbc.co.uk/blogs/internet/authors/Jeremy_Tarling, http://uk.linkedin.com/in/jarredmcginnis - TTL - 2013-05-01 - Creative Commons 4.0 - https://iptc.org/thirdparty/bbc-ontologies/storyline.html - News and Media - Storyline - 0.3 - - - REX - Physico-chemical process ontology (REX) - REX is an ontology of physico-chemical processes, i.e. physico-chemical changes occurring in course of time. REX includes both microscopic processes (involving molecular entities or subatomic particles) and macroscopic processes. Some biochemical processes from Gene Ontology (GO Biological process) can be described as instances of REX. - University of Warsaw - OWL - 2025-03-11 - Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/REX - Chemistry - 1.0 - - - OIECharacterisation - Open Innovation Environment Characterisation (OIECharacterisation) - EMMO-compliant, domain-level OIE ontology tackling the areas of characterization methods. - Daniele Toti, Gerhard Goldbeck, Pierluigi Del Nostro - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/OIE-Ontologies/ - Materials Science and Engineering - Materials - - - DoCO - Document Components Ontology (DoCO) - DoCO, the Document Components Ontology, is an OWL 2 DL ontology that provides a general-purpose structured vocabulary of document elements. DoCO has been designed as a general unifying ontological framework for describing different aspects related to the content of scientific and other scholarly texts. Its primary goal has been to improve the interoperability and shareability of academic documents (and related services) when multiple formats are actually used for their storage. - David Shotton and Silvio Peroni - RDF - 2015-07-03 - Creative Commons 4.0 - http://www.sparontologies.net/ontologies/doco - Education - document components - 1.3 - - - SPWorkflow - SMART Protocols Ontology: Workflow Module (SP-Workflow) - SP-Workflow module represents: i) the executable elements of a protocol; ii) the experimental actions and material entities that participates in instructions (sample/specimen, organisms, reagents, instruments); and iii) the order of execution of the instructions. - http://oxgiraldo.wordpress.com - OWL - 2013-07-01 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/SMARTProtocols/SMART-Protocols - Scholarly Knowledge - Workflows - 4.0 - - - SchemaOrg - Schema.org Ontology (SchemaOrg) - Schema.org is a collaborative, community activity with a mission to create, maintain, and promote schemas for structured data on the Internet, on web pages, in email messages, and beyond. - Schema.org Community - OWL - 2024-11-22 - Creative Commons 4.0 - https://github.com/schemaorg/schemaorg/blob/main/data/releases/28.1/schemaorg.owl - General Knowledge - Web Development - 28.1 - - - MSEO - Materials Science and Engineering Ontology (MSEO) - MSEO utilizes the IOF Ontology stack giving materials scientists and engineers the ability to represent their experiments and resulting data. The goal is to create machine and human readable sematic data which can be easily digested by other science domains. It is a product of the joint venture Materials Open Lab Project between the Bundesanstalt für Materialforschung und -prüfung (BAM) and the Fraunhofer Group MATERIALS and uses the BWMD ontology created by Fraunhofer IWM as a starting point. - Thomas Hanke, Fraunhofer IWM - TTL - MIT License - https://github.com/Mat-O-Lab/MSEO - Materials Science and Engineering - Materials Science - - - AFO - Allotrope Foundation Ontology (AFO) - The AFO is an ontology suite that provides a standard vocabulary and semantic model for the representation of laboratory analytical processes. The AFO suite is aligned at the upper layer to the Basic Formal Ontology (BFO). The core domains modeled include, Equipment, Material, Process, and Results. This artifact contains all triples of Allotrope Foundation Merged Without QUDT Ontology Suite (REC/2023/12) together with triples inferred with HermiT. - Allotrope Foundation - TTL - 2024-06-28 - CC BY 4.0 - https://terminology.tib.eu/ts/ontologies/AFO - Chemistry - Laboratory Analytical Processes - 2024-06 - - - NPO - NanoParticle Ontology (NPO) - NanoParticle Ontology (NPO) is developed within the framework of the Basic Formal Ontology (BFO), and implemented in the Ontology Web Language (OWL) using well-defined ontology design principles. The NPO was developed to represent knowledge underlying the preparation, chemical composition, and characterization of nanomaterials involved in cancer research. Public releases of the NPO are available through BioPortal website, maintained by the National Center for Biomedical Ontology. Mechanisms for editorial and governance processes are being developed for the maintenance, review, and growth of the NPO. - Dennis G. Thomas - OWL - 2013-05-31 - BSD-3-Clause license - https://github.com/sobolevnrm/npo?tab=readme-ov-file - Biology and Life Sciences - Materials Science - 2013-05-31 - - - IOF - Industrial Ontology Foundry (IOF) - The IOF Core Ontology contains notions found to be common across multiple manufacturing domains. This file is an RDF implementation of these notions. The ontology utilizes the Basic Formal Ontology or BFO as a top-level ontology but also borrows terms from various domain-independent or mid-level ontologies. The purpose of the ontology is to serve as a foundation for ensuring consistency and interoperability across various domain-specific reference ontologies the IOF publishes. - IOF Core Working Group - RDF - 2020 - MIT - https://oagi.org/pages/Released-Ontologies - Industry - Manufacturing - 1.0 - - - Wine - Wine Ontology (Wine) - A project to define an RDF style ontology for wines and the wine-industry - RDF - https://github.com/UCDavisLibrary/wine-ontology - Food and Beverage - Wine - - - Framester - Framester Ontology (Framester) - Framester is a a frame-based ontological resource acting as a hub between linguistic resources such as FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, and leveraging this wealth of links to create an interoperable predicate space formalized according to frame semantics and semiotics. Framester uses WordNet and FrameNet at its core, expands it to other resources transitively, and represents them in a formal version of frame semantics. - Aldo Gangemi - RDF - 19-04-2016 - Creative Commons 4.0 - http://150.146.207.114/lode/extract?url=http://ontologydesignpatterns.org/ont/framester/framester.owl - Scholarly Knowledge - Linguistics - 1.0 - - - VOAF - Vocabulary of a Friend (VOAF) - The Vocabulary of a Friend (VOAF) is a vocabulary specification providing elements allowing the description of vocabularies (RDFS vocabularies or OWL ontologies). It is based on Dublin Core and VOID. - Bernard Vatant - RDF - 2013-05-24 - Creative Commons 3.0 - https://lov.linkeddata.es/vocommons/voaf/v2.3/ - Scholarly Knowledge - Social Network - 2.3 - - - PKO - Provenance Knowledge Ontology (PKO) - Procedural Knowledge (PK) is knowing how to perform some tasks, as opposed to descriptive/declarative knowledge, which is knowing what in terms of facts and notions. In industry, PK refers in general to structured processes to be followed, and can be related to both production (e.g., procedure on the production line in a plant) and services (e.g., procedure for troubleshooting during customer support); to specific technical expertise (e.g., procedure to set up a specific machine) and general regulations and best practices (e.g., safety procedures, activities to minimise environmental impact). - Mario Scrocca (Cefriel), Valentina Carriero (Cefriel) - RDF - 2025-03-01 - Creative Commons 4.0 - https://github.com/perks-project/pk-ontology/tree/master - Industry - Provenance - 1.0.0 - - - SUMO - Suggested Upper Merged Ontology (SUMO) - The Suggested Upper Merged Ontology (SUMO) and its domain ontologies form the largest formal public ontology in existence today. They are being used for research and applications in search, linguistics and reasoning. SUMO is the only formal ontology that has been mapped to all of the WordNet lexicon. - OWL - 2025-02-17 - Creative Commons 4.0 - https://www.ontologyportal.org/ - Upper Ontology - 1.0 - - - PRotein - Protein Ontology (PRO) - The PRotein Ontology (PRO) formally defines taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. - RDF - 08:08:2024 - Creative Commons 4.0 - http://purl.obolibrary.org/obo/pr.owl - Medicine - Protein - 1.2 - - - WiLD - Workflows in Linked Data (WiLD) - Ontology to describe Workflows in Linked Data. - Tobias Käfer - TTL - 2020-06-10 - DBpedia License - https://databus.dbpedia.org/ontologies/purl.org/wild--vocab/2020.06.10-210552 - Scholarly Knowledge - Materials Science - - - OIEManufacturing - Open Innovation Environment Manufacturing (OIEManufacturing) - The manufacturing module populates the physicalistic perspective with manufacturing subclasses categorised according to modern applied physical sciences. - Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/OIE-Ontologies/ - Materials Science and Engineering - Materials - - - QUDT - Quantities, Units, Dimensions and Data Types (QUDT) - QUDT is an advocate for the development and implementation of standards to quantify data expressed in RDF and JSON. - NASA Ames Research Center - TTL - March 1, 2022 - Creative Commons 4.0 - https://qudt.org/ - Units and Measurements - Physics - 2.1 - - - Juso - Juso Ontology (Juso) - Juso Ontology is a Web vocabulary for describing geographical addresses and features. - James G. Kim, LiST Inc. - TTL - 2015-11-10 - Creative Commons 4.0 - https://rdfs.co/juso/0.1.1/html - Geography - geographical knowledge - 0.1.1 - - - ENM - Environmental Noise Measurement Ontology (ENM) - The eNanoMapper project (https://www.enanomapper.net/), NanoCommons project (https://www.nanocommons.eu/) and ACEnano project (http://acenano-project.eu/) are creating a pan-European computational infrastructure for toxicological data management for ENMs, based on semantic web standards and ontologies. This ontology is an application ontology targeting the full domain of nanomaterial safety assessment. It re-uses several other ontologies including the NPO, CHEMINF, ChEBI, and ENVO. - eNanoMapper Consortium - OWL - 2025-02-17 - Creative Commons 3.0 - https://terminology.tib.eu/ts/ontologies/ENM - Medicine - Material Science and Engineering - 10.0 - - - PTO - Product Types Ontology (PTO) - The Product Types Ontology is designed to be used in combination with GoodRelations, a standard vocabulary for the commercial aspects of offers. - Martin Hepp - RDF - 2025-02-21 - Creative Commons 3.0 - http://www.productontology.org/ - Industry - 1.0 - - - CIFCore - Crystallographic Information Framework Core Dictionary (CIFCore) - (1) to explain the historical development of CIF dictionaries to define in a machine-actionable manner the contents of data files covering various aspects of crystallography and related structural sciences; (2) to demonstrate some of the more complex types of information that can be handled with this approach. - TTL - May 24, 2023 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/CIF-ontology?tab=readme-ov-file - Materials Science and Engineering - Materials Science - 0.1.0 - - - MDO - Materials Design Ontology (MDO) - MDO is an ontology for materials design field, representing the domain knowledge specifically related to solid-state physics and computational materials science. - Materials Design Division, National Institute for Materials Science (NIMS) - OWL - 2022-08-02 - Creative Commons 4.0 - https://github.com/LiUSemWeb/Materials-Design-Ontology/tree/master/ - Materials Science and Engineering - Materials Design - 1.1 - - - OntoKin - Chemical Kinetics Ontology (OntoKin) - OntoKin is an ontology developed for representing chemical kinetic reaction mechanisms. - IEEE - OWL - 08 February 2022 - Creative Commons 4.0 - https://www.ontologyportal.org/ - Chemistry - 1.0 - - - Atomistic - Atomistic Ontology (Atomistic) - An EMMO-based domain ontology for atomistic and electronic modelling. - Francesca L. Bleken, Jesper Friis - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/domain-atomistic - Materials Science and Engineering - Materials Science - 0.0.2 - - - PSIMOD - Protein Modifications Ontology (PSIMOD) - PSI-MOD is an ontology developed by the Proteomics Standards Initiative (PSI) that describes protein chemical modifications, logically linked by an is_a relationship in such a way as to form a direct acyclic graph (DAG). The PSI-MOD ontology has more than 45 top-level nodes, and provides alternative hierarchical paths for classifying protein modifications either by the molecular structure of the modification, or by the amino acid residue that is modified. - OWL - 2022-06-13 - Creative Commons Attribution 4.0 - https://github.com/HUPO-PSI/psi-mod-CV - Chemistry - Protein Modifications - 1.031.6 - - - ChordOntology - Chord Ontology (ChordOntology) - The Chord Ontology is an ontology for describing chords in musical pieces. - Yves Raimond, Samer Abdallah, Centre for Digital Music, Queen Mary, University of London - RDF - 2007-10-25 - Creative Commons 3.0 - https://github.com/motools/chordontology - Arts and Humanities - Musical Works - 1.0 - - - GeoNames - GeoNames Ontology (GeoNames) - The Geonames ontologies provides elements of description for geographical features, in particular those defined in the geonames.org database. - Bernard Vatant - RDF - 2022-01-30 - Creative Commons 3.0 - https://www.geonames.org/ontology - Geography - Geographic Knowledge - 3.3 - - - LPBFO - Laser Powder Bed Fusion Ontology (LPBFO) - The LPBF Ontology can be used to describe the additive manufacturing of a component via Laser Powder Bed Fusion (LPBF) / Selective Laser Melting (SLM). The ontology builds on BFO2.0 and BWMD_mid and has been developed to be used in conjunction with the digital workflows provided by Fraunhofer IWM. If possible, the terminology within this ontology was used as provided by ISO/ASTM 52900:2015. Recently, classes relevant for Life Cycle Analysis (LCA) were added that enable sustainability assessment. - Fraunhofer IWM - OWL - 2022-09-20 - Creative Commons 4.0 - https://matportal.org/ontologies/LPBFO - Materials Science and Engineering - Materials Science - 1.1.9 - - - PPlan - Ontology for Provenance and Plans (P-Plan) - The Ontology for Provenance and Plans (P-Plan) is an extension of the PROV-O ontology [PROV-O] created to represent the plans that guided the execution of scientific processes. P-Plan describes how the plans are composed and their correspondence to provenance records that describe the execution itself. - http://www.isi.edu/~gil/ - OWL - 2014-03-12 - Creative Commons 4.0 - https://vocab.linkeddata.es/p-plan/index.html - Scholarly Knowledge - 1.3 - - - PROV - PROV Ontology (PROV-O) - The PROV Ontology (PROV-O) expresses the PROV Data Model [PROV-DM] using the OWL2 Web Ontology Language (OWL2) [OWL2-OVERVIEW]. It provides a set of classes, properties, and restrictions that can be used to represent and interchange provenance information generated in different systems and under different contexts. It can also be specialized to create new classes and properties to model provenance information for different applications and domains. The PROV Document Overview describes the overall state of PROV, and should be read before other PROV documents. - OWL - 2013-04-30 - W3C Software License - https://terminology.tib.eu/ts/ontologies/PROV - General Knowledge - General - 2013-04-30 - - - QUDV - Quantities, Units, Dimensions and Values (QUDV) - The SysML QUDV (Quantities, Units, Dimensions and Values) modelLibrary is specified in a UML/SysML class/block diagram. In order to generalize its potential usage and alignment with other standardization efforts concerning quantities and units, it is of interest to verify that the QUDV model can also be represented in the form of an ontology using a formal ontology definition language. - SysML - OWL - 2009-10-30 - Apache License 2.0 - https://www.omgwiki.org/OMGSysML/doku.php?id=sysml-qudv:qudv_owl - Units and Measurements - 2009-10-30 - - - DBpedia - DBpedia Ontology (DBpedia) - The DBpedia ontology is generated from the manually created specifications in the DBpedia Mappings Wiki. Each release of this ontology corresponds to a new release of the DBpedia dataset, which contains instance data extracted from various language versions of Wikipedia. The DBpedia ontology has evolved into a crowd-sourced effort, resulting in a shallow cross-domain ontology. - DBpedia Maintainers and Contributors - OWL - 2008-11-17 - Creative Commons 3.0 - https://wiki.dbpedia.org/ - General Knowledge - Knowledge Graph - - - BBCCMS - BBC CMS Ontology (BBCCMS) - The Content Management Systems ontology defines the terms that LDP needs to interact with systems that produce content. The linked data platform contain semantic metadata for the creative content and also the things the BBC produces content about. The CMS ontology defines how these things and content are associated with other BBC instances of the same thing. - LinkedData@bbc.co.uk - TTL - 2012-12-01 - Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/cms-ontology - News and Media - Content Management Systems - 3.7 - - - MatOnto - Material Ontology (MatOnto) - The Material Ontology (MatOnto) is based on the upper level ontology, the BFO. - OWL - https://github.com/EngyNasr/MSE-Benchmark/blob/main/testCases/secondTestCase/MatOnto.owl - Materials Science and Engineering - Scholarly Knowledge - - - CopyrightOnto - Copyright Ontology (CopyrightOnto) - The Copyright Ontology tries to formalise the copyright domain as a way to facilitate automated (or computer-supported) copyright management through the whole content value chain, as it is shaped by copyright law. Therefore, it does not focus just on the last step, end-users permissions to consume content, like many rights languages and ontologies do. - Rhizomik - TTL - 2019-09 + + MusicOntology + Music Ontology (MusicOntology) + The Music Ontology Specification provides main concepts and properties fo describing music (i.e. artists, albums and tracks) on the Semantic Web. + Knowledge Media Institute, Open University + RDF + 2013/07/22 Creative Commons 4.0 - https://rhizomik.net/ontologies/copyrightonto/ - Law - Legal Knowledge - - - AS2 - Activity Streams 2.0 Ontology (AS2) - The Activity Streams 2.0 ontology is a vocabulary for describing social activities and actions. It is based on the Activity Streams 2.0 specification and provides a set of classes and properties for describing activities on the web. - TTL - 23 May 2017 - W3C Document License - https://github.com/w3c/activitystreams?tab=License-1-ov-file#readme - Social Sciences - Social - 2.0 + https://github.com/motools/musicontology + Arts and Humanities + Music Theory + 2.1.5 - - ASMO - Atomistic Simulation Methods Ontology (ASMO) - ASMO is an ontology that aims to define the concepts needed to describe commonly used atomic scale simulation methods, i.e. density functional theory, molecular dynamics, Monte Carlo methods, etc. ASMO uses the Provenance Ontology (PROV-O) to describe the simulation process. + + PODO + Point Defects Ontology (PODO) + PODO focuses on the description of point defects in crystalline materials. https://orcid.org/0000-0001-7564-7990 OWL Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/OCDO/asmo?tab=readme-ov-file#atomistic-simulation-methods-ontology-asmo + https://github.com/OCDO/podo Materials Science and Engineering Materials Science 1.0.0 - - DataCite - DataCite Ontology (DataCite) - The DataCite Ontology (DataCite) is an ontology that enables the metadata properties of the DataCite Metadata Schema Specification (i.e., a list of metadata properties for the accurate and consistent identification of a resource for citation and retrieval purposes) to be described in RDF. - David Shotton, Silvio Peroni - RDF - 15/09/2022 - Creative Commons 4.0 - https://schema.datacite.org/ - Scholarly Knowledge - Metadata - 3.1 - - - ENVO - Environment Ontology (ENVO) - ENVO is an expressive, community ontology which helps humans, machines, and semantic web applications understand environmental entities of all kinds, from microscopic to intergalactic scales. As a FAIR-compliant resource, it promotes interoperability through the concise, controlled description of all things environmental. - Pier Luigi Buttigieg (https://orcid.org/0000-0002-4366-3088) + + DOID + Human Disease Ontology (DOID) + The Disease Ontology has been developed as a standardized ontology for human disease with the purpose of providing the biomedical community with consistent, reusable and sustainable descriptions of human disease terms, phenotype characteristics and related medical vocabulary disease concepts. + The Open Biological and Biomedical Ontology Foundry OWL - 2024-07-01 + 2024-12-18 Creative Commons 1.0 - https://obofoundry.org/ontology/envo.html - Ecology and Environment - Environment, Ecosystems, Habitats - 2024-07-01 + http://purl.obolibrary.org/obo/doid/releases/2024-12-18/doid.owl + Medicine + Human Diseases - - Hydra - Hydra Ontology (Hydra) - Hydra is a lightweight vocabulary to create hypermedia-driven Web APIs. By specifying a number of concepts commonly used in Web APIs it enables the creation of generic API clients. - Hydra W3C Community Group - JSONLD - 13 July 2021 - Creative Commons 4.0 - https://www.hydra-cg.com/spec/latest/core/#references - Web and Internet - Web Development + + OntoCAPE + Ontology of Computer-Aided Process Engineering (OntoCAPE) + OntoCAPE is a large-scale ontology for the domain of Computer Aided Process Engineering (CAPE). Represented in a formal, machine-interpretable ontology language, OntoCAPE captures consensual knowledge of the process engineering domain in a generic way such that it can be reused and shared by groups of people and across software systems. On the basis of OntoCAPE, novel software support for various engineering activities can be developed; possible applications include the systematic management and retrieval of simulation models and design documents, electronic procurement of plant equipment, mathematical modeling, as well as the integration of design data from distributed sources. + RWTH Aachen University + OWL + GNU General Public License. + https://www.avt.rwth-aachen.de/cms/avt/forschung/sonstiges/software/~ipts/ontocape/?lidx=1 + Materials Science and Engineering + Manufacturing + 2.0 GTS @@ -584,114 +69,68 @@ geospatial Information, Geology 1.0 - - TimelineOntology - Timeline Ontology (TimelineOntology) - The Timeline Ontology is centered around the notion of timeline, seen here as a way to identify a temporal backbone. A timeline may support a signal, a video, a score, a work, etc. - Christopher Sutton, Yves Raimond, Matthias Mauch + + GIST + GIST Upper Ontology (GIST) + Gist is Semantic Arts' minimalist upper ontology for the enterprise. It is designed to have the maximum coverage of typical business ontology concepts with the fewest number of primitives and the least amount of ambiguity. + Semantic Arts RDF - 25th October 2007 - Creative Commons 1.0 - https://github.com/motools/timelineontology - Arts and Humanities - Music Theory - 1.0 - - - NCIt - NCI Thesaurus (NCIt) - NCI Thesaurus (NCIt) is a reference terminology that includes broad coverage of the cancer domain, including cancer related diseases, findings and abnormalities. The NCIt OBO Edition aims to increase integration of the NCIt with OBO Library ontologies. NCIt OBO Edition releases should be considered experimental. - OWL - 2023-10-19 + 2024-Feb-27 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/NCIT - Medicine - Cancer, Oncology - 24.04e + https://semanticarts.com/gist + General Knowledge + Upper Ontology + 12.1.0 - - HPOnt - The Heat Pump Ontology (HPOnt) - The Heat Pump Ontology (HPOnt) aims to formalize and represent all the relevant information of Heat Pumps. The HPOnt has been developed as part of the REACT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395. - REACT project team + + PLDO + Planar Defects Ontology (PLDO) + PLDO is an ontology designed to describe planar defects in crystalline materials, such as grain boundaries and stacking faults, with a focus on their atomic-scale structure and properties. + https://orcid.org/0000-0001-7564-7990 OWL - Creative Commons 4.0 - https://react2020.github.io/REACT-ONTOLOGY/HPOnt/index-en.html/ - Materials Science and Engineering - Materials Science - 0.2 - - - GPO - General Process Ontology (GPO) - Basically, this ontology aims to model processes. Processes are holistic perspective elements that transform inputs/educts (matter, energy, information) into output/products (matter, energy, information) with the help of tools (devices, algorithms). They can be decomposed into sub-processes and have predecessor and successor processes. - Simon Stier - TTL Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/General-Process-Ontology/ontology + https://github.com/OCDO/pldo Materials Science and Engineering - Materials Science - - - GND - Gemeinsame Normdatei (GND) - GND stands for Gemeinsame Normdatei (Integrated Authority File) and offers a broad range of elements to describe authorities. The GND originates from the German library community and aims to solve the name ambiguity problem in the library world. - Alexander Haffner - RDF - 2024-08-26 - Creative Commons 1.0 - https://d-nb.info/standards/elementset/gnd - Library and Cultural Heritage - Authority Files - 1.2.0 + Materials Defects + 1.0.0 - - CHAMEO - Characterisation Methodology Domain Ontology (CHAMEO) - An ontology for materials characterization which represents the evolution of the CHADA template in an ontological form, allowing to generate FAIR documentation of Characterisation Experiments and that has been used as a basis for the development of a number of technique-specific or application-specific ontologies in the materials characterisation domain. CHAMEO has been used as a foundation for the definition of the new CHADA template during the CWA. - https://orcid.org/0000-0002-4181-2852, https://orcid.org/0000-0002-5174-8508, https://orcid.org/0000-0002-9668-6961 + + PRIMA + PRovenance Information in MAterials science (PRIMA) + An ontology that captures the provenance information in the materials science domain. + Ahmad Zainul Ihsan, Mehrdad Jalali, Rossella Aversa TTL - 2024-04-12 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/domain-characterisation-methodology + 2024-01-29 + Creative Commons Attribution 3.0 Unported (CC BY 3.0) + https://materials-data-science-and-informatics.github.io/MDMC-NEP-top-level-ontology/PRIMA/complete/ver_2_0/index.html Materials Science and Engineering Materials Science - 1.0.0 + 2.0 - - PODO - Point Defects Ontology (PODO) - PODO focuses on the description of point defects in crystalline materials. - https://orcid.org/0000-0001-7564-7990 + + RO + Relation Ontology (RO) + The Relations Ontology (RO) is a collection of OWL relations (ObjectProperties) intended for use across a wide variety of biological ontologies. OWL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/OCDO/podo - Materials Science and Engineering - Materials Science - 1.0.0 - - - SAREF - Smart Applications REFerence ontology (SAREF) - The Smart Applications REFerence (SAREF) suite of ontologies forms a shared model of consensus intended to enable semantic interoperability between solutions from different providers and among various activity sectors in the Internet of Things (IoT), thus contributing to the development of data spaces. SAREF is published as a set of open standards produced by ETSI Technical Committee Smart Machine-to-Machine communications (SmartM2M). - ETSI Technical Committee Smart Machine-to-Machine communications (SmartM2M) - RDF - 2020-12-31 - https://saref.etsi.org/core/v3.2.1/ - Web and Internet - interoperability - 3.2.1 + 2024-04-24 + CC0 + http://purl.obolibrary.org/obo/ro.owl + General Knowledge + Relations + 2024-04-24 - - OIEModels - Open Innovation Environment Models (OIEModels) - The models module defines models as semiotic signs that stands for an object by resembling or imitating it, in shape or by sharing a similar logical structure. - Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/OIE-Ontologies/ - Materials Science and Engineering - Materials + + ICON + Icon Ontology (ICON) + The ICON ontology deals with high granularity art interpretation. It was developed by conceptualizing Panofsky's theory of levels of interpretation, therefore artworks can be described according to Pre-iconographical, Iconographical and Iconological information. + Knowledge Media Institute + OWL + April 26th, 2024 + Creative Commons 4.0 + https://w3id.org/icon/ontology/ + Arts and Humanities + Art History, Cultural Heritage + 2.1.0 CiTO @@ -706,51 +145,65 @@ Scholarly Communication 2.8.1 - - ONTORULE - Ontology for the Steel Domain (ONTORULE) - This deliverable consists of the ontology developed in ONTORULE for the steel industry use case. It is presented as an attachment to this document as an html document which was generated by SpecGen from the OWL file. The original OWL file is also included. This document describes the different concepts and attributes included in the ontology. For a better understanding of the decisions taken at the time of representing the knowledge in the ontology, the reader is encouraged to also read the document D5.4. - Diego Daz + + MO + Microscopy Ontology (MO) + The Microscopy Ontology (MO) extends the ontological framework of the PMDco. The MO facilitates semantic integration and the interoperable connection of diverse data sources from the fields of microscopy and microanalysis. Consequently, the MO paves the way for new, adaptable data applications and analyses across various experiments and studies + https://orcid.org/0000-0002-3717-7104,https://orcid.org/0000-0002-7094-5371 TTL - 2010-05-31 - N/A - https://raw.githubusercontent.com/ISE-FIZKarlsruhe/mseo.github.io/master/Ontology_files/ONTORULEsteel.ttl + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/materialdigital/microscopy-ontology?tab=readme-ov-file + Biology and Life Sciences + Microscopy + 2.0 + + + GPO + General Process Ontology (GPO) + Basically, this ontology aims to model processes. Processes are holistic perspective elements that transform inputs/educts (matter, energy, information) into output/products (matter, energy, information) with the help of tools (devices, algorithms). They can be decomposed into sub-processes and have predecessor and successor processes. + Simon Stier + TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/General-Process-Ontology/ontology Materials Science and Engineering Materials Science - - DublinCore - Dublin Core Vocabulary (DublinCore) - The Dublin Core Schema is a small set of vocabulary terms that can be used to describe several kinds of resources. Dublin Core Metadata may be used for multiple purposes, from simple resource description, to combining metadata vocabularies of different metadata standards, to providing interoperability for metadata vocabularies in the Linked Data cloud and Semantic Web implementations. - The Dublin Core Metadata Initiative - RDF - February 17, 2017 - Public Domain - https://bioportal.bioontology.org/ontologies/DC - General Knowledge - Metadata + + MDO + Materials Design Ontology (MDO) + MDO is an ontology for materials design field, representing the domain knowledge specifically related to solid-state physics and computational materials science. + Materials Design Division, National Institute for Materials Science (NIMS) + OWL + 2022-08-02 + Creative Commons 4.0 + https://github.com/LiUSemWeb/Materials-Design-Ontology/tree/master/ + Materials Science and Engineering + Materials Design 1.1 - - MicroStructures - EMMO-based ontology for microstructures (MicroStructures) - This is intended to be a domain ontology for metallic microstructures, covering aspects like: composition, particles, both stable (primary) and metastable (precipitates), grains, subgrains, grain boundaries & particle free zones (PFZs), texture, dislocations. The aim is to support both microstructure modelling as well as characterisation. - OWL - https://github.com/jesper-friis/emmo-microstructure + + MatWerk + NFDI MatWerk Ontology (MatWerk) + NFDI-MatWerk aims to establish a digital infrastructure for Materials Science and Engineering (MSE), fostering improved data sharing and collaboration. This repository provides comprehensive documentation for NFDI MatWerk Ontology (MWO) v3.0, a foundational framework designed to structure research data and enhance interoperability within the MSE community. To ensure compliance with top-level ontology standards, MWO v3.0 is aligned with the Basic Formal Ontology (BFO) and incorporates the modular approach of the NFDIcore mid-level ontology, enriching metadata through standardized classes and properties. The MWO addresses key aspects of MSE research data, including the NFDI-MatWerk community structure, covering task areas, infrastructure use cases, projects, researchers, and organizations. It also describes essential NFDI resources, such as software, workflows, ontologies, publications, datasets, metadata schemas, instruments, facilities, and educational materials. Additionally, MWO represents NFDI-MatWerk services, academic events, courses, and international collaborations. As the foundation for the MSE Knowledge Graph, MWO facilitates efficient data integration and retrieval, promoting collaboration and knowledge representation across MSE domains. This digital transformation enhances data discoverability, reusability, and accelerates scientific exchange, innovation, and discoveries by optimizing research data management and accessibility. + Hossein Beygi Nasrabadi, Jörg Waitelonis, Ebrahim Norouzi, Kostiantyn Hubaiev, Harald Sack + TTL + 2025-03-01 + Creative Commons 1.0 + https://github.com/ISE-FIZKarlsruhe/mwo?tab=readme-ov-file Materials Science and Engineering - Microstructure + Research Data, Interoperability + 3.0.0 - - OM - Ontology of Units of Measure (OM) - The Ontology of units of Measure (OM) models concepts and relations important to scientific research. It has a strong focus on units, quantities, measurements, and dimensions. It includes, for instance, common units such as the SI units metre and kilogram, but also units from other systems of units such as the mile or nautical mile. For many application areas it includes more specific units and quantities, such as the unit of the Hubble constant or the quantity vaselife. The following application areas are supported by OM: Geometry; Mechanics; Thermodynamics; Electromagnetism; Fluid mechanics; Chemical physics; Photometry; Radiometry and Radiobiology; Nuclear physics; Astronomy and Astrophysics; Cosmology; Earth science; Meteorology; Material science; Microbiology; Economics; Information technology and Typography. - Hajo Rijgersberg, Don Willems, Jan Top - RDF - June 28, 2024 - Creative Commons 4.0 - https://bioportal.bioontology.org/ontologies/OM - Units and Measurements - 2.0.57 + + BioPAX + Biological Pathways Exchange (BioPAX) + BioPAX is a standard language that aims to enable integration, exchange, visualization and analysis of biological pathway data. Specifically, BioPAX supports data exchange between pathway data groups and thus reduces the complexity of interchange between data formats by providing an accepted standard format for pathway data. + OWL + 16 April 2015 + http://www.biopax.org/ + Biology and Life Sciences + Bioinformatics + 1.0 VIMMP @@ -764,147 +217,156 @@ Materials Science and Engineering Materials Modeling - - BIO - BIO: A vocabulary for biographical information (BIO) - The BIO vocabulary contains terms useful for finding out more about people and their backgrounds and has some cross-over into genealogical information. The approach taken is to describe a person's life as a series of interconnected key events, around which other information can be woven. This vocabulary defines the event framework and supplies a set of core event types that cover many use cases, but it is expected that it will be extended in other vocabularies to suit their needs. The intention of this vocabulary is to describe biographical events of people and this intention carries through to the definitions of the properties and classes which are person-centric rather than neutral. For example the Employment event puts the person being employed as the principal agent in the event rather than the employer. - Ian Davis and David Galbraith + + AGROVOC + AGROVOC Multilingual Thesaurus (AGROVOC) + AGROVOC is a relevant Linked Open Data set about agriculture available for public use and facilitates access and visibility of data across domains and languages. It offers a structured collection of agricultural concepts, terms, definitions and relationships which are used to unambiguously identify resources, allowing standardized indexing processes and making searches more efficient. + Food and Agriculture Organization of the United Nations RDF - 2010-05-10 - Public Domain - https://vocab.org/bio/ - Social Sciences - Biographical Information - 0.1 - - - NMRCV - Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV) - This artefact is an MSI-approved controlled vocabulary primarily developed under COSMOS EU and PhenoMeNal EU governance. The nmrCV is supporting the nmrML XML format with standardized terms. nmrML is a vendor agnostic open access NMR raw data standard. Its primaly role is analogous to the mzCV for the PSI-approved mzML XML format. It uses BFO2.0 as its Top level. This CV was derived from two predecessors (The NMR CV from the David Wishart Group, developed by Joseph Cruz) and the MSI nmr CV developed by Daniel Schober at the EBI. This simple taxonomy of terms (no DL semantics used) serves the nuclear magnetic resonance markup language (nmrML) with meaningful descriptors to amend the nmrML xml file with CV terms. Metabolomics scientists are encouraged to use this CV to annotrate their raw and experimental context data, i.e. within nmrML. The approach to have an exchange syntax mixed of an xsd and CV stems from the PSI mzML effort. The reason to branch out from an xsd into a CV is, that in areas where the terminology is likely to change faster than the nmrML xsd could be updated and aligned, an externally and decentrallised maintained CV can accompensate for such dynamics in a more flexible way. A second reason for this set-up is that semantic validity of CV terms used in an nmrML XML instance (allowed CV terms, position/relation to each other, cardinality) can be validated by rule-based proprietary validators: By means of cardinality specifications and XPath expressions defined in an XML mapping file (an instances of the CvMappingRules.xsd ), one can define what ontology terms are allowed in a specific location of the data model. - Daniel Schober - OWL - 2017-10-19 + August 12, 2024 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/NMRCV - Chemistry - 1.1.0 + https://agroportal.lirmm.fr/ontologies/AGROVOC + Agriculture + Agricultural Knowledge + 2024-04 - - MDS - Materials Data Science Ontology (MDS) - Materials Data Science (MDS) is an ontology encompassing multiple domains relevant to materials science, chemical synthesis and characterizations, photovoltaics and geospatial datasets. The terms used for classes, subclasses and instances are mapped to PMDCo and BFO Ontologies. - SDLE Research Center + + CopyrightOnto + Copyright Ontology (CopyrightOnto) + The Copyright Ontology tries to formalise the copyright domain as a way to facilitate automated (or computer-supported) copyright management through the whole content value chain, as it is shaped by copyright law. Therefore, it does not focus just on the last step, end-users permissions to consume content, like many rights languages and ontologies do. + Rhizomik TTL - 03/24/2024 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://matportal.org/ontologies/MDS - Materials Science and Engineering - Materials Science - 0.3.0.0 + 2019-09 + Creative Commons 4.0 + https://rhizomik.net/ontologies/copyrightonto/ + Law + Legal Knowledge - - TribAIn - Tribology and Artificial Intelligence Ontology (TribAIn) - TribAIn is an ontology for the description of tribological experiments and their results. It is designed to be used in the context of the TribAIn project, which aims to develop a knowledge-based system for the design of tribological systems. - Patricia Kügler - TTL + + FRBRoo + Functional Requirements for Bibliographic Records - object-oriented (FRBRoo) + The FRBRoo (Functional Requirements for Bibliographic Records - object-oriented) initiative is a joint effort of the CIDOC Conceptual Reference Model and Functional Requirements for Bibliographic Records international working groups to establish a formal ontology intended to capture and represent the underlying semantics of bibliographic information and to facilitate the integration, mediation, and interchange of bibliographic and museum information. + RDF + November 2015 Creative Commons 4.0 - https://github.com/snow0815/tribAIn + https://ontome.net/namespace/6#summary Scholarly Knowledge + Bibliographic Records + 2.4 - - PeriodicTable - Periodic Table of the Elements Ontology (PeriodicTable) - PeriodicTable.owl is a representation of the Periodic Table of the Elements in the OWL Web Ontology Language. It provides reference data to support Semantic Web applications in chemistry and related disciplines. - Michael Cook + + CSO + Computer Science Ontology (CSO) + The Computer Science Ontology (CSO) is a large-scale ontology of research areas in computer science. It provides a comprehensive vocabulary of research topics in computing, organized in a hierarchical structure. This class processes the Computer Science Ontology (CSO) with custom hooks for: - Topic-based class detection - superTopicOf relationships - contributesTo relationships + Knowledge Media Institute, Open University OWL - 2004/02/05 - https://www.daml.org/2003/01/periodictable/ + Creative Commons 4.0 + https://cso.kmi.open.ac.uk/home + Scholarly Knowledge + Computer Science + 3.4 + + + DataCite + DataCite Ontology (DataCite) + The DataCite Ontology (DataCite) is an ontology that enables the metadata properties of the DataCite Metadata Schema Specification (i.e., a list of metadata properties for the accurate and consistent identification of a resource for citation and retrieval purposes) to be described in RDF. + David Shotton, Silvio Peroni + RDF + 15/09/2022 + Creative Commons 4.0 + https://schema.datacite.org/ + Scholarly Knowledge + Metadata + 3.1 + + + BBCCMS + BBC CMS Ontology (BBCCMS) + The Content Management Systems ontology defines the terms that LDP needs to interact with systems that produce content. The linked data platform contain semantic metadata for the creative content and also the things the BBC produces content about. The CMS ontology defines how these things and content are associated with other BBC instances of the same thing. + LinkedData@bbc.co.uk + TTL + 2012-12-01 + Creative Commons 4.0 + https://www.bbc.co.uk/ontologies/cms-ontology + News and Media + Content Management Systems + 3.7 + + + FSO + Flow Systems Ontology (FSO) + The Flow Systems Ontology (FSO) is an ontology for describing interconnected systems with material or energy flow connections, and their components. + Ali Kücükavci, Mads Holten Rasmussen, Ville Kukkonen + TTL + 2020-08-06 + Creative Commons 4.0 + https://github.com/alikucukavci/FSO/ Materials Science and Engineering - Periodic Table of Elements - 1.10 + Materials Science + 0.1.0 - - LDO - Line Defect Ontology (LDO) - LDO is an ontology designed to describe line defects in crystalline materials, such as dislocations and disclinations. - https://orcid.org/0000-0001-7564-7990 + + LIFO + Life Ontology (LifO) + The Life Ontology (LifO) is an ontology of the life of organism. LifO represents the life processes of organisms and related entities and relations. LifO is a general purpose ontology that covers the common features associated with different organisms such as unicellular prokaryotes (e.g., E. coli) and multicellular organisms (e.g., human). + Yongqun "Oliver" He (YH) OWL + March 11, 2018 + Creative Commons 4.0 + https://bioportal.bioontology.org/ontologies/LIFO + Biology and Life Sciences + General Purpose + 1.0.17 + + + MDS + Materials Data Science Ontology (MDS) + Materials Data Science (MDS) is an ontology encompassing multiple domains relevant to materials science, chemical synthesis and characterizations, photovoltaics and geospatial datasets. The terms used for classes, subclasses and instances are mapped to PMDCo and BFO Ontologies. + SDLE Research Center + TTL + 03/24/2024 Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/OCDO/ldo + https://matportal.org/ontologies/MDS Materials Science and Engineering - Materials Defects - 1.0.0 + Materials Science + 0.3.0.0 - - BBCBusiness - BBC Business News Ontology (BBCBusiness) - The Business News Ontology describes the concepts that occur in BBC business news. - https://www.bbc.co.uk/blogs/internet/authors/Jeremy_Tarling, https://uk.linkedin.com/in/amaalmohamed + + BBCWildlife + BBC Wildlife Ontology (BBCWildlife) + A simple vocabulary for describing biological species and related taxa. The vocabulary defines terms for describing the names and ranking of taxa, as well as providing support for describing their habitats, conservation status, and behavioural characteristics, etc. + https://www.ldodds.com#me, http://tomscott.name/ TTL - 2014-11-09 + 2013/12/18 Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/business-news-ontology + https://www.bbc.co.uk/ontologies/wildlife-ontology News and Media - Business News - 0.5 - - - SWEET - Semantic Web for Earth and Environment Technology Ontology (SWEET) - The Semantic Web for Earth and Environment Technology Ontology (SWEET) is an investigation in improving discovery and use of Earth science data, through software understanding of the semantics of web resources. SWEET is a collection of ontologies conceptualizing a knowledge space for Earth system science, represented using the web ontology language (OWL). It includes both orthogonal concepts (space, time, Earth realms, physical quantities, etc.) and integrative science knowledge concepts (phenomena, events, etc.). - NASA, JPL, Caltech - OWL - July 14, 2022 - Creative Commons 4.0 - https://bioportal.bioontology.org/ontologies/SWEET - Ecology and Environment - Earth Science, Geoscience - 3.6.0 - - - LexInfo - LexInfo (LexInfo) - LexInfo allows us to associate linguistic information to elements in an ontology with respect to any level of linguistic description and expressivity. LexInfo has been implemented as an OWL ontology and is available together with an API. - RDF - Apache 2.0 - https://lexinfo.net/index.html - Scholarly Knowledge - Linguistics - 3.0 + Wildlife + 1.1 - - NanoMine - NanoMine Ontology (NanoMine) - Polymer Nanocomposites based ontology which enable researchers to develop and test broad-reaching hypotheses about how inter-relationships between different materials processing methods and composition result in specific changes in material properties. + + WiLD + Workflows in Linked Data (WiLD) + Ontology to describe Workflows in Linked Data. + Tobias Käfer TTL - APACHE 2.0 - https://github.com/tetherless-world/nanomine-ontology - Materials Science and Engineering + 2020-06-10 + DBpedia License + https://databus.dbpedia.org/ontologies/purl.org/wild--vocab/2020.06.10-210552 + Scholarly Knowledge Materials Science - - MAMBO - Molecules And Materials Basic Ontology (MAMBO) - MAMBO (Molecules And Materials Basic Ontology) is a domain ontology for molecular materials. Its main targets are: Allowing the retrieval of structured information regarding molecular materials and related applications (i.e. devices based on molecular materials) Supporting the development of new, complex workflows for modelling systems based on molecular materials (computational modelling and data-driven techniques) Integrating data generated via computational simulations and empirical experiments. + + CDCO + Crystallographic Defect Core Ontology (CDCO) + CDCO defines the common terminology shared across all types of crystallographic defects, providing a unified framework for data integration in materials science. + https://orcid.org/0000-0001-7564-7990 OWL - General Public License v3.0 (GPL-3.0) - https://github.com/daimoners/MAMBO - Materials Science and Engineering - Materials Science - - - PRIMA - PRovenance Information in MAterials science (PRIMA) - An ontology that captures the provenance information in the materials science domain. - Ahmad Zainul Ihsan, Mehrdad Jalali, Rossella Aversa - TTL - 2024-01-29 - Creative Commons Attribution 3.0 Unported (CC BY 3.0) - https://materials-data-science-and-informatics.github.io/MDMC-NEP-top-level-ontology/PRIMA/complete/ver_2_0/index.html + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/OCDO/cdco Materials Science and Engineering Materials Science - 2.0 + 1.0.0 Photovoltaics @@ -918,66 +380,66 @@ Materials Science 0.0.1 - - PMDco - The Platform MaterialDigital core ontology (PMDco) - The PMD Core Ontology (PMDco) is a comprehensive framework for representing knowledge that encompasses fundamental concepts from the domains of materials science and engineering (MSE). The PMDco has been designed as a mid-level ontology to establish a connection between specific MSE application ontologies and the domain neutral concepts found in established top-level ontologies. The primary goal of the PMDco is to promote interoperability between diverse domains. - Jannis Grundmann + + TUBES + TUBES System Ontology (TUBES) + The scope of the TUBES System Ontology is to explicitly define interconnected building service system in the AECO industry, their hierarchical subdivisions, structural and functional aspects, and links to spatial entities. As such, TSO supports the effort to represent linkable information in a future semantic web of building data. It has a strong alignment to other ontologies within the W3C community. + Nicolas Pauen + RDF + 2022-02-01 + Creative Commons 4.0 + https://rwth-e3d.github.io/tso/ + Industry + Building Services + 0.3.0 + + + IAO + Information Artifact Ontology (IAO) + The Information Artifact Ontology (IAO) is an ontology of information entities, originally driven by work by the OBI digital entity and realizable information entity branch. OWL - 2025-03-20 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/materialdigital/core-ontology?tab=readme-ov-file + 2022-11-07 + Creative Commons 4.0 + https://terminology.tib.eu/ts/ontologies/IAO + General Knowledge + Information, Data, Knowledge + + + MDSOnto + The Modular Ontology for Materials and Data Science (MDS-Onto) + MDS-Onto is a domain (low) level ontology that describes terms in Materials Data Science. It is divided into six large modules: BuiltEnv, Exposure, Chemistry, Manufacture, Characterization, and Geospatial. Under each module, there are multiple sub-modules such as FTIR, AFM, Chem-Rxn, PV-Module, Accelerated Exposure, etc. + SDLE Research Center + OWL + 2026-02-03 + CC BY-SA 4.0 + https://cwrusdle.bitbucket.io/files/MDS_Onto/index-en.html Materials Science and Engineering - Materials Science - 3.0.0-alpha1 + Materials Data Science + 0.3.1.16 - - DBO - Digital Buildings Ontology (DBO) - The Digital Buildings ontology (DBO) is used by Google to represent structured information about buildings and building-installed equipment. - Google + + IOF + Industrial Ontology Foundry (IOF) + The IOF Core Ontology contains notions found to be common across multiple manufacturing domains. This file is an RDF implementation of these notions. The ontology utilizes the Basic Formal Ontology or BFO as a top-level ontology but also borrows terms from various domain-independent or mid-level ontologies. The purpose of the ontology is to serve as a foundation for ensuring consistency and interoperability across various domain-specific reference ontologies the IOF publishes. + IOF Core Working Group RDF - 02/23/2023 - Apache 2.0 - https://github.com/google/digitalbuildings?tab=readme-ov-file + 2020 + MIT + https://oagi.org/pages/Released-Ontologies Industry - Building Information - 0.0.1 - - - DCAT - Data Catalog Vocabulary (DCAT) - Data Catalog Vocabulary (DCAT) is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web. This document defines the schema and provides examples for its use. DCAT enables a publisher to describe datasets and data services in a catalog using a standard model and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs. This can increase the discoverability of datasets and data services. It also makes it possible to have a decentralized approach to publishing data catalogs and makes federated search for datasets across catalogs in multiple sites possible using the same query mechanism and structure. Aggregated DCAT metadata can serve as a manifest file as part of the digital preservation process. - Digital Enterprise Research Institute (DERI) - RDF - 22 August 2024 - W3C Document License - https://www.w3.org/TR/vocab-dcat-3/ - Scholarly Knowledge - Data Catalogs - 3.0 + Manufacturing + 1.0 - - GEO - Geographical Entities Ontology (GEO) - Geographical Entities Ontology (GEO) is an inventory of geopolitical entities (such as sovereign states and their administrative subdivisions) as well as various geographical regions (including but not limited to the specific ones over which the governments have jurisdiction) - William R Hogan + + OntoKin + Chemical Kinetics Ontology (OntoKin) + OntoKin is an ontology developed for representing chemical kinetic reaction mechanisms. + IEEE OWL - 2019-02-17 + 08 February 2022 Creative Commons 4.0 - https://github.com/mcwdsi/geographical-entity-ontology/blob/master/geo-all.owl - Geography - Geographic Knowledge - - - BioPAX - Biological Pathways Exchange (BioPAX) - BioPAX is a standard language that aims to enable integration, exchange, visualization and analysis of biological pathway data. Specifically, BioPAX supports data exchange between pathway data groups and thus reduces the complexity of interchange between data formats by providing an accepted standard format for pathway data. - OWL - 16 April 2015 - http://www.biopax.org/ - Biology and Life Sciences - Bioinformatics + https://www.ontologyportal.org/ + Chemistry 1.0 @@ -987,241 +449,244 @@ Mark Fox, Megan Katsumi RDF 2018-07-06 - https://enterpriseintegrationlab.github.io/icity/Contact/Contact_1.0/doc/index-en.html - Social Sciences - Social - 1.0 - - - MO - Microscopy Ontology (MO) - The Microscopy Ontology (MO) extends the ontological framework of the PMDco. The MO facilitates semantic integration and the interoperable connection of diverse data sources from the fields of microscopy and microanalysis. Consequently, the MO paves the way for new, adaptable data applications and analyses across various experiments and studies - https://orcid.org/0000-0002-3717-7104,https://orcid.org/0000-0002-7094-5371 - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/materialdigital/microscopy-ontology?tab=readme-ov-file - Biology and Life Sciences - Microscopy - 2.0 - - - EURIO - EUropean Research Information Ontology (EURIO) - EURIO (EUropean Research Information Ontology) conceptualises, formally encodes and makes available in an open, structured and machine-readable format data about resarch projects funded by the EU's framework programmes for research and innovation. - Publications Office of the European Commission - RDF - 2023-10-19 - Creative Commons 4.0 - https://op.europa.eu/de/web/eu-vocabularies/dataset/-/resource?uri=http://publications.europa.eu/resource/dataset/eurio - Scholarly Knowledge - Research Information - 2.4 - - - Common - Common Ontology (Common) - Ontology for the representation of commons elements in the Trias ontology - Jhon Toledo, Miguel Angel García, Oscar Corcho - RDF - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://w3id.org/mobility/trias/common/0.1.0 - Education - Computer Science - 0.1.0 - - - BBCWildlife - BBC Wildlife Ontology (BBCWildlife) - A simple vocabulary for describing biological species and related taxa. The vocabulary defines terms for describing the names and ranking of taxa, as well as providing support for describing their habitats, conservation status, and behavioural characteristics, etc. - https://www.ldodds.com#me, http://tomscott.name/ - TTL - 2013/12/18 - Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/wildlife-ontology - News and Media - Wildlife - 1.1 - - - UO - Units of Measurement Ontology (UO) - Metrical units for use in conjunction with PATO. - KAUST - OWL - 2023-05-25 - Creative Commons 3.0 - https://bioportal.bioontology.org/ontologies/UO - Units and Measurements + https://enterpriseintegrationlab.github.io/icity/Contact/Contact_1.0/doc/index-en.html + Social Sciences + Social + 1.0 - - ChEBI - Chemical Entities of Biological Interest (ChEBI) - Chemical Entities of Biological Interest (ChEBI) is a dictionary of molecular entities focused on ‘small’ chemical compounds. The term ‘molecular entity’ refers to any constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc., identifiable as a separately distinguishable entity. The molecular entities in question are either products of nature or synthetic products used to intervene in the processes of living organisms. ChEBI incorporates an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. + + SchemaOrg + Schema.org Ontology (SchemaOrg) + Schema.org is a collaborative, community activity with a mission to create, maintain, and promote schemas for structured data on the Internet, on web pages, in email messages, and beyond. + Schema.org Community OWL - 01/01/2025 + 2024-11-22 Creative Commons 4.0 - https://www.ebi.ac.uk/chebi/ - Chemistry - Chemical Entities - 239 + https://github.com/schemaorg/schemaorg/blob/main/data/releases/28.1/schemaorg.owl + General Knowledge + Web Development + 28.1 - - GIST - GIST Upper Ontology (GIST) - Gist is Semantic Arts' minimalist upper ontology for the enterprise. It is designed to have the maximum coverage of typical business ontology concepts with the fewest number of primitives and the least amount of ambiguity. - Semantic Arts + + PTO + Product Types Ontology (PTO) + The Product Types Ontology is designed to be used in combination with GoodRelations, a standard vocabulary for the commercial aspects of offers. + Martin Hepp RDF - 2024-Feb-27 + 2025-02-21 + Creative Commons 3.0 + http://www.productontology.org/ + Industry + 1.0 + + + OM + Ontology of Units of Measure (OM) + The Ontology of units of Measure (OM) models concepts and relations important to scientific research. It has a strong focus on units, quantities, measurements, and dimensions. It includes, for instance, common units such as the SI units metre and kilogram, but also units from other systems of units such as the mile or nautical mile. For many application areas it includes more specific units and quantities, such as the unit of the Hubble constant or the quantity vaselife. The following application areas are supported by OM: Geometry; Mechanics; Thermodynamics; Electromagnetism; Fluid mechanics; Chemical physics; Photometry; Radiometry and Radiobiology; Nuclear physics; Astronomy and Astrophysics; Cosmology; Earth science; Meteorology; Material science; Microbiology; Economics; Information technology and Typography. + Hajo Rijgersberg, Don Willems, Jan Top + RDF + June 28, 2024 Creative Commons 4.0 - https://semanticarts.com/gist - General Knowledge - Upper Ontology - 12.1.0 + https://bioportal.bioontology.org/ontologies/OM + Units and Measurements + 2.0.57 - - CDCO - Crystallographic Defect Core Ontology (CDCO) - CDCO defines the common terminology shared across all types of crystallographic defects, providing a unified framework for data integration in materials science. - https://orcid.org/0000-0001-7564-7990 - OWL + + OIEMaterials + Open Innovation Environment Materials (OIEMaterials) + The materials module populates the physicalistic perspective with materials subclasses categorised according to modern applied physical sciences. + Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro + TTL Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/OCDO/cdco + https://github.com/emmo-repo/OIE-Ontologies/ Materials Science and Engineering - Materials Science - 1.0.0 + Materials - - SWO - Software Ontology (SWO) - The Software Ontology (SWO) is a resource for describing software tools, their types, tasks, versions, provenance and associated data. It contains detailed information on licensing and formats as well as software applications themselves, mainly (but not limited) to the bioinformatics community. - Allyson Lister, Andy Brown, Duncan Hull, Helen Parkinson, James Malone, Jon Ison, Nandini Badarinarayan, Robert Stevens + + NCIt + NCI Thesaurus (NCIt) + NCI Thesaurus (NCIt) is a reference terminology that includes broad coverage of the cancer domain, including cancer related diseases, findings and abnormalities. The NCIt OBO Edition aims to increase integration of the NCIt with OBO Library ontologies. NCIt OBO Edition releases should be considered experimental. OWL - 2013-07-01 + 2023-10-19 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/SWO - Scholarly Knowledge - Software - 1.0 + https://terminology.tib.eu/ts/ontologies/NCIT + Medicine + Cancer, Oncology + 24.04e - - FoodOn - Food Ontology (FoodON) - FoodOn, the food ontology, contains vocabulary for naming food materials and their anatomical and taxonomic origins, from raw harvested food to processed food products, for humans and domesticated animals. It provides a neutral and ontology-driven standard for government agencies, industry, nonprofits and consumers to name and reference food products and their components throughout the food supply chain. - OWL - 2025-01-16 - Creative Commons 4.0 - http://purl.obolibrary.org/obo/foodon.owl - Agriculture - Diet, Metabolomics, and Nutrition + + DBO + Digital Buildings Ontology (DBO) + The Digital Buildings ontology (DBO) is used by Google to represent structured information about buildings and building-installed equipment. + Google + RDF + 02/23/2023 + Apache 2.0 + https://github.com/google/digitalbuildings?tab=readme-ov-file + Industry + Building Information + 0.0.1 - - SIO - Semanticscience Integrated Ontology (SIO) - The semanticscience integrated ontology (SIO) provides a simple, integrated upper level ontology (types, relations) for consistent knowledge representation across physical, processual and informational entities. This project provides foundational support for the Bio2RDF (http://bio2rdf.org) and SADI (http://sadiframework.org) projects. - M. Dumontier + + PROV + PROV Ontology (PROV-O) + The PROV Ontology (PROV-O) expresses the PROV Data Model [PROV-DM] using the OWL2 Web Ontology Language (OWL2) [OWL2-OVERVIEW]. It provides a set of classes, properties, and restrictions that can be used to represent and interchange provenance information generated in different systems and under different contexts. It can also be specialized to create new classes and properties to model provenance information for different applications and domains. The PROV Document Overview describes the overall state of PROV, and should be read before other PROV documents. OWL - 03/25/2024 - Creative Commons 4.0 - https://bioportal.bioontology.org/ontologies/SIO - Upper Ontology - Basic - 1.59 - - - CCO - Common Core Ontologies (CCO) - The Common Core Ontologies (CCO) is a widely-used suite of eleven ontologies that consist of logically well-defined generic terms and relations among them reflecting entities across all domains of interest. - TTL - 2024-11-06 - BSD-3-Clause license - https://github.com/CommonCoreOntology/CommonCoreOntologies + 2013-04-30 + W3C Software License + https://terminology.tib.eu/ts/ontologies/PROV General Knowledge General - 2.0 + 2013-04-30 - - BBCPolitics - BBC Politics News Ontology (BBCPolitics) - The Politics Ontology describes the concepts that occur in BBC politics news. - https://www.r4isstatic.com/ - TTL - 2014-01-06 - Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/politics-ontology - News and Media - Politics - 0.9 + + OEO + The Open Energy Ontology (OEO) + The Open Energy Ontology (OEO) is a domain ontology of the energy system analysis context. It is developed as part of the Open Energy Family. The OEO is published on GitHub under an open source license. The language used is the Manchester OWL Syntax, which was chosen because it is user-friendly for editing and viewing differences of edited files. The OEO is constantly being extended. The first version of the OEO has been released on June 11th 2020. A Steering Committee (OEO-SC) was created to accompany the development, increase awareness of the ontology and include it in current projects. + OWL + 03/2025 + Creative Commons Attribution 1.0 Generic (CC BY 1.0) + https://github.com/OpenEnergyPlatform/ontology?tab=readme-ov-file + Ecology and Environment + Energy + 2.7.0 - - BBCSport - BBC Sport Ontology (BBCSport) - The Sport Ontology is a simple lightweight ontology for publishing data about competitive sports events. The terms in this ontology allow data to be published about: The structure of sports tournaments as a series of eventsThe competing of agents in a competitionThe type of discipline a event involvesThe award associated with the competition and how received it...etc Whilst it originates in a specific BBC use case, the Sport Ontology should be applicable to a wide range of competitive sporting events data publishing use cases. Care has been taken to try and ensure interoperability with more general ontologies in use. In particular, it draws heavily upon the events ontology. - https://uk.linkedin.com/pub/jem-rayfield/27/b19/757, https://uk.linkedin.com/in/paulwilton, https://www.blockslabpillar.com, https://www.linkedin.com/in/tfgrahame, https://uk.linkedin.com/pub/stuart-williams/8/684/351, https://uk.linkedin.com/in/brianwmcbride + + BBC + BBC Ontology (BBC) + The BBC ontology codifies the logic that connects web documents, BBC products and platforms for which content is available. Currently, there are 10 major products in Future Media which produce content for BBC online. The majority of those contain more products dedicated in thematic areas, for example Education propositions are part of the K&L (Knowledge and Learning) product portfolio. + LinkedData@bbc.co.uk TTL + 2012-12-01 Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/sport-ontology + https://www.bbc.co.uk/ontologies/bbc-ontology/ News and Media - Sport - 3.2 + News + 1.37 - - BBCFood - BBC Food Ontology (BBCFood) - The Food Ontology is a simple lightweight ontology for publishing data about recipes, including the foods they are made from and the foods they create as well as the diets, menus, seasons, courses and occasions they may be suitable for. Whilst it originates in a specific BBC use case, the Food Ontology should be applicable to a wide range of recipe data publishing across the web. + + VIBSO + Vibrational Spectroscopy Ontology (VIBSO) + The Vibration Spectroscopy Ontology defines technical terms with which research data produced in vibrational spectroscopy experiments can be semantically enriched, made machine readable and FAIR. + VIBSO Workgroup + OWL + 2024-09-23 + Creative Commons Attribution 4.0 + https://terminology.tib.eu/ts/ontologies/vibso + Chemistry + Spectroscopy + 2024-09-23 + + + BBCCreativeWork + BBC Creative Work Ontology (BBCCreativeWork) + This ontology defines the terms required to describe the creative works produced by the BBC and their associated metadata. This ontology powers reading and writing creative works in the triplestore using tags associated with them (about) their more specific types (BlogPost, NewsItem, Programme) and audiences (audience). + LinkedData@bbc.co.uk TTL - 2014/03/18 + 2012-12-01 Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/food-ontology + https://www.bbc.co.uk/ontologies/creative-work-ontology News and Media - Food and Beverage - 0.1 + Creative Work + 1.19 - - SPDocument - SMART Protocols Ontology: Document Module (SP-Document) - SMART Protocols Ontology: Document Module is an ontology designed to represent metadata used to report an experimental protocol. - http://oxgiraldo.wordpress.com + + NFDIcore + National Research Data Infrastructure Ontology (NFDIcore) + The National Research Data Infrastructure (NFDI) initiative has led to the formation of various consortia, each focused on developing a research data infrastructure tailored to its specific domain. To ensure interoperability across these consortia, the NFDIcore ontology has been developed as a mid-level ontology for representing metadata related to NFDI resources, including individuals, organizations, projects, data portals, and more. + Jörg Waitelonis, Oleksandra Bruns, Tabea Tietz, Etienne Posthumus, Hossein Beygi Nasrabadi, Harald Sack OWL - 2013-07-01 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/SMARTProtocols/SMART-Protocols + 2025-02-07 + Creative Commons 1.0 + https://ise-fizkarlsruhe.github.io/nfdicore/ Scholarly Knowledge - Materials Science - 4.0 + Research Data Infrastructure + 3.0.0 - - AIISO - Academic Institution Internal Structure Ontology (AIISO) - The Academic Institution Internal Structure Ontology (AIISO) provides classes and properties to describe the internal organizational structure of an academic institution. AIISO is designed to work in partnership with Participation (http://purl.org/vocab/participation/schema), FOAF (http://xmlns.com/foaf/0.1/) and aiiso-roles (http://purl.org/vocab/aiiso-roles/schema) to describe the roles that people play within an institution. - Open University - RDF - 2008-05-14 + + PreMOn + Pre-Modern Ontology (PreMOn) + The PreMOn Ontology is an extension of lemon (W3C Ontology Lexicon Community Group, 2015) for representing predicate models and their mappings. The Core Module of the PreMOn Ontology defines the main abstractions for modelling semantic classes with their semantic roles, mappings between different predicate models, and annotations. + Francesco Corcoglioniti, Marco Rospocher <https://dkm.fbk.eu/rospocher> + OWL + 2018-02-15 Creative Commons 4.0 - https://vocab.org/aiiso/ + https://premon.fbk.eu/ontology/core# Scholarly Knowledge - Academic Institution - 1.0 + Linguistics + 2018a + + + ASMO + Atomistic Simulation Methods Ontology (ASMO) + ASMO is an ontology that aims to define the concepts needed to describe commonly used atomic scale simulation methods, i.e. density functional theory, molecular dynamics, Monte Carlo methods, etc. ASMO uses the Provenance Ontology (PROV-O) to describe the simulation process. + https://orcid.org/0000-0001-7564-7990 + OWL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/OCDO/asmo?tab=readme-ov-file#atomistic-simulation-methods-ontology-asmo + Materials Science and Engineering + Materials Science + 1.0.0 - - FIX - FIX Ontology (FIX) - An ontology of physico-chemical methods and properties. + + DISO + Dislocation Ontology (DISO) + DISO is an ontology that defines the linear defect, in particular dislocation concepts and relations between them in crystalline materials. + Ahmad Zainul Ihsan OWL - 2020-04-13 - https://terminology.tib.eu/ts/ontologies/FIX - Chemistry - Chemicals, Properties - 2020-04-13 + 21.03.202 + Creative Commons Attribution 3.0 International (CC BY 3.0) + https://github.com/Materials-Data-Science-and-Informatics/dislocation-ontology + Materials Science and Engineering + Materials Science + 1.0 - - BattINFO - Battery Interface Ontology (BattINFO) - BattINFO is a foundational resource for harmonizing battery knowledge representation and enhancing data interoperability. The primary objective is to provide the necessary tools to create FAIR (Findable, Accessible, Interoperable, Reusable) battery data that can be integrated into the Semantic Web. + + NanoMine + NanoMine Ontology (NanoMine) + Polymer Nanocomposites based ontology which enable researchers to develop and test broad-reaching hypotheses about how inter-relationships between different materials processing methods and composition result in specific changes in material properties. TTL - https://github.com/BIG-MAP/BattINFO + APACHE 2.0 + https://github.com/tetherless-world/nanomine-ontology Materials Science and Engineering Materials Science + + BTO + BRENDA Tissue Ontology (BTO) + A structured controlled vocabulary for the source of an enzyme comprising tissues, cell lines, cell types and cell cultures. + OWL + 2021-10-26 + Creative Commons 4.0 + https://terminology.tib.eu/ts/ontologies/BTO + Medicine + Enzyme + 2021-10-26 + + + GEO + Geographical Entities Ontology (GEO) + Geographical Entities Ontology (GEO) is an inventory of geopolitical entities (such as sovereign states and their administrative subdivisions) as well as various geographical regions (including but not limited to the specific ones over which the governments have jurisdiction) + William R Hogan + OWL + 2019-02-17 + Creative Commons 4.0 + https://github.com/mcwdsi/geographical-entity-ontology/blob/master/geo-all.owl + Geography + Geographic Knowledge + + + Hydra + Hydra Ontology (Hydra) + Hydra is a lightweight vocabulary to create hypermedia-driven Web APIs. By specifying a number of concepts commonly used in Web APIs it enables the creation of generic API clients. + Hydra W3C Community Group + JSONLD + 13 July 2021 + Creative Commons 4.0 + https://www.hydra-cg.com/spec/latest/core/#references + Web and Internet + Web Development + MOLTENSILE Matolab Tensile Test Ontology (MOL_TENSILE) @@ -1247,18 +712,110 @@ Chemistry, Molecular Biology 2022-05-11 - - EDAM - The ontology of data analysis and management (EDAM) - EDAM is a domain ontology of data analysis and data management in bio- and other sciences, and science-based applications. It comprises concepts related to analysis, modelling, optimisation, and data life cycle. Targetting usability by diverse users, the structure of EDAM is relatively simple, divided into 4 main sections: Topic, Operation, Data (incl. Identifier), and Format. - Federico Bianchini, Hervé Ménager, Jon Ison, Matúš Kalaš + + Atomistic + Atomistic Ontology (Atomistic) + An EMMO-based domain ontology for atomistic and electronic modelling. + Francesca L. Bleken, Jesper Friis + TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/domain-atomistic + Materials Science and Engineering + Materials Science + 0.0.2 + + + GND + Gemeinsame Normdatei (GND) + GND stands for Gemeinsame Normdatei (Integrated Authority File) and offers a broad range of elements to describe authorities. The GND originates from the German library community and aims to solve the name ambiguity problem in the library world. + Alexander Haffner + RDF + 2024-08-26 + Creative Commons 1.0 + https://d-nb.info/standards/elementset/gnd + Library and Cultural Heritage + Authority Files + 1.2.0 + + + OIEModels + Open Innovation Environment Models (OIEModels) + The models module defines models as semiotic signs that stands for an object by resembling or imitating it, in shape or by sharing a similar logical structure. + Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro + TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/OIE-Ontologies/ + Materials Science and Engineering + Materials + + + EMMOCrystallography + Crystallography Ontology (EMMOCrystallography) + A crystallography domain ontology based on EMMO and the CIF core dictionary. It is implemented as a formal language. + TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/domain-crystallography + Materials Science and Engineering + Crystallography + 0.0.1 + + + OWLTime + Time Ontology in OWL (OWL-Time) + OWL-Time is an OWL-2 DL ontology of temporal concepts, for describing the temporal properties of resources in the world or described in Web pages. The ontology provides a vocabulary for expressing facts about topological (ordering) relations among instants and intervals, together with information about durations, and about temporal position including date-time information. Time positions and durations may be expressed using either the conventional (Gregorian) calendar and clock, or using another temporal reference system such as Unix-time, geologic time, or different calendars. + World Wide Web Consortium + TTL + 15 November 2022 + W3C Software Notice and Document License + https://www.w3.org/TR/owl-time/ + Units and Measurements + Temporal Reasoning + 1.0 + + + MSLE + Material Science Lab Equipment Ontology (MSLE) + The current ontology describes Material Science Lab Equipment. + TTL + Sep 15, 2022 + https://github.com/MehrdadJalali-AI/MSLE-Ontology + Materials Science and Engineering + Materials Science + 1.1 + + + MatOnto + Material Ontology (MatOnto) + The Material Ontology (MatOnto) is based on the upper level ontology, the BFO. OWL - 24.09.2024 - Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/edam + https://github.com/EngyNasr/MSE-Benchmark/blob/main/testCases/secondTestCase/MatOnto.owl + Materials Science and Engineering + Scholarly Knowledge + + + SPDocument + SMART Protocols Ontology: Document Module (SP-Document) + SMART Protocols Ontology: Document Module is an ontology designed to represent metadata used to report an experimental protocol. + http://oxgiraldo.wordpress.com + OWL + 2013-07-01 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/SMARTProtocols/SMART-Protocols + Scholarly Knowledge + Materials Science + 4.0 + + + DBpedia + DBpedia Ontology (DBpedia) + The DBpedia ontology is generated from the manually created specifications in the DBpedia Mappings Wiki. Each release of this ontology corresponds to a new release of the DBpedia dataset, which contains instance data extracted from various language versions of Wikipedia. The DBpedia ontology has evolved into a crowd-sourced effort, resulting in a shallow cross-domain ontology. + DBpedia Maintainers and Contributors + OWL + 2008-11-17 + Creative Commons 3.0 + https://wiki.dbpedia.org/ General Knowledge - General - 1.25-20240924T0027Z-unstable(1.26) + Knowledge Graph Metadata4Ing @@ -1273,81 +830,91 @@ Materials Science 1.3.1 - - SIOC - Semantically-Interlinked Online Communities (SIOC) - The SIOC (Semantically-Interlinked Online Communities) Ontology is an ontology for describing the information in online communities. This includes sites that support online discussions, blogging, file sharing, photo sharing, social networking, etc. - Data Science Institute, NUI Galway + + iCalendar + iCalendar Vocabulary (iCalendar) + iCalendar is an Internet standard for exchanging calendar and scheduling data across different applications and platforms using a standardized text-based format (.ics). It enables interoperability for events, tasks, and scheduling, supporting features like recurring events, invitations, and time zone adjustments. While widely used in applications like Google Calendar and Outlook, its complexity and partial implementations pose challenges, leading to efforts to integrate it with Semantic Web technologies for enhanced data linking and automation. + Dan Connolly, W3C, Libby Miller, ASemantics RDF - 2018/02/28 - Creative Commons 3.0 - http://rdfs.org/sioc/spec/ - Social Sciences - Social Networks - 1.36 - - - Conference - Conference Ontology (Conference) - The conference-ontology is a new self-contained ontology for modeling knowledge about conferences. The conference-ontology adopts the best ontology design practices (e.g., Ontology Design Patterns, ontology reuse and interlinking) and guarantees interoperability with SWC ontology and all other pertinent vocabularies. - Aldo Gangemi et al. - OWL - 2016/04/30 - Creative Commons 3.0 - http://www.scholarlydata.org/ontology/conference-ontology.owl + 2004/04/07 + Open Publication License + https://www.w3.org/2002/12/cal/ Events - Conferences + Calendar and Scheduling + 1.14 - - OEO - The Open Energy Ontology (OEO) - The Open Energy Ontology (OEO) is a domain ontology of the energy system analysis context. It is developed as part of the Open Energy Family. The OEO is published on GitHub under an open source license. The language used is the Manchester OWL Syntax, which was chosen because it is user-friendly for editing and viewing differences of edited files. The OEO is constantly being extended. The first version of the OEO has been released on June 11th 2020. A Steering Committee (OEO-SC) was created to accompany the development, increase awareness of the ontology and include it in current projects. - OWL - 03/2025 - Creative Commons Attribution 1.0 Generic (CC BY 1.0) - https://github.com/OpenEnergyPlatform/ontology?tab=readme-ov-file - Ecology and Environment - Energy - 2.7.0 + + Wine + Wine Ontology (Wine) + A project to define an RDF style ontology for wines and the wine-industry + RDF + https://github.com/UCDavisLibrary/wine-ontology + Food and Beverage + Wine - - SSN - Semantic Sensor Network Ontology (SSN) - The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators. SSN follows a horizontal and vertical modularization architecture by including a lightweight but self-contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) for its elementary classes and properties. With their different scope and different degrees of axiomatization, SSN and SOSA are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things. Both ontologies are described below, and examples of their usage are given. - W3C/OGC Spatial Data on the Web Working Group - TTL - 2017-04-17 - http://www.w3.org/Consortium/Legal/2015/copyright-software-and-document - https://github.com/w3c/sdw-sosa-ssn/tree/482484fe2edc1ba8aa7f19214a72bdb77123e833 + + PeriodicTable + Periodic Table of the Elements Ontology (PeriodicTable) + PeriodicTable.owl is a representation of the Periodic Table of the Elements in the OWL Web Ontology Language. It provides reference data to support Semantic Web applications in chemistry and related disciplines. + Michael Cook + OWL + 2004/02/05 + https://www.daml.org/2003/01/periodictable/ Materials Science and Engineering - Sensor Networks + Periodic Table of Elements + 1.10 + + + MarineTLO + Marine Taxonomy and Life Ontology (MarineTLO) + MarineTLO is a top level ontology, generic enough to provide consistent abstractions or specifications of concepts included in all data models or ontologies of marine data sources and provide the necessary properties to make this distributed knowledge base a coherent source of facts relating observational data with the respective spatiotemporal context and categorical (systematic) domain knowledge. It can be used as the core schema for publishing Linked Data, as well as for setting up integration systems for the marine domain. It can be extended to any level of detail on demand, while preserving monotonicity. For its development and evolution we have adopted an iterative and incremental methodology where a new version is released every two months. For the implementation we use OWL 2, and to evaluate it we use a set of competency queries, formulating the domain requirements provided by the related communities. + Information System Laboratory (ISL), Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH) + OWL + 2017-01-05 + Creative Commons 4.0 + https://projects.ics.forth.gr/isl/MarineTLO/ + Biology and Life Sciences + Marine Science, Oceanography 1.0 - - MMO - Materials Mechanics Ontology (MMO) - The materials mechanics ontology is an application-level ontology that was created for supporting named entity recognition tasks for materials fatigue domain. The ontology covers some fairly general MSE concepts that could prospectively be merged into PMDco or other upper materials ontologies such as descriptions of crystallographic defects and microstructural entities. Furthermore, concepts related to the materials fatigue subdomain are also heavily incorporated. - Akhil Thomas, Ali Riza Durmaz + + VOAF + Vocabulary of a Friend (VOAF) + The Vocabulary of a Friend (VOAF) is a vocabulary specification providing elements allowing the description of vocabularies (RDFS vocabularies or OWL ontologies). It is based on Dublin Core and VOID. + Bernard Vatant RDF - 2024-01-30 - Creative Commons 4.0 - https://iwm-micro-mechanics-public.pages.fraunhofer.de/ontologies/materials-mechanics-ontology/index-en.html - Materials Science and Engineering + 2013-05-24 + Creative Commons 3.0 + https://lov.linkeddata.es/vocommons/voaf/v2.3/ Scholarly Knowledge - 1.0.1 + Social Network + 2.3 - - BBCProgrammes - BBC Programmes Ontology (BBCProgrammes) - This ontology aims at providing a simple vocabulary for describing programmes. It covers brands, series (seasons), episodes, broadcast events, broadcast services,etc. Its development was funded by the BBC, and is heavily grounded on previous programmes data modelling work done there. - https://moustaki.org/foaf.rdf#moustaki + + QUDT + Quantities, Units, Dimensions and Data Types (QUDT) + QUDT is an advocate for the development and implementation of standards to quantify data expressed in RDF and JSON. + NASA Ames Research Center TTL - 2009/02/20 + March 1, 2022 Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/programmes-ontology - News and Media - Programmes - 1.1 + https://qudt.org/ + Units and Measurements + Physics + 2.1 + + + BIO + BIO: A vocabulary for biographical information (BIO) + The BIO vocabulary contains terms useful for finding out more about people and their backgrounds and has some cross-over into genealogical information. The approach taken is to describe a person's life as a series of interconnected key events, around which other information can be woven. This vocabulary defines the event framework and supplies a set of core event types that cover many use cases, but it is expected that it will be extended in other vocabularies to suit their needs. The intention of this vocabulary is to describe biographical events of people and this intention carries through to the definitions of the properties and classes which are person-centric rather than neutral. For example the Employment event puts the person being employed as the principal agent in the event rather than the employer. + Ian Davis and David Galbraith + RDF + 2010-05-10 + Public Domain + https://vocab.org/bio/ + Social Sciences + Biographical Information + 0.1 FAIR @@ -1359,76 +926,220 @@ Upper Ontology Data, Metadata - - EXPO - Ontology of Scientific Experiments (EXPO) - Formalise generic knowledge about scientific experimental design, methodology, and results representation. + + ENM + Environmental Noise Measurement Ontology (ENM) + The eNanoMapper project (https://www.enanomapper.net/), NanoCommons project (https://www.nanocommons.eu/) and ACEnano project (http://acenano-project.eu/) are creating a pan-European computational infrastructure for toxicological data management for ENMs, based on semantic web standards and ontologies. This ontology is an application ontology targeting the full domain of nanomaterial safety assessment. It re-uses several other ontologies including the NPO, CHEMINF, ChEBI, and ENVO. + eNanoMapper Consortium OWL - Academic Free License (AFL) - https://expo.sourceforge.net/ - Scholarly Knowledge - Scientific Experiments + 2025-02-17 + Creative Commons 3.0 + https://terminology.tib.eu/ts/ontologies/ENM + Medicine + Material Science and Engineering + 10.0 - - ICON - Icon Ontology (ICON) - The ICON ontology deals with high granularity art interpretation. It was developed by conceptualizing Panofsky's theory of levels of interpretation, therefore artworks can be described according to Pre-iconographical, Iconographical and Iconological information. - Knowledge Media Institute + + LDO + Line Defect Ontology (LDO) + LDO is an ontology designed to describe line defects in crystalline materials, such as dislocations and disclinations. + https://orcid.org/0000-0001-7564-7990 OWL - April 26th, 2024 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/OCDO/ldo + Materials Science and Engineering + Materials Defects + 1.0.0 + + + AS2 + Activity Streams 2.0 Ontology (AS2) + The Activity Streams 2.0 ontology is a vocabulary for describing social activities and actions. It is based on the Activity Streams 2.0 specification and provides a set of classes and properties for describing activities on the web. + TTL + 23 May 2017 + W3C Document License + https://github.com/w3c/activitystreams?tab=License-1-ov-file#readme + Social Sciences + Social + 2.0 + + + BBCSport + BBC Sport Ontology (BBCSport) + The Sport Ontology is a simple lightweight ontology for publishing data about competitive sports events. The terms in this ontology allow data to be published about: The structure of sports tournaments as a series of eventsThe competing of agents in a competitionThe type of discipline a event involvesThe award associated with the competition and how received it...etc Whilst it originates in a specific BBC use case, the Sport Ontology should be applicable to a wide range of competitive sporting events data publishing use cases. Care has been taken to try and ensure interoperability with more general ontologies in use. In particular, it draws heavily upon the events ontology. + https://uk.linkedin.com/pub/jem-rayfield/27/b19/757, https://uk.linkedin.com/in/paulwilton, https://www.blockslabpillar.com, https://www.linkedin.com/in/tfgrahame, https://uk.linkedin.com/pub/stuart-williams/8/684/351, https://uk.linkedin.com/in/brianwmcbride + TTL Creative Commons 4.0 - https://w3id.org/icon/ontology/ - Arts and Humanities - Art History, Cultural Heritage - 2.1.0 + https://www.bbc.co.uk/ontologies/sport-ontology + News and Media + Sport + 3.2 - - MatWerk - NFDI MatWerk Ontology (MatWerk) - NFDI-MatWerk aims to establish a digital infrastructure for Materials Science and Engineering (MSE), fostering improved data sharing and collaboration. This repository provides comprehensive documentation for NFDI MatWerk Ontology (MWO) v3.0, a foundational framework designed to structure research data and enhance interoperability within the MSE community. To ensure compliance with top-level ontology standards, MWO v3.0 is aligned with the Basic Formal Ontology (BFO) and incorporates the modular approach of the NFDIcore mid-level ontology, enriching metadata through standardized classes and properties. The MWO addresses key aspects of MSE research data, including the NFDI-MatWerk community structure, covering task areas, infrastructure use cases, projects, researchers, and organizations. It also describes essential NFDI resources, such as software, workflows, ontologies, publications, datasets, metadata schemas, instruments, facilities, and educational materials. Additionally, MWO represents NFDI-MatWerk services, academic events, courses, and international collaborations. As the foundation for the MSE Knowledge Graph, MWO facilitates efficient data integration and retrieval, promoting collaboration and knowledge representation across MSE domains. This digital transformation enhances data discoverability, reusability, and accelerates scientific exchange, innovation, and discoveries by optimizing research data management and accessibility. - Hossein Beygi Nasrabadi, Jörg Waitelonis, Ebrahim Norouzi, Kostiantyn Hubaiev, Harald Sack + + MSEO + Materials Science and Engineering Ontology (MSEO) + MSEO utilizes the IOF Ontology stack giving materials scientists and engineers the ability to represent their experiments and resulting data. The goal is to create machine and human readable sematic data which can be easily digested by other science domains. It is a product of the joint venture Materials Open Lab Project between the Bundesanstalt für Materialforschung und -prüfung (BAM) and the Fraunhofer Group MATERIALS and uses the BWMD ontology created by Fraunhofer IWM as a starting point. + Thomas Hanke, Fraunhofer IWM + TTL + MIT License + https://github.com/Mat-O-Lab/MSEO + Materials Science and Engineering + Materials Science + + + BVCO + Battery Value Chain Ontology (BVCO) + Basically, Battery Value Chain Ontology (BVCO) aims to model processes along the Battery value chain. Processes are holistic perspective elements that transform inputs/educts (matter, energy, information) into output/products (matter, energy, information) with the help of tools (devices, algorithms). They can be decomposed into sub-processes and have predecessor and successor processes. + Lukas Gold, Simon Stier TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/Battery-Value-Chain-Ontology/ontology + Materials Science and Engineering + Materials Science + 0.4.3 + + + CMSO + Computational Material Sample Ontology (CMSO) + CMSO is an ontology that aims to describe computational materials science samples (or structures), including crystalline defects. Initially focusing on the description at the atomic scale. + https://orcid.org/0000-0001-7564-7990 + OWL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/OCDO/cmso/tree/main + Materials Science and Engineering + Materials Science + 0.0.1 + + + PKO + Provenance Knowledge Ontology (PKO) + Procedural Knowledge (PK) is knowing how to perform some tasks, as opposed to descriptive/declarative knowledge, which is knowing what in terms of facts and notions. In industry, PK refers in general to structured processes to be followed, and can be related to both production (e.g., procedure on the production line in a plant) and services (e.g., procedure for troubleshooting during customer support); to specific technical expertise (e.g., procedure to set up a specific machine) and general regulations and best practices (e.g., safety procedures, activities to minimise environmental impact). + Mario Scrocca (Cefriel), Valentina Carriero (Cefriel) + RDF 2025-03-01 - Creative Commons 1.0 - https://github.com/ISE-FIZKarlsruhe/mwo?tab=readme-ov-file + Creative Commons 4.0 + https://github.com/perks-project/pk-ontology/tree/master + Industry + Provenance + 1.0.0 + + + OIESoftware + Open Innovation Environment Software (OIESoftware) + EMMO-compliant, domain-level OIE ontology tackling the areas of software products. + Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro + TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/OIE-Ontologies/ Materials Science and Engineering - Research Data, Interoperability - 3.0.0 + Materials + 0.1 - - DOAP - The Description of a Project vocabulary (DOAP) - The Description of a Project vocabulary (DOAP), described using W3C RDF Schema and the Web Ontology Language to describe software projects, and in particular open source projects. - Edd Wilder-James + + SPWorkflow + SMART Protocols Ontology: Workflow Module (SP-Workflow) + SP-Workflow module represents: i) the executable elements of a protocol; ii) the experimental actions and material entities that participates in instructions (sample/specimen, organisms, reagents, instruments); and iii) the order of execution of the instructions. + http://oxgiraldo.wordpress.com + OWL + 2013-07-01 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/SMARTProtocols/SMART-Protocols + Scholarly Knowledge + Workflows + 4.0 + + + ChMO + Chemical Methods Ontology (ChMO) + The Chemical Methods Ontology contains more than 3000 classes and describes methods used to: - collect data in chemical experiments, such as mass spectrometry and electron microscopy. - prepare and separate material for further analysis, such as sample ionisation, chromatography, and electrophoresis - synthesise materials, such as epitaxy and continuous vapour deposition It also describes the instruments used in these experiments, such as mass spectrometers and chromatography columns and their outputs. + OWL + 2022-04-19 + Creative Commons 4.0 + https://github.com/rsc-ontologies/rsc-cmo + Chemistry + + + UMBEL + Upper Mapping and Binding Exchange Layer Vocabulary (UMBEL) + UMBEL is the Upper Mapping and Binding Exchange Layer, designed to help content interoperate on the Web. UMBEL provides two valuable functions: First, it is a broad, general reference structure of 34,000 concepts, which provides a scaffolding to link and interoperate other datasets and domain vocabularies. Second, it is a base vocabulary for the construction of other concept-based domain ontologies, also designed for interoperation. + n3 + May 10, 2016 + https://github.com/structureddynamics/UMBEL/tree/master/Ontology + General Knowledge + Web Development + 1.50 + + + PROCO + PROcess Chemistry Ontology (PROCO) + PROCO (PROcess Chemistry Ontology) is a formal ontology that aims to standardly represent entities and relations among entities in the domain of process chemistry. + Anna Dun, Wes A. Schafer, Yongqun "Oliver" He (YH), Zachary Dance + OWL + 04-14-2022 + Creative Commons 4.0 + https://github.com/proco-ontology/PROCO + Chemistry + Chemicals, Processes + 04-14-2022 + + + AUTO + Automotive Ontology (AUTO) + The AUTOMOTIVE ONTOLOGY (AUTO) defines the shared conceptual structures in the automotive industry. It is an OWL ontology. It is built upon the auto schema.org extension created by the W3C Automotive Ontology Community Group. AUTO's development process follows the best practices established by the EDMC FIBO Community. + EDM Council RDF - 2020-04-03 - Apache License 2.0 - https://github.com/ewilderj/doap/blob/master/schema/doap.rdf + 2021-03-01 + MIT + https://github.com/edmcouncil/auto/tree/master Industry - Software + Automotive - - OPMW - Open Provenance Model for Workflows (OPMW) - The Open Provenance Model for Workflows (OPMW) is an ontology for describing workflow traces and their templates based on the Open Provenance Model. It has been designed as a profile for OPM, extending and reusing OPM's core ontologies OPMV (OPM-Vocabulary) and OPMO (OPM-Ontology). - http://delicias.dia.fi.upm.es/members/DGarijo/#me, http://www.isi.edu/~gil/ - OWL - 2014-12-22 - Creative Commons Attribution 2.0 Generic (CC BY 2.0) - https://www.opmw.org/model/OPMW_20141222/ - Scholarly Knowledge - Workflows - 3.1 + + LODE + Linking Open Descriptions of Events (LODE) + People conventionally refer to an action or occurrence taking place at a certain time at a specific location as an event. This notion is potentially useful for connecting individual facts recorded in the rapidly growing collection of linked data sets and for discovering more complex relationships between data. The LODE provide an overview and comparison of existing event models, looking at the different choices they make of how to represent events. It is a model for publishing records of events as Linked Data. A tools for populating this model and a prototype “event directory” web service, which can be used to locate stable URIs for events that have occurred, provide RDFS+OWL descriptions and link to related resources. + Ryan Shaw + RDF + 2020-10-31 + Creative Commons Attribution 3.0 + https://linkedevents.org/ontology/ + Events + 2020-10-31 - - GFO - General Formal Ontology (GFO) - The General Formal Ontology is a top-level ontology for conceptual modeling, which is being constantly further developed by Onto-Med. It includes elaborations of categories like objects, processes, time and space, properties, relations, roles, functions, facts, and situations. Moreover, we are working on an integration with the notion of levels of reality in order to more appropriately capture entities in the material, mental, and social areas. + + SIO + Semanticscience Integrated Ontology (SIO) + The semanticscience integrated ontology (SIO) provides a simple, integrated upper level ontology (types, relations) for consistent knowledge representation across physical, processual and informational entities. This project provides foundational support for the Bio2RDF (http://bio2rdf.org) and SADI (http://sadiframework.org) projects. + M. Dumontier OWL - 2024-11-18 + 03/25/2024 Creative Commons 4.0 - https://onto-med.github.io/GFO/release/2024-11-18/index-en.html + https://bioportal.bioontology.org/ontologies/SIO Upper Ontology + Basic + 1.59 + + + MicroStructures + EMMO-based ontology for microstructures (MicroStructures) + This is intended to be a domain ontology for metallic microstructures, covering aspects like: composition, particles, both stable (primary) and metastable (precipitates), grains, subgrains, grain boundaries & particle free zones (PFZs), texture, dislocations. The aim is to support both microstructure modelling as well as characterisation. + OWL + https://github.com/jesper-friis/emmo-microstructure + Materials Science and Engineering + Microstructure + + + DCAT + Data Catalog Vocabulary (DCAT) + Data Catalog Vocabulary (DCAT) is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web. This document defines the schema and provides examples for its use. DCAT enables a publisher to describe datasets and data services in a catalog using a standard model and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs. This can increase the discoverability of datasets and data services. It also makes it possible to have a decentralized approach to publishing data catalogs and makes federated search for datasets across catalogs in multiple sites possible using the same query mechanism and structure. Aggregated DCAT metadata can serve as a manifest file as part of the digital preservation process. + Digital Enterprise Research Institute (DERI) + RDF + 22 August 2024 + W3C Document License + https://www.w3.org/TR/vocab-dcat-3/ + Scholarly Knowledge + Data Catalogs + 3.0 MOLBRINELL @@ -1442,18 +1153,43 @@ Materials Testing 0.1 - - MGED - MGED Ontology (MGED) - An ontology for microarray experiments in support of MAGE v.1. Concepts, definitions, terms, and resources for standardized description of a microarray experiment in support of MAGE v.1. The MGED ontology is divided into the MGED Core ontology which is intended to be stable and in synch with MAGE v.1; and the MGED Extended ontology which adds further associations and classes not found in MAGE v.1 - Chris Stoeckert, Helen Parkinson, Trish Whetzel, Paul Spellman, Catherine A. Ball, Joseph White, John Matese, Liju Fan, Gilberto Fragoso, Mervi Heiskanen, Susanna Sansone, Helen Causton, Laurence Game, Chris Taylor - OWL - Feb. 9, 2007 - Creative Commons 4.0 - https://mged.sourceforge.net/ontologies/MGEDontology.php/ - Biology and Life Sciences - Domain Ontology - 1.3.1.1 + + AMOntology + Additive Manufacturing Ontology (AMOntology) + The AM ontology has been developed following two major milestones. The ontology developed within the first milestone includes AMProcessOntology, ModelOntology and AMOntology files. AMProcessOntology contains the set of entities used to capture knowledge about additive manufacturing processes. ModelOntology contains the set of entities used to capture knowledge about modeling concepts that represent (possibly) multi-physics multi-scale processes. AMOntology uses AMProcessOntology and ModelOntology files to describe entities that capture knowledge about characteristics of computational models for AM processes. + Iassou Souroko, Ali Riza Durmaz + TTL + 2023-05-10 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/iassouroko/AMontology + Materials Science and Engineering + Manufacturing + 1.0 + + + AFO + Allotrope Foundation Ontology (AFO) + The AFO is an ontology suite that provides a standard vocabulary and semantic model for the representation of laboratory analytical processes. The AFO suite is aligned at the upper layer to the Basic Formal Ontology (BFO). The core domains modeled include, Equipment, Material, Process, and Results. This artifact contains all triples of Allotrope Foundation Merged Without QUDT Ontology Suite (REC/2023/12) together with triples inferred with HermiT. + Allotrope Foundation + TTL + 2024-06-28 + CC BY 4.0 + https://terminology.tib.eu/ts/ontologies/AFO + Chemistry + Laboratory Analytical Processes + 2024-06 + + + DOAP + The Description of a Project vocabulary (DOAP) + The Description of a Project vocabulary (DOAP), described using W3C RDF Schema and the Web Ontology Language to describe software projects, and in particular open source projects. + Edd Wilder-James + RDF + 2020-04-03 + Apache License 2.0 + https://github.com/ewilderj/doap/blob/master/schema/doap.rdf + Industry + Software AgrO @@ -1468,236 +1204,283 @@ Agronomy 1.0 - - YAGO - YAGO Ontology (YAGO) - YAGO is a large semantic knowledge base, derived from Wikipedia, WordNet, and GeoNames. It contains knowledge about more than 10 million entities and contains more than 120 million facts about these entities. YAGO is special in several ways: It has a clean taxonomy, which was manually built, and it is the only knowledge base with such a large coverage, the clean taxonomy, and the extraction from Wikipedia, WordNet, and GeoNames. - Max Planck Institute for Informatics + + Juso + Juso Ontology (Juso) + Juso Ontology is a Web vocabulary for describing geographical addresses and features. + James G. Kim, LiST Inc. TTL - April, 2024 - Creative Commons 3.0 - https://yago-knowledge.org/downloads/yago-4-5 - General Knowledge - People, Cities, Countries, Movies, Organizations - 4.5 - - - DOLCE - Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) - The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) is a foundational ontology that provides a conceptual framework for the formalization of domain ontologies. - Laboratory for Applied Ontology, ISTC-CNR - OWL + 2015-11-10 Creative Commons 4.0 - https://www.loa.istc.cnr.it/index.php/dolce/ - Upper Ontology - Linguistics, Cognitive Science + https://rdfs.co/juso/0.1.1/html + Geography + geographical knowledge + 0.1.1 - - Nomisma - Nomisma Ontology (Nomisma) - Nomisma Ontology is a collaborative project to provide stable digital representations of numismatic concepts according to the principles of Linked Open Data. These take the form of http URIs that provide access to the information about a concept in various formats. The project is a collaborative effort of the American Numismatic Society and the Institute for the Study of the Ancient World at New York University. - American Numismatic Society, Institute for the Study of the Ancient World + + CCO + Common Core Ontologies (CCO) + The Common Core Ontologies (CCO) is a widely-used suite of eleven ontologies that consist of logically well-defined generic terms and relations among them reflecting entities across all domains of interest. TTL - 2025-01-22 - Creative Commons 4.0 - https://www.dainst.org/forschung/projekte/noslug/2098 - Arts and Humanities - Numismatics + 2024-11-06 + BSD-3-Clause license + https://github.com/CommonCoreOntology/CommonCoreOntologies + General Knowledge + General + 2.0 - - TUBES - TUBES System Ontology (TUBES) - The scope of the TUBES System Ontology is to explicitly define interconnected building service system in the AECO industry, their hierarchical subdivisions, structural and functional aspects, and links to spatial entities. As such, TSO supports the effort to represent linkable information in a future semantic web of building data. It has a strong alignment to other ontologies within the W3C community. - Nicolas Pauen - RDF - 2022-02-01 + + PATO + Phenotype and Trait Ontology (PATO) + An ontology of phenotypic qualities (properties, attributes or characteristics). + OWL + 2025-02-01 Creative Commons 4.0 - https://rwth-e3d.github.io/tso/ - Industry - Building Services - 0.3.0 + https://terminology.tib.eu/ts/ontologies/PATO + Biology and Life Sciences + Biology + 1.2 - - MSLE - Material Science Lab Equipment Ontology (MSLE) - The current ontology describes Material Science Lab Equipment. - TTL - Sep 15, 2022 - https://github.com/MehrdadJalali-AI/MSLE-Ontology + + EMMO + The Elementary Multiperspective Material Ontology (EMMO) + The Elementary Multiperspective Material Ontology (EMMO) is the result of a multidisciplinary effort within the EMMC, aimed at the development of a standard representational ontology framework based on current materials modelling and characterization knowledge. Instead of starting from general upper level concepts, as done by other ontologies, the EMMO development started from the very bottom level, using the actual picture of the physical world coming from applied sciences, and in particular from physics and material sciences. + European Materials Modelling Council (EMMC) + OWL + 2024-03 + Creative Commons 4.0 + https://emmo-repo.github.io/ Materials Science and Engineering - Materials Science - 1.1 + Materials Modelling + 1.0.0-rc3 - - SEPIO - Scientific Evidence and Provenance Information Ontology (SEPIO) - The SEPIO ontology is in its early stages of development, undergoing iterative refinement as new requirements emerge and alignment with existing standards is explored. The SEPIO core file imports two files which can be resolved at the URLs below: IAO ontology-metadata import: https://raw.githubusercontent.com/monarch-initiative/SEPIO-ontology/master/src/ontology/imports/ontology-metadata.owl bfo mireot: https://raw.githubusercontent.com/monarch-initiative/SEPIO-ontology/master/src/ontology/mireots/bfo-mireot.owl + + MFOEM + Mental Functioning Ontology of Emotions - Emotion Module (MFOEM) + The Mental Functioning Ontology - Emotion Module (MFOEM) aims to include all relevant aspects of affective phenomena including their bearers, the different types of emotions, moods, etc., their different parts and dimensions of variation, their facial and vocal expressions, and the role of emotions and affective phenomena in general in influencing human behavior.This class processes Mental Functioning Ontology of Emotions (MFOEM) using default behavior. + Swiss Centre for Affective Sciences & University at Buffalo OWL - 2015-02-23 + Creative Commons 3.0 + http://purl.obolibrary.org/obo/MFOEM.owl + Medicine + Emotion + + + DUO + Data Use Ontology (DUO) + DUO is an ontology which represent data use conditions. + OWL + 2025-02-17 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/SEPIO + https://terminology.tib.eu/ts/ontologies/DUO/ Scholarly Knowledge - Scientific Evidence + 1.0 - - MassSpectrometry - Mass Spectrometry Ontology (MassSpectrometry) - A structured controlled vocabulary for the annotation of experiments concerned with proteomics mass spectrometry. - Andreas Bertsch + + ATOL + Animal Trait Ontology for Livestock (ATOL) + ATOL (Animal Trait Ontology for Livestock) is an ontology of characteristics defining phenotypes of livestock in their environment (EOL). ATOL aims to: - provide a reference ontology of phenotypic traits of farm animals for the international scientific and educational - communities, farmers, etc.; - deliver this reference ontology in a language which can be used by computers in order to support database management, semantic analysis and modeling; - represent traits as generic as possible for livestock vertebrates; - make the ATOL ontology as operational as possible and closely related to measurement techniques; - structure the ontology in relation to animal production. + INRAE, France OWL - 12:02:2025 + May 11, 2020 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/MS - Chemistry - Mass Spectrometry, Proteomics - - - OWLTime - Time Ontology in OWL (OWL-Time) - OWL-Time is an OWL-2 DL ontology of temporal concepts, for describing the temporal properties of resources in the world or described in Web pages. The ontology provides a vocabulary for expressing facts about topological (ordering) relations among instants and intervals, together with information about durations, and about temporal position including date-time information. Time positions and durations may be expressed using either the conventional (Gregorian) calendar and clock, or using another temporal reference system such as Unix-time, geologic time, or different calendars. - World Wide Web Consortium - TTL - 15 November 2022 - W3C Software Notice and Document License - https://www.w3.org/TR/owl-time/ - Units and Measurements - Temporal Reasoning - 1.0 + https://bioportal.bioontology.org/ontologies/ATOL + Agriculture + Animal Science + 6.0 - - BBC - BBC Ontology (BBC) - The BBC ontology codifies the logic that connects web documents, BBC products and platforms for which content is available. Currently, there are 10 major products in Future Media which produce content for BBC online. The majority of those contain more products dedicated in thematic areas, for example Education propositions are part of the K&L (Knowledge and Learning) product portfolio. - LinkedData@bbc.co.uk + + BBCBusiness + BBC Business News Ontology (BBCBusiness) + The Business News Ontology describes the concepts that occur in BBC business news. + https://www.bbc.co.uk/blogs/internet/authors/Jeremy_Tarling, https://uk.linkedin.com/in/amaalmohamed TTL - 2012-12-01 + 2014-11-09 Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/bbc-ontology/ + https://www.bbc.co.uk/ontologies/business-news-ontology News and Media - News - 1.37 + Business News + 0.5 - - BFO - Basic Formal Ontology (BFO) - The Basic Formal Ontology (BFO) is a small, upper-level ontology that describes the basic types of entities in the world and how they relate to each other. - University at Buffalo + + MAT + Material Properties Ontology (MAT) + The Material Properties Ontology aims to provide the vocabulary to describe the building components, materials, and their corresponding properties, relevant within the construction industry. More specifically, the building elements and properties covered in this ontology support applications focused on the design of building renovation projects. + María Poveda-Villalón, Serge Chávez-Feria + RDF + Creative Commons 4.0 + https://bimerr.iot.linkeddata.es/def/material-properties/ + Materials Science and Engineering + Materials Properties + 0.0.8 + + + SUMO + Suggested Upper Merged Ontology (SUMO) + The Suggested Upper Merged Ontology (SUMO) and its domain ontologies form the largest formal public ontology in existence today. They are being used for research and applications in search, linguistics and reasoning. SUMO is the only formal ontology that has been mapped to all of the WordNet lexicon. OWL - 2020 + 2025-02-17 Creative Commons 4.0 - https://github.com/BFO-ontology/BFO-2020/ + https://www.ontologyportal.org/ Upper Ontology - Basic - 2.0 + 1.0 - - EMMO - The Elementary Multiperspective Material Ontology (EMMO) - The Elementary Multiperspective Material Ontology (EMMO) is the result of a multidisciplinary effort within the EMMC, aimed at the development of a standard representational ontology framework based on current materials modelling and characterization knowledge. Instead of starting from general upper level concepts, as done by other ontologies, the EMMO development started from the very bottom level, using the actual picture of the physical world coming from applied sciences, and in particular from physics and material sciences. - European Materials Modelling Council (EMMC) + + DublinCore + Dublin Core Vocabulary (DublinCore) + The Dublin Core Schema is a small set of vocabulary terms that can be used to describe several kinds of resources. Dublin Core Metadata may be used for multiple purposes, from simple resource description, to combining metadata vocabularies of different metadata standards, to providing interoperability for metadata vocabularies in the Linked Data cloud and Semantic Web implementations. + The Dublin Core Metadata Initiative + RDF + February 17, 2017 + Public Domain + https://bioportal.bioontology.org/ontologies/DC + General Knowledge + Metadata + 1.1 + + + SWEET + Semantic Web for Earth and Environment Technology Ontology (SWEET) + The Semantic Web for Earth and Environment Technology Ontology (SWEET) is an investigation in improving discovery and use of Earth science data, through software understanding of the semantics of web resources. SWEET is a collection of ontologies conceptualizing a knowledge space for Earth system science, represented using the web ontology language (OWL). It includes both orthogonal concepts (space, time, Earth realms, physical quantities, etc.) and integrative science knowledge concepts (phenomena, events, etc.). + NASA, JPL, Caltech OWL - 2024-03 + July 14, 2022 Creative Commons 4.0 - https://emmo-repo.github.io/ - Materials Science and Engineering - Materials Modelling - 1.0.0-rc3 + https://bioportal.bioontology.org/ontologies/SWEET + Ecology and Environment + Earth Science, Geoscience + 3.6.0 - - BMO - Building Material Ontology (BMO) - Building Material Ontology defines the main concepts of building material, types, layers, and properties. - Janakiram Karlapudi, Prathap Valluru + + BBCProgrammes + BBC Programmes Ontology (BBCProgrammes) + This ontology aims at providing a simple vocabulary for describing programmes. It covers brands, series (seasons), episodes, broadcast events, broadcast services,etc. Its development was funded by the BBC, and is heavily grounded on previous programmes data modelling work done there. + https://moustaki.org/foaf.rdf#moustaki TTL - 2019-12-10 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://matportal.org/ontologies/BUILDMAT + 2009/02/20 + Creative Commons 4.0 + https://www.bbc.co.uk/ontologies/programmes-ontology + News and Media + Programmes + 1.1 + + + BattINFO + Battery Interface Ontology (BattINFO) + BattINFO is a foundational resource for harmonizing battery knowledge representation and enhancing data interoperability. The primary objective is to provide the necessary tools to create FAIR (Findable, Accessible, Interoperable, Reusable) battery data that can be integrated into the Semantic Web. + TTL + https://github.com/BIG-MAP/BattINFO Materials Science and Engineering - Materials + Materials Science + + + NPO + NanoParticle Ontology (NPO) + NanoParticle Ontology (NPO) is developed within the framework of the Basic Formal Ontology (BFO), and implemented in the Ontology Web Language (OWL) using well-defined ontology design principles. The NPO was developed to represent knowledge underlying the preparation, chemical composition, and characterization of nanomaterials involved in cancer research. Public releases of the NPO are available through BioPortal website, maintained by the National Center for Biomedical Ontology. Mechanisms for editorial and governance processes are being developed for the maintenance, review, and growth of the NPO. + Dennis G. Thomas + OWL + 2013-05-31 + BSD-3-Clause license + https://github.com/sobolevnrm/npo?tab=readme-ov-file + Biology and Life Sciences + Materials Science + 2013-05-31 + + + BBCFood + BBC Food Ontology (BBCFood) + The Food Ontology is a simple lightweight ontology for publishing data about recipes, including the foods they are made from and the foods they create as well as the diets, menus, seasons, courses and occasions they may be suitable for. Whilst it originates in a specific BBC use case, the Food Ontology should be applicable to a wide range of recipe data publishing across the web. + TTL + 2014/03/18 + Creative Commons 4.0 + https://www.bbc.co.uk/ontologies/food-ontology + News and Media + Food and Beverage 0.1 - - ChMO - Chemical Methods Ontology (ChMO) - The Chemical Methods Ontology contains more than 3000 classes and describes methods used to: - collect data in chemical experiments, such as mass spectrometry and electron microscopy. - prepare and separate material for further analysis, such as sample ionisation, chromatography, and electrophoresis - synthesise materials, such as epitaxy and continuous vapour deposition It also describes the instruments used in these experiments, such as mass spectrometers and chromatography columns and their outputs. + + MGED + MGED Ontology (MGED) + An ontology for microarray experiments in support of MAGE v.1. Concepts, definitions, terms, and resources for standardized description of a microarray experiment in support of MAGE v.1. The MGED ontology is divided into the MGED Core ontology which is intended to be stable and in synch with MAGE v.1; and the MGED Extended ontology which adds further associations and classes not found in MAGE v.1 + Chris Stoeckert, Helen Parkinson, Trish Whetzel, Paul Spellman, Catherine A. Ball, Joseph White, John Matese, Liju Fan, Gilberto Fragoso, Mervi Heiskanen, Susanna Sansone, Helen Causton, Laurence Game, Chris Taylor OWL - 2022-04-19 + Feb. 9, 2007 Creative Commons 4.0 - https://github.com/rsc-ontologies/rsc-cmo - Chemistry + https://mged.sourceforge.net/ontologies/MGEDontology.php/ + Biology and Life Sciences + Domain Ontology + 1.3.1.1 - - MechanicalTesting - Mechanical Testing Ontology (MechanicalTesting) - A domain ontology for mechanical testing based on EMMO. - Fraunhofer IWM + + DSIM + Dislocation Simulation and Model Ontology (DSIM) + Dislocation simulation and model ontology (DSIM) is an ontology developed to model various concepts and relationships in the discrete dislocation dynamics domain and microscopy techniques used in the dislocation domain. The various concepts are the numerical representation of dislocation applied in the dislocation dynamic simulation and the pictorial concept of pixel applied in representing dislocation in the experimental image, eg., TEM image, SEM image, and FIM image. + Ahmad Zainul Ihsan OWL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/domain-mechanical-testing + 17.08.2023 + Creative Commons Attribution 3.0 Unported (CC BY 3.0) + https://github.com/OCDO/DSIM Materials Science and Engineering - Mechanical Testing - 1.0.0 + Materials Science + 1.0 - - FRBRoo - Functional Requirements for Bibliographic Records - object-oriented (FRBRoo) - The FRBRoo (Functional Requirements for Bibliographic Records - object-oriented) initiative is a joint effort of the CIDOC Conceptual Reference Model and Functional Requirements for Bibliographic Records international working groups to establish a formal ontology intended to capture and represent the underlying semantics of bibliographic information and to facilitate the integration, mediation, and interchange of bibliographic and museum information. + + Common + Common Ontology (Common) + Ontology for the representation of commons elements in the Trias ontology + Jhon Toledo, Miguel Angel García, Oscar Corcho RDF - November 2015 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://w3id.org/mobility/trias/common/0.1.0 + Education + Computer Science + 0.1.0 + + + BBCStoryline + BBC Storyline Ontology (BBCStoryline) + The News Storyline Ontology is a generic model for describing and organising the stories news organisations tell. The ontology is intended to be flexible to support any given news or media publisher's approach to handling news stories. At the heart of the ontology, is the concept of Storyline. As a nuance of the English language the word 'story' has multiple meanings. In news organisations, a story can be an individual piece of content, such as an article or news report. It can also be the editorial view on events occurring in the world. + http://uk.linkedin.com/in/paulwilton, http://www.bbc.co.uk/blogs/internet/authors/Jeremy_Tarling, http://uk.linkedin.com/in/jarredmcginnis + TTL + 2013-05-01 Creative Commons 4.0 - https://ontome.net/namespace/6#summary - Scholarly Knowledge - Bibliographic Records - 2.4 + https://iptc.org/thirdparty/bbc-ontologies/storyline.html + News and Media + Storyline + 0.3 - - MAT - Material Properties Ontology (MAT) - The Material Properties Ontology aims to provide the vocabulary to describe the building components, materials, and their corresponding properties, relevant within the construction industry. More specifically, the building elements and properties covered in this ontology support applications focused on the design of building renovation projects. - María Poveda-Villalón, Serge Chávez-Feria + + MMO + Materials Mechanics Ontology (MMO) + The materials mechanics ontology is an application-level ontology that was created for supporting named entity recognition tasks for materials fatigue domain. The ontology covers some fairly general MSE concepts that could prospectively be merged into PMDco or other upper materials ontologies such as descriptions of crystallographic defects and microstructural entities. Furthermore, concepts related to the materials fatigue subdomain are also heavily incorporated. + Akhil Thomas, Ali Riza Durmaz RDF + 2024-01-30 Creative Commons 4.0 - https://bimerr.iot.linkeddata.es/def/material-properties/ + https://iwm-micro-mechanics-public.pages.fraunhofer.de/ontologies/materials-mechanics-ontology/index-en.html Materials Science and Engineering - Materials Properties - 0.0.8 + Scholarly Knowledge + 1.0.1 - - SystemCapabilities - System Capabilities Ontology (SystemCapabilities) - This ontology describes system capabilities, operating ranges, and survival ranges. - W3C/OGC Spatial Data on the Web Working Group + + MAMBO + Molecules And Materials Basic Ontology (MAMBO) + MAMBO (Molecules And Materials Basic Ontology) is a domain ontology for molecular materials. Its main targets are: Allowing the retrieval of structured information regarding molecular materials and related applications (i.e. devices based on molecular materials) Supporting the development of new, complex workflows for modelling systems based on molecular materials (computational modelling and data-driven techniques) Integrating data generated via computational simulations and empirical experiments. OWL - 2017-05-14 - W3C Software and Document License - https://terminology.tib.eu/ts/ontologies/SSNSYSTEM + General Public License v3.0 (GPL-3.0) + https://github.com/daimoners/MAMBO Materials Science and Engineering - Materials Science, Engineering, Systems - - - DOID - Human Disease Ontology (DOID) - The Disease Ontology has been developed as a standardized ontology for human disease with the purpose of providing the biomedical community with consistent, reusable and sustainable descriptions of human disease terms, phenotype characteristics and related medical vocabulary disease concepts. - The Open Biological and Biomedical Ontology Foundry - OWL - 2024-12-18 - Creative Commons 1.0 - http://purl.obolibrary.org/obo/doid/releases/2024-12-18/doid.owl - Medicine - Human Diseases + Materials Science - - DEB - Devices, Experimental scaffolds and Biomaterials Ontology (DEB) - The devices, experimental scaffolds, and biomaterials ontology (DEB) is an open resource for organizing information about biomaterials, their design, manufacture, and biological testing. It was developed using text analysis for identifying ontology terms from a biomaterials gold standard corpus, systematically curated to represent the domain's lexicon. Topics covered were validated by members of the biomaterials research community. - Osnat Hakimi + + EXPO + Ontology of Scientific Experiments (EXPO) + Formalise generic knowledge about scientific experimental design, methodology, and results representation. OWL - Jun 2, 2021 - GPL-3.0 - https://github.com/ProjectDebbie/Ontology_DEB - Medicine - Biomaterials - 06/2021 + Academic Free License (AFL) + https://expo.sourceforge.net/ + Scholarly Knowledge + Scientific Experiments FOAF @@ -1712,326 +1495,305 @@ Social 0.1 - - MusicOntology - Music Ontology (MusicOntology) - The Music Ontology Specification provides main concepts and properties fo describing music (i.e. artists, albums and tracks) on the Semantic Web. - Knowledge Media Institute, Open University - RDF - 2013/07/22 - Creative Commons 4.0 - https://github.com/motools/musicontology - Arts and Humanities - Music Theory - 2.1.5 - - - CMSO - Computational Material Sample Ontology (CMSO) - CMSO is an ontology that aims to describe computational materials science samples (or structures), including crystalline defects. Initially focusing on the description at the atomic scale. - https://orcid.org/0000-0001-7564-7990 + + QUDV + Quantities, Units, Dimensions and Values (QUDV) + The SysML QUDV (Quantities, Units, Dimensions and Values) modelLibrary is specified in a UML/SysML class/block diagram. In order to generalize its potential usage and alignment with other standardization efforts concerning quantities and units, it is of interest to verify that the QUDV model can also be represented in the form of an ontology using a formal ontology definition language. + SysML OWL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/OCDO/cmso/tree/main - Materials Science and Engineering - Materials Science - 0.0.1 + 2009-10-30 + Apache License 2.0 + https://www.omgwiki.org/OMGSysML/doku.php?id=sysml-qudv:qudv_owl + Units and Measurements + 2009-10-30 - - BTO - BRENDA Tissue Ontology (BTO) - A structured controlled vocabulary for the source of an enzyme comprising tissues, cell lines, cell types and cell cultures. + + DEB + Devices, Experimental scaffolds and Biomaterials Ontology (DEB) + The devices, experimental scaffolds, and biomaterials ontology (DEB) is an open resource for organizing information about biomaterials, their design, manufacture, and biological testing. It was developed using text analysis for identifying ontology terms from a biomaterials gold standard corpus, systematically curated to represent the domain's lexicon. Topics covered were validated by members of the biomaterials research community. + Osnat Hakimi OWL - 2021-10-26 - Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/BTO + Jun 2, 2021 + GPL-3.0 + https://github.com/ProjectDebbie/Ontology_DEB Medicine - Enzyme - 2021-10-26 - - - AUTO - Automotive Ontology (AUTO) - The AUTOMOTIVE ONTOLOGY (AUTO) defines the shared conceptual structures in the automotive industry. It is an OWL ontology. It is built upon the auto schema.org extension created by the W3C Automotive Ontology Community Group. AUTO's development process follows the best practices established by the EDMC FIBO Community. - EDM Council - RDF - 2021-03-01 - MIT - https://github.com/edmcouncil/auto/tree/master - Industry - Automotive - - - BIBFRAME - Bibliographic Framework Ontology (BIBFRAME) - The Bibframe vocabulary consists of RDF classes and properties used for the description of items cataloged principally by libraries, but may also be used to describe items cataloged by museums and archives. Classes include the three core classes - Work, Instance, and Item - in addition to many more classes to support description. Properties describe characteristics of the resource being described as well as relationships among resources. For example: one Work might be a "translation of" another Work; an Instance may be an "instance of" a particular Bibframe Work. Other properties describe attributes of Works and Instances. For example: the Bibframe property "subject" expresses an important attribute of a Work (what the Work is about), and the property "extent" (e.g. number of pages) expresses an attribute of an Instance. - United States, Library of Congress - RDF - 2022-10-03 - Creative Commons 1.0 - https://id.loc.gov/ontologies/bflc.html - Education - Library, Museums, Archives - 2.5.0 + Biomaterials + 06/2021 - - OntoCAPE - Ontology of Computer-Aided Process Engineering (OntoCAPE) - OntoCAPE is a large-scale ontology for the domain of Computer Aided Process Engineering (CAPE). Represented in a formal, machine-interpretable ontology language, OntoCAPE captures consensual knowledge of the process engineering domain in a generic way such that it can be reused and shared by groups of people and across software systems. On the basis of OntoCAPE, novel software support for various engineering activities can be developed; possible applications include the systematic management and retrieval of simulation models and design documents, electronic procurement of plant equipment, mathematical modeling, as well as the integration of design data from distributed sources. - RWTH Aachen University + + HPOnt + The Heat Pump Ontology (HPOnt) + The Heat Pump Ontology (HPOnt) aims to formalize and represent all the relevant information of Heat Pumps. The HPOnt has been developed as part of the REACT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395. + REACT project team OWL - GNU General Public License. - https://www.avt.rwth-aachen.de/cms/avt/forschung/sonstiges/software/~ipts/ontocape/?lidx=1 + Creative Commons 4.0 + https://react2020.github.io/REACT-ONTOLOGY/HPOnt/index-en.html/ Materials Science and Engineering - Manufacturing - 2.0 + Materials Science + 0.2 - - VIBSO - Vibrational Spectroscopy Ontology (VIBSO) - The Vibration Spectroscopy Ontology defines technical terms with which research data produced in vibrational spectroscopy experiments can be semantically enriched, made machine readable and FAIR. - VIBSO Workgroup - OWL - 2024-09-23 - Creative Commons Attribution 4.0 - https://terminology.tib.eu/ts/ontologies/vibso - Chemistry - Spectroscopy - 2024-09-23 + + OIEManufacturing + Open Innovation Environment Manufacturing (OIEManufacturing) + The manufacturing module populates the physicalistic perspective with manufacturing subclasses categorised according to modern applied physical sciences. + Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro + TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/OIE-Ontologies/ + Materials Science and Engineering + Materials - - PROCO - PROcess Chemistry Ontology (PROCO) - PROCO (PROcess Chemistry Ontology) is a formal ontology that aims to standardly represent entities and relations among entities in the domain of process chemistry. - Anna Dun, Wes A. Schafer, Yongqun "Oliver" He (YH), Zachary Dance + + MassSpectrometry + Mass Spectrometry Ontology (MassSpectrometry) + A structured controlled vocabulary for the annotation of experiments concerned with proteomics mass spectrometry. + Andreas Bertsch OWL - 04-14-2022 + 12:02:2025 Creative Commons 4.0 - https://github.com/proco-ontology/PROCO + https://terminology.tib.eu/ts/ontologies/MS Chemistry - Chemicals, Processes - 04-14-2022 - - - LODE - Linking Open Descriptions of Events (LODE) - People conventionally refer to an action or occurrence taking place at a certain time at a specific location as an event. This notion is potentially useful for connecting individual facts recorded in the rapidly growing collection of linked data sets and for discovering more complex relationships between data. The LODE provide an overview and comparison of existing event models, looking at the different choices they make of how to represent events. It is a model for publishing records of events as Linked Data. A tools for populating this model and a prototype “event directory” web service, which can be used to locate stable URIs for events that have occurred, provide RDFS+OWL descriptions and link to related resources. - Ryan Shaw - RDF - 2020-10-31 - Creative Commons Attribution 3.0 - https://linkedevents.org/ontology/ - Events - 2020-10-31 - - - MFOEM - Mental Functioning Ontology of Emotions - Emotion Module (MFOEM) - The Mental Functioning Ontology - Emotion Module (MFOEM) aims to include all relevant aspects of affective phenomena including their bearers, the different types of emotions, moods, etc., their different parts and dimensions of variation, their facial and vocal expressions, and the role of emotions and affective phenomena in general in influencing human behavior.This class processes Mental Functioning Ontology of Emotions (MFOEM) using default behavior. - Swiss Centre for Affective Sciences & University at Buffalo - OWL - Creative Commons 3.0 - http://purl.obolibrary.org/obo/MFOEM.owl - Medicine - Emotion + Mass Spectrometry, Proteomics - - AMOntology - Additive Manufacturing Ontology (AMOntology) - The AM ontology has been developed following two major milestones. The ontology developed within the first milestone includes AMProcessOntology, ModelOntology and AMOntology files. AMProcessOntology contains the set of entities used to capture knowledge about additive manufacturing processes. ModelOntology contains the set of entities used to capture knowledge about modeling concepts that represent (possibly) multi-physics multi-scale processes. AMOntology uses AMProcessOntology and ModelOntology files to describe entities that capture knowledge about characteristics of computational models for AM processes. - Iassou Souroko, Ali Riza Durmaz + + BMO + Building Material Ontology (BMO) + Building Material Ontology defines the main concepts of building material, types, layers, and properties. + Janakiram Karlapudi, Prathap Valluru TTL - 2023-05-10 + 2019-12-10 Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/iassouroko/AMontology + https://matportal.org/ontologies/BUILDMAT Materials Science and Engineering - Manufacturing - 1.0 + Materials + 0.1 - - DISO - Dislocation Ontology (DISO) - DISO is an ontology that defines the linear defect, in particular dislocation concepts and relations between them in crystalline materials. - Ahmad Zainul Ihsan + + PSIMOD + Protein Modifications Ontology (PSIMOD) + PSI-MOD is an ontology developed by the Proteomics Standards Initiative (PSI) that describes protein chemical modifications, logically linked by an is_a relationship in such a way as to form a direct acyclic graph (DAG). The PSI-MOD ontology has more than 45 top-level nodes, and provides alternative hierarchical paths for classifying protein modifications either by the molecular structure of the modification, or by the amino acid residue that is modified. OWL - 21.03.202 - Creative Commons Attribution 3.0 International (CC BY 3.0) - https://github.com/Materials-Data-Science-and-Informatics/dislocation-ontology + 2022-06-13 + Creative Commons Attribution 4.0 + https://github.com/HUPO-PSI/psi-mod-CV + Chemistry + Protein Modifications + 1.031.6 + + + SSN + Semantic Sensor Network Ontology (SSN) + The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators. SSN follows a horizontal and vertical modularization architecture by including a lightweight but self-contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) for its elementary classes and properties. With their different scope and different degrees of axiomatization, SSN and SOSA are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things. Both ontologies are described below, and examples of their usage are given. + W3C/OGC Spatial Data on the Web Working Group + TTL + 2017-04-17 + http://www.w3.org/Consortium/Legal/2015/copyright-software-and-document + https://github.com/w3c/sdw-sosa-ssn/tree/482484fe2edc1ba8aa7f19214a72bdb77123e833 Materials Science and Engineering - Materials Science + Sensor Networks 1.0 - - CSO - Computer Science Ontology (CSO) - The Computer Science Ontology (CSO) is a large-scale ontology of research areas in computer science. It provides a comprehensive vocabulary of research topics in computing, organized in a hierarchical structure. This class processes the Computer Science Ontology (CSO) with custom hooks for: - Topic-based class detection - superTopicOf relationships - contributesTo relationships - Knowledge Media Institute, Open University - OWL - Creative Commons 4.0 - https://cso.kmi.open.ac.uk/home + + LexInfo + LexInfo (LexInfo) + LexInfo allows us to associate linguistic information to elements in an ontology with respect to any level of linguistic description and expressivity. LexInfo has been implemented as an OWL ontology and is available together with an API. + RDF + Apache 2.0 + https://lexinfo.net/index.html Scholarly Knowledge - Computer Science - 3.4 + Linguistics + 3.0 + + + PRotein + Protein Ontology (PRO) + The PRotein Ontology (PRO) formally defines taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. + RDF + 08:08:2024 + Creative Commons 4.0 + http://purl.obolibrary.org/obo/pr.owl + Medicine + Protein + 1.2 + + + FIX + FIX Ontology (FIX) + An ontology of physico-chemical methods and properties. + OWL + 2020-04-13 + https://terminology.tib.eu/ts/ontologies/FIX + Chemistry + Chemicals, Properties + 2020-04-13 - - AGROVOC - AGROVOC Multilingual Thesaurus (AGROVOC) - AGROVOC is a relevant Linked Open Data set about agriculture available for public use and facilitates access and visibility of data across domains and languages. It offers a structured collection of agricultural concepts, terms, definitions and relationships which are used to unambiguously identify resources, allowing standardized indexing processes and making searches more efficient. - Food and Agriculture Organization of the United Nations + + BIBFRAME + Bibliographic Framework Ontology (BIBFRAME) + The Bibframe vocabulary consists of RDF classes and properties used for the description of items cataloged principally by libraries, but may also be used to describe items cataloged by museums and archives. Classes include the three core classes - Work, Instance, and Item - in addition to many more classes to support description. Properties describe characteristics of the resource being described as well as relationships among resources. For example: one Work might be a "translation of" another Work; an Instance may be an "instance of" a particular Bibframe Work. Other properties describe attributes of Works and Instances. For example: the Bibframe property "subject" expresses an important attribute of a Work (what the Work is about), and the property "extent" (e.g. number of pages) expresses an attribute of an Instance. + United States, Library of Congress RDF - August 12, 2024 - Creative Commons 4.0 - https://agroportal.lirmm.fr/ontologies/AGROVOC - Agriculture - Agricultural Knowledge - 2024-04 - - - BVCO - Battery Value Chain Ontology (BVCO) - Basically, Battery Value Chain Ontology (BVCO) aims to model processes along the Battery value chain. Processes are holistic perspective elements that transform inputs/educts (matter, energy, information) into output/products (matter, energy, information) with the help of tools (devices, algorithms). They can be decomposed into sub-processes and have predecessor and successor processes. - Lukas Gold, Simon Stier - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/Battery-Value-Chain-Ontology/ontology - Materials Science and Engineering - Materials Science - 0.4.3 + 2022-10-03 + Creative Commons 1.0 + https://id.loc.gov/ontologies/bflc.html + Education + Library, Museums, Archives + 2.5.0 - - PO - Plant Ontology (PO) - The Plant Ontology (PO) is a structured vocabulary and database resource that links plant anatomy, morphology and growth and development to plant genomics data. + + OBI + Ontology for Biomedical Investigations (OBI) + The Ontology for Biomedical Investigations (OBI) helps you communicate clearly about scientific investigations by defining more than 2500 terms for assays, devices, objectives, and more. OWL + 2025-01-09 Creative Commons 4.0 - https://github.com/Planteome/plant-ontology - Agriculture - Plant Anatomy, Morphology, Growth and Development + https://github.com/obi-ontology/obi/tree/master + Medicine + Biomedical Investigations - - MarineTLO - Marine Taxonomy and Life Ontology (MarineTLO) - MarineTLO is a top level ontology, generic enough to provide consistent abstractions or specifications of concepts included in all data models or ontologies of marine data sources and provide the necessary properties to make this distributed knowledge base a coherent source of facts relating observational data with the respective spatiotemporal context and categorical (systematic) domain knowledge. It can be used as the core schema for publishing Linked Data, as well as for setting up integration systems for the marine domain. It can be extended to any level of detail on demand, while preserving monotonicity. For its development and evolution we have adopted an iterative and incremental methodology where a new version is released every two months. For the implementation we use OWL 2, and to evaluate it we use a set of competency queries, formulating the domain requirements provided by the related communities. - Information System Laboratory (ISL), Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH) + + REX + Physico-chemical process ontology (REX) + REX is an ontology of physico-chemical processes, i.e. physico-chemical changes occurring in course of time. REX includes both microscopic processes (involving molecular entities or subatomic particles) and macroscopic processes. Some biochemical processes from Gene Ontology (GO Biological process) can be described as instances of REX. + University of Warsaw OWL - 2017-01-05 + 2025-03-11 Creative Commons 4.0 - https://projects.ics.forth.gr/isl/MarineTLO/ - Biology and Life Sciences - Marine Science, Oceanography + https://terminology.tib.eu/ts/ontologies/REX + Chemistry 1.0 - - ATOL - Animal Trait Ontology for Livestock (ATOL) - ATOL (Animal Trait Ontology for Livestock) is an ontology of characteristics defining phenotypes of livestock in their environment (EOL). ATOL aims to: - provide a reference ontology of phenotypic traits of farm animals for the international scientific and educational - communities, farmers, etc.; - deliver this reference ontology in a language which can be used by computers in order to support database management, semantic analysis and modeling; - represent traits as generic as possible for livestock vertebrates; - make the ATOL ontology as operational as possible and closely related to measurement techniques; - structure the ontology in relation to animal production. - INRAE, France - OWL - May 11, 2020 - Creative Commons 4.0 - https://bioportal.bioontology.org/ontologies/ATOL - Agriculture - Animal Science - 6.0 - - - iCalendar - iCalendar Vocabulary (iCalendar) - iCalendar is an Internet standard for exchanging calendar and scheduling data across different applications and platforms using a standardized text-based format (.ics). It enables interoperability for events, tasks, and scheduling, supporting features like recurring events, invitations, and time zone adjustments. While widely used in applications like Google Calendar and Outlook, its complexity and partial implementations pose challenges, leading to efforts to integrate it with Semantic Web technologies for enhanced data linking and automation. - Dan Connolly, W3C, Libby Miller, ASemantics + + SAREF + Smart Applications REFerence ontology (SAREF) + The Smart Applications REFerence (SAREF) suite of ontologies forms a shared model of consensus intended to enable semantic interoperability between solutions from different providers and among various activity sectors in the Internet of Things (IoT), thus contributing to the development of data spaces. SAREF is published as a set of open standards produced by ETSI Technical Committee Smart Machine-to-Machine communications (SmartM2M). + ETSI Technical Committee Smart Machine-to-Machine communications (SmartM2M) RDF - 2004/04/07 - Open Publication License - https://www.w3.org/2002/12/cal/ - Events - Calendar and Scheduling - 1.14 - - - OIESoftware - Open Innovation Environment Software (OIESoftware) - EMMO-compliant, domain-level OIE ontology tackling the areas of software products. - Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/OIE-Ontologies/ - Materials Science and Engineering - Materials - 0.1 + 2020-12-31 + https://saref.etsi.org/core/v3.2.1/ + Web and Internet + interoperability + 3.2.1 - - BBCProvenance - BBC Provenance News Ontology (BBCProvenance) - An ontology to capture data about the provenance of data in an RDF Triple Store. This provenance is focused on the immediate providers and not the ultimate source, so for example, this would record that geodata was provided by the BBC Locator team, and not geonames. In the Linked Data Platform, this data is applied to contexts or named graphs. A named graph is, in effect, a 'fourth part' to a triple, hence the term 'quad store'. - LinkedData@bbc.co.uk - TTL - 2012-12-01 - Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/provenance-ontology - News and Media - Provenance - 1.9 + + ENVO + Environment Ontology (ENVO) + ENVO is an expressive, community ontology which helps humans, machines, and semantic web applications understand environmental entities of all kinds, from microscopic to intergalactic scales. As a FAIR-compliant resource, it promotes interoperability through the concise, controlled description of all things environmental. + Pier Luigi Buttigieg (https://orcid.org/0000-0002-4366-3088) + OWL + 2024-07-01 + Creative Commons 1.0 + https://obofoundry.org/ontology/envo.html + Ecology and Environment + Environment, Ecosystems, Habitats + 2024-07-01 - - FSO - Flow Systems Ontology (FSO) - The Flow Systems Ontology (FSO) is an ontology for describing interconnected systems with material or energy flow connections, and their components. - Ali Kücükavci, Mads Holten Rasmussen, Ville Kukkonen + + CIFCore + Crystallographic Information Framework Core Dictionary (CIFCore) + (1) to explain the historical development of CIF dictionaries to define in a machine-actionable manner the contents of data files covering various aspects of crystallography and related structural sciences; (2) to demonstrate some of the more complex types of information that can be handled with this approach. TTL - 2020-08-06 - Creative Commons 4.0 - https://github.com/alikucukavci/FSO/ + May 24, 2023 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/CIF-ontology?tab=readme-ov-file Materials Science and Engineering Materials Science 0.1.0 - - OIEMaterials - Open Innovation Environment Materials (OIEMaterials) - The materials module populates the physicalistic perspective with materials subclasses categorised according to modern applied physical sciences. - Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/OIE-Ontologies/ + + CHIRO + CHEBI Integrated Role Ontology (CHIRO) + CHEBI provides a distinct role hierarchy. Chemicals in the structural hierarchy are connected via a 'has role' relation. CHIRO provides links from these roles to useful other classes in other ontologies. This will allow direct connection between chemical structures (small molecules, drugs) and what they do. This could be formalized using 'capable of', in the same way Uberon and the Cell Ontology link structures to processes. + OWL + 2015-11-23 + Creative Commons 1.0 + https://terminology.tib.eu/ts/ontologies/chiro + Chemistry + Chemicals, Roles + 2015-11-23 + + + SystemCapabilities + System Capabilities Ontology (SystemCapabilities) + This ontology describes system capabilities, operating ranges, and survival ranges. + W3C/OGC Spatial Data on the Web Working Group + OWL + 2017-05-14 + W3C Software and Document License + https://terminology.tib.eu/ts/ontologies/SSNSYSTEM Materials Science and Engineering - Materials + Materials Science, Engineering, Systems - - RO - Relation Ontology (RO) - The Relations Ontology (RO) is a collection of OWL relations (ObjectProperties) intended for use across a wide variety of biological ontologies. + + GFO + General Formal Ontology (GFO) + The General Formal Ontology is a top-level ontology for conceptual modeling, which is being constantly further developed by Onto-Med. It includes elaborations of categories like objects, processes, time and space, properties, relations, roles, functions, facts, and situations. Moreover, we are working on an integration with the notion of levels of reality in order to more appropriately capture entities in the material, mental, and social areas. OWL - 2024-04-24 - CC0 - http://purl.obolibrary.org/obo/ro.owl + 2024-11-18 + Creative Commons 4.0 + https://onto-med.github.io/GFO/release/2024-11-18/index-en.html + Upper Ontology + + + EDAM + The ontology of data analysis and management (EDAM) + EDAM is a domain ontology of data analysis and data management in bio- and other sciences, and science-based applications. It comprises concepts related to analysis, modelling, optimisation, and data life cycle. Targetting usability by diverse users, the structure of EDAM is relatively simple, divided into 4 main sections: Topic, Operation, Data (incl. Identifier), and Format. + Federico Bianchini, Hervé Ménager, Jon Ison, Matúš Kalaš + OWL + 24.09.2024 + Creative Commons 4.0 + https://terminology.tib.eu/ts/ontologies/edam General Knowledge - Relations - 2024-04-24 + General + 1.25-20240924T0027Z-unstable(1.26) - - PLDO - Planar Defects Ontology (PLDO) - PLDO is an ontology designed to describe planar defects in crystalline materials, such as grain boundaries and stacking faults, with a focus on their atomic-scale structure and properties. - https://orcid.org/0000-0001-7564-7990 + + UO + Units of Measurement Ontology (UO) + Metrical units for use in conjunction with PATO. + KAUST OWL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/OCDO/pldo + 2023-05-25 + Creative Commons 3.0 + https://bioportal.bioontology.org/ontologies/UO + Units and Measurements + + + DOLCE + Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) + The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) is a foundational ontology that provides a conceptual framework for the formalization of domain ontologies. + Laboratory for Applied Ontology, ISTC-CNR + OWL + Creative Commons 4.0 + https://www.loa.istc.cnr.it/index.php/dolce/ + Upper Ontology + Linguistics, Cognitive Science + + + MatVoc + Materials Vocabulary (MatVoc) + The official ontology produced in the context of the STREAM project. + Tatyana Sheveleva, Javad Chamanara + RDF + 2022-12-12 + MIT License + https://stream-project.github.io/#overv Materials Science and Engineering - Materials Defects + Materials Science 1.0.0 - - LIFO - Life Ontology (LifO) - The Life Ontology (LifO) is an ontology of the life of organism. LifO represents the life processes of organisms and related entities and relations. LifO is a general purpose ontology that covers the common features associated with different organisms such as unicellular prokaryotes (e.g., E. coli) and multicellular organisms (e.g., human). - Yongqun "Oliver" He (YH) + + BFO + Basic Formal Ontology (BFO) + The Basic Formal Ontology (BFO) is a small, upper-level ontology that describes the basic types of entities in the world and how they relate to each other. + University at Buffalo OWL - March 11, 2018 + 2020 Creative Commons 4.0 - https://bioportal.bioontology.org/ontologies/LIFO - Biology and Life Sciences - General Purpose - 1.0.17 + https://github.com/BFO-ontology/BFO-2020/ + Upper Ontology + Basic + 2.0 GoodRelations @@ -2046,119 +1808,210 @@ E-commerce 1.0 - - RXNO - Reaction Ontology (RXNO) - RXNO is the name reaction ontology. It contains more than 500 classes representing organic reactions such as the Diels–Alder cyclization. - OWL - 2021-12-16 + + ChordOntology + Chord Ontology (ChordOntology) + The Chord Ontology is an ontology for describing chords in musical pieces. + Yves Raimond, Samer Abdallah, Centre for Digital Music, Queen Mary, University of London + RDF + 2007-10-25 + Creative Commons 3.0 + https://github.com/motools/chordontology + Arts and Humanities + Musical Works + 1.0 + + + BBCCoreConcepts + BBC Core Concepts Ontology (BBCCoreConcepts) + The generic BBC ontology for people, places, events, organisations, themes which represent things that make sense across the BBC. This model is meant to be generic enough, and allow clients (domain experts) link their own concepts e.g., athletes or politicians using rdfs:sublClassOf the particular concept. + jeremy.tarling@bbc.co.uk, tom.hodgkinson@bbc.co.uk + TTL + 2019-11-21 Creative Commons 4.0 - https://github.com/rsc-ontologies/rxno - Chemistry + https://www.bbc.co.uk/ontologies/core-concepts-ontology + News and Media + Core Concepts + 1.30 - - UMBEL - Upper Mapping and Binding Exchange Layer Vocabulary (UMBEL) - UMBEL is the Upper Mapping and Binding Exchange Layer, designed to help content interoperate on the Web. UMBEL provides two valuable functions: First, it is a broad, general reference structure of 34,000 concepts, which provides a scaffolding to link and interoperate other datasets and domain vocabularies. Second, it is a base vocabulary for the construction of other concept-based domain ontologies, also designed for interoperation. - n3 - May 10, 2016 - https://github.com/structureddynamics/UMBEL/tree/master/Ontology - General Knowledge - Web Development - 1.50 + + MaterialInformation + Material Information Ontology (MaterialInformation) + The Material Information ontology is divided into smaller ontologies (partitions). The partitions are Environment, Geometry, Material Information, Manufacturing Process, Property, Substance, Unit Dimension, Structure, Equation and Physical Constant. + Toshihiro Ashino + OWL + https://github.com/EngyNasr/MSE-Benchmark/blob/main/testCases/secondTestCase/MaterialInformation.owl + Materials Science and Engineering + Materials Science - - OBOE - Extensible Observation Ontology (OBOE) - The Extensible Observation Ontology (OBOE) is a formal ontology for capturing the semantics of scientific observation and measurement. The ontology supports researchers to add detailed semantic annotations to scientific data, thereby clarifying the inherent meaning of scientific observations. - The Regents of the University of California + + OIECharacterisation + Open Innovation Environment Characterisation (OIECharacterisation) + EMMO-compliant, domain-level OIE ontology tackling the areas of characterization methods. + Daniele Toti, Gerhard Goldbeck, Pierluigi Del Nostro + TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/OIE-Ontologies/ + Materials Science and Engineering + Materials + + + PO + Plant Ontology (PO) + The Plant Ontology (PO) is a structured vocabulary and database resource that links plant anatomy, morphology and growth and development to plant genomics data. OWL - Creative Commons 3.0 - https://terminology.tib.eu/ts/ontologies/OBOE - Scholarly Knowledge - Scientific Observation - 1.2 + Creative Commons 4.0 + https://github.com/Planteome/plant-ontology + Agriculture + Plant Anatomy, Morphology, Growth and Development - - FRAPO - Funding, Research Administration and Projects Ontology (FRAPO) - The Funding, Research Administration and Projects Ontology (FRAPO) is an ontology for describing the administrative information of research projects, e.g., grant applications, funding bodies, project partners, etc. - David Shotton + + Framester + Framester Ontology (Framester) + Framester is a a frame-based ontological resource acting as a hub between linguistic resources such as FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, and leveraging this wealth of links to create an interoperable predicate space formalized according to frame semantics and semiotics. Framester uses WordNet and FrameNet at its core, expands it to other resources transitively, and represents them in a formal version of frame semantics. + Aldo Gangemi RDF + 19-04-2016 Creative Commons 4.0 - http://www.sparontologies.net/ontologies/frapo + http://150.146.207.114/lode/extract?url=http://ontologydesignpatterns.org/ont/framester/framester.owl Scholarly Knowledge - Administration + Linguistics + 1.0 - - CHIRO - CHEBI Integrated Role Ontology (CHIRO) - CHEBI provides a distinct role hierarchy. Chemicals in the structural hierarchy are connected via a 'has role' relation. CHIRO provides links from these roles to useful other classes in other ontologies. This will allow direct connection between chemical structures (small molecules, drugs) and what they do. This could be formalized using 'capable of', in the same way Uberon and the Cell Ontology link structures to processes. + + SWO + Software Ontology (SWO) + The Software Ontology (SWO) is a resource for describing software tools, their types, tasks, versions, provenance and associated data. It contains detailed information on licensing and formats as well as software applications themselves, mainly (but not limited) to the bioinformatics community. + Allyson Lister, Andy Brown, Duncan Hull, Helen Parkinson, James Malone, Jon Ison, Nandini Badarinarayan, Robert Stevens OWL - 2015-11-23 - Creative Commons 1.0 - https://terminology.tib.eu/ts/ontologies/chiro - Chemistry - Chemicals, Roles - 2015-11-23 + 2013-07-01 + Creative Commons 4.0 + https://terminology.tib.eu/ts/ontologies/SWO + Scholarly Knowledge + Software + 1.0 - - DSIM - Dislocation Simulation and Model Ontology (DSIM) - Dislocation simulation and model ontology (DSIM) is an ontology developed to model various concepts and relationships in the discrete dislocation dynamics domain and microscopy techniques used in the dislocation domain. The various concepts are the numerical representation of dislocation applied in the dislocation dynamic simulation and the pictorial concept of pixel applied in representing dislocation in the experimental image, eg., TEM image, SEM image, and FIM image. - Ahmad Zainul Ihsan + + FoodOn + Food Ontology (FoodON) + FoodOn, the food ontology, contains vocabulary for naming food materials and their anatomical and taxonomic origins, from raw harvested food to processed food products, for humans and domesticated animals. It provides a neutral and ontology-driven standard for government agencies, industry, nonprofits and consumers to name and reference food products and their components throughout the food supply chain. OWL - 17.08.2023 - Creative Commons Attribution 3.0 Unported (CC BY 3.0) - https://github.com/OCDO/DSIM + 2025-01-16 + Creative Commons 4.0 + http://purl.obolibrary.org/obo/foodon.owl + Agriculture + Diet, Metabolomics, and Nutrition + + + PMDco + The Platform MaterialDigital core ontology (PMDco) + The PMD Core Ontology (PMDco) is a comprehensive framework for representing knowledge that encompasses fundamental concepts from the domains of materials science and engineering (MSE). The PMDco has been designed as a mid-level ontology to establish a connection between specific MSE application ontologies and the domain neutral concepts found in established top-level ontologies. The primary goal of the PMDco is to promote interoperability between diverse domains. + Jannis Grundmann + OWL + 2025-03-20 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/materialdigital/core-ontology?tab=readme-ov-file Materials Science and Engineering Materials Science - 1.0 + 3.0.0-alpha1 - - GO - Gene Ontology (GO) - The Gene Ontology (GO) Provides structured controlled vocabularies for the annotation of gene products with respect to their molecular function, cellular component, and biological role. + + PPlan + Ontology for Provenance and Plans (P-Plan) + The Ontology for Provenance and Plans (P-Plan) is an extension of the PROV-O ontology [PROV-O] created to represent the plans that guided the execution of scientific processes. P-Plan describes how the plans are composed and their correspondence to provenance records that describe the execution itself. + http://www.isi.edu/~gil/ OWL - 2024-11-03 + 2014-03-12 Creative Commons 4.0 - https://geneontology.org/docs/download-ontology/ - Biology and Life Sciences - Molecular Biology, Genetics + https://vocab.linkeddata.es/p-plan/index.html + Scholarly Knowledge + 1.3 - - NFDIcore - National Research Data Infrastructure Ontology (NFDIcore) - The National Research Data Infrastructure (NFDI) initiative has led to the formation of various consortia, each focused on developing a research data infrastructure tailored to its specific domain. To ensure interoperability across these consortia, the NFDIcore ontology has been developed as a mid-level ontology for representing metadata related to NFDI resources, including individuals, organizations, projects, data portals, and more. - Jörg Waitelonis, Oleksandra Bruns, Tabea Tietz, Etienne Posthumus, Hossein Beygi Nasrabadi, Harald Sack + + CHAMEO + Characterisation Methodology Domain Ontology (CHAMEO) + An ontology for materials characterization which represents the evolution of the CHADA template in an ontological form, allowing to generate FAIR documentation of Characterisation Experiments and that has been used as a basis for the development of a number of technique-specific or application-specific ontologies in the materials characterisation domain. CHAMEO has been used as a foundation for the definition of the new CHADA template during the CWA. + https://orcid.org/0000-0002-4181-2852, https://orcid.org/0000-0002-5174-8508, https://orcid.org/0000-0002-9668-6961 + TTL + 2024-04-12 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/domain-characterisation-methodology + Materials Science and Engineering + Materials Science + 1.0.0 + + + OPMW + Open Provenance Model for Workflows (OPMW) + The Open Provenance Model for Workflows (OPMW) is an ontology for describing workflow traces and their templates based on the Open Provenance Model. It has been designed as a profile for OPM, extending and reusing OPM's core ontologies OPMV (OPM-Vocabulary) and OPMO (OPM-Ontology). + http://delicias.dia.fi.upm.es/members/DGarijo/#me, http://www.isi.edu/~gil/ OWL - 2025-02-07 - Creative Commons 1.0 - https://ise-fizkarlsruhe.github.io/nfdicore/ + 2014-12-22 + Creative Commons Attribution 2.0 Generic (CC BY 2.0) + https://www.opmw.org/model/OPMW_20141222/ Scholarly Knowledge - Research Data Infrastructure - 3.0.0 + Workflows + 3.1 - - MaterialInformation - Material Information Ontology (MaterialInformation) - The Material Information ontology is divided into smaller ontologies (partitions). The partitions are Environment, Geometry, Material Information, Manufacturing Process, Property, Substance, Unit Dimension, Structure, Equation and Physical Constant. - Toshihiro Ashino + + NMRCV + Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV) + This artefact is an MSI-approved controlled vocabulary primarily developed under COSMOS EU and PhenoMeNal EU governance. The nmrCV is supporting the nmrML XML format with standardized terms. nmrML is a vendor agnostic open access NMR raw data standard. Its primaly role is analogous to the mzCV for the PSI-approved mzML XML format. It uses BFO2.0 as its Top level. This CV was derived from two predecessors (The NMR CV from the David Wishart Group, developed by Joseph Cruz) and the MSI nmr CV developed by Daniel Schober at the EBI. This simple taxonomy of terms (no DL semantics used) serves the nuclear magnetic resonance markup language (nmrML) with meaningful descriptors to amend the nmrML xml file with CV terms. Metabolomics scientists are encouraged to use this CV to annotrate their raw and experimental context data, i.e. within nmrML. The approach to have an exchange syntax mixed of an xsd and CV stems from the PSI mzML effort. The reason to branch out from an xsd into a CV is, that in areas where the terminology is likely to change faster than the nmrML xsd could be updated and aligned, an externally and decentrallised maintained CV can accompensate for such dynamics in a more flexible way. A second reason for this set-up is that semantic validity of CV terms used in an nmrML XML instance (allowed CV terms, position/relation to each other, cardinality) can be validated by rule-based proprietary validators: By means of cardinality specifications and XPath expressions defined in an XML mapping file (an instances of the CvMappingRules.xsd ), one can define what ontology terms are allowed in a specific location of the data model. + Daniel Schober OWL - https://github.com/EngyNasr/MSE-Benchmark/blob/main/testCases/secondTestCase/MaterialInformation.owl + 2017-10-19 + Creative Commons 4.0 + https://terminology.tib.eu/ts/ontologies/NMRCV + Chemistry + 1.1.0 + + + ONTORULE + Ontology for the Steel Domain (ONTORULE) + This deliverable consists of the ontology developed in ONTORULE for the steel industry use case. It is presented as an attachment to this document as an html document which was generated by SpecGen from the OWL file. The original OWL file is also included. This document describes the different concepts and attributes included in the ontology. For a better understanding of the decisions taken at the time of representing the knowledge in the ontology, the reader is encouraged to also read the document D5.4. + Diego Daz + TTL + 2010-05-31 + N/A + https://raw.githubusercontent.com/ISE-FIZKarlsruhe/mseo.github.io/master/Ontology_files/ONTORULEsteel.ttl Materials Science and Engineering Materials Science - - OBI - Ontology for Biomedical Investigations (OBI) - The Ontology for Biomedical Investigations (OBI) helps you communicate clearly about scientific investigations by defining more than 2500 terms for assays, devices, objectives, and more. + + ChEBI + Chemical Entities of Biological Interest (ChEBI) + Chemical Entities of Biological Interest (ChEBI) is a dictionary of molecular entities focused on ‘small’ chemical compounds. The term ‘molecular entity’ refers to any constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc., identifiable as a separately distinguishable entity. The molecular entities in question are either products of nature or synthetic products used to intervene in the processes of living organisms. ChEBI incorporates an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. + OWL + 01/01/2025 + Creative Commons 4.0 + https://www.ebi.ac.uk/chebi/ + Chemistry + Chemical Entities + 239 + + + AIISO + Academic Institution Internal Structure Ontology (AIISO) + The Academic Institution Internal Structure Ontology (AIISO) provides classes and properties to describe the internal organizational structure of an academic institution. AIISO is designed to work in partnership with Participation (http://purl.org/vocab/participation/schema), FOAF (http://xmlns.com/foaf/0.1/) and aiiso-roles (http://purl.org/vocab/aiiso-roles/schema) to describe the roles that people play within an institution. + Open University + RDF + 2008-05-14 + Creative Commons 4.0 + https://vocab.org/aiiso/ + Scholarly Knowledge + Academic Institution + 1.0 + + + GO + Gene Ontology (GO) + The Gene Ontology (GO) Provides structured controlled vocabularies for the annotation of gene products with respect to their molecular function, cellular component, and biological role. OWL - 2025-01-09 + 2024-11-03 Creative Commons 4.0 - https://github.com/obi-ontology/obi/tree/master - Medicine - Biomedical Investigations + https://geneontology.org/docs/download-ontology/ + Biology and Life Sciences + Molecular Biology, Genetics CHEMINF @@ -2171,40 +2024,93 @@ Chemistry 2.1.0 - - EMMOCrystallography - Crystallography Ontology (EMMOCrystallography) - A crystallography domain ontology based on EMMO and the CIF core dictionary. It is implemented as a formal language. - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/domain-crystallography - Materials Science and Engineering - Crystallography - 0.0.1 - - - MatVoc - Materials Vocabulary (MatVoc) - The official ontology produced in the context of the STREAM project. - Tatyana Sheveleva, Javad Chamanara - RDF - 2022-12-12 - MIT License - https://stream-project.github.io/#overv + + LPBFO + Laser Powder Bed Fusion Ontology (LPBFO) + The LPBF Ontology can be used to describe the additive manufacturing of a component via Laser Powder Bed Fusion (LPBF) / Selective Laser Melting (SLM). The ontology builds on BFO2.0 and BWMD_mid and has been developed to be used in conjunction with the digital workflows provided by Fraunhofer IWM. If possible, the terminology within this ontology was used as provided by ISO/ASTM 52900:2015. Recently, classes relevant for Life Cycle Analysis (LCA) were added that enable sustainability assessment. + Fraunhofer IWM + OWL + 2022-09-20 + Creative Commons 4.0 + https://matportal.org/ontologies/LPBFO Materials Science and Engineering Materials Science - 1.0.0 + 1.1.9 - - DUO - Data Use Ontology (DUO) - DUO is an ontology which represent data use conditions. + + Conference + Conference Ontology (Conference) + The conference-ontology is a new self-contained ontology for modeling knowledge about conferences. The conference-ontology adopts the best ontology design practices (e.g., Ontology Design Patterns, ontology reuse and interlinking) and guarantees interoperability with SWC ontology and all other pertinent vocabularies. + Aldo Gangemi et al. OWL - 2025-02-17 + 2016/04/30 + Creative Commons 3.0 + http://www.scholarlydata.org/ontology/conference-ontology.owl + Events + Conferences + + + EURIO + EUropean Research Information Ontology (EURIO) + EURIO (EUropean Research Information Ontology) conceptualises, formally encodes and makes available in an open, structured and machine-readable format data about resarch projects funded by the EU's framework programmes for research and innovation. + Publications Office of the European Commission + RDF + 2023-10-19 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/DUO/ + https://op.europa.eu/de/web/eu-vocabularies/dataset/-/resource?uri=http://publications.europa.eu/resource/dataset/eurio Scholarly Knowledge - 1.0 + Research Information + 2.4 + + + BBCProvenance + BBC Provenance News Ontology (BBCProvenance) + An ontology to capture data about the provenance of data in an RDF Triple Store. This provenance is focused on the immediate providers and not the ultimate source, so for example, this would record that geodata was provided by the BBC Locator team, and not geonames. In the Linked Data Platform, this data is applied to contexts or named graphs. A named graph is, in effect, a 'fourth part' to a triple, hence the term 'quad store'. + LinkedData@bbc.co.uk + TTL + 2012-12-01 + Creative Commons 4.0 + https://www.bbc.co.uk/ontologies/provenance-ontology + News and Media + Provenance + 1.9 + + + Nomisma + Nomisma Ontology (Nomisma) + Nomisma Ontology is a collaborative project to provide stable digital representations of numismatic concepts according to the principles of Linked Open Data. These take the form of http URIs that provide access to the information about a concept in various formats. The project is a collaborative effort of the American Numismatic Society and the Institute for the Study of the Ancient World at New York University. + American Numismatic Society, Institute for the Study of the Ancient World + TTL + 2025-01-22 + Creative Commons 4.0 + https://www.dainst.org/forschung/projekte/noslug/2098 + Arts and Humanities + Numismatics + + + OBOE + Extensible Observation Ontology (OBOE) + The Extensible Observation Ontology (OBOE) is a formal ontology for capturing the semantics of scientific observation and measurement. The ontology supports researchers to add detailed semantic annotations to scientific data, thereby clarifying the inherent meaning of scientific observations. + The Regents of the University of California + OWL + Creative Commons 3.0 + https://terminology.tib.eu/ts/ontologies/OBOE + Scholarly Knowledge + Scientific Observation + 1.2 + + + BBCPolitics + BBC Politics News Ontology (BBCPolitics) + The Politics Ontology describes the concepts that occur in BBC politics news. + https://www.r4isstatic.com/ + TTL + 2014-01-06 + Creative Commons 4.0 + https://www.bbc.co.uk/ontologies/politics-ontology + News and Media + Politics + 0.9 EFO @@ -2218,16 +2124,123 @@ Biology 3.75.0 - - PATO - Phenotype and Trait Ontology (PATO) - An ontology of phenotypic qualities (properties, attributes or characteristics). + + SIOC + Semantically-Interlinked Online Communities (SIOC) + The SIOC (Semantically-Interlinked Online Communities) Ontology is an ontology for describing the information in online communities. This includes sites that support online discussions, blogging, file sharing, photo sharing, social networking, etc. + Data Science Institute, NUI Galway + RDF + 2018/02/28 + Creative Commons 3.0 + http://rdfs.org/sioc/spec/ + Social Sciences + Social Networks + 1.36 + + + DoCO + Document Components Ontology (DoCO) + DoCO, the Document Components Ontology, is an OWL 2 DL ontology that provides a general-purpose structured vocabulary of document elements. DoCO has been designed as a general unifying ontological framework for describing different aspects related to the content of scientific and other scholarly texts. Its primary goal has been to improve the interoperability and shareability of academic documents (and related services) when multiple formats are actually used for their storage. + David Shotton and Silvio Peroni + RDF + 2015-07-03 + Creative Commons 4.0 + http://www.sparontologies.net/ontologies/doco + Education + document components + 1.3 + + + TimelineOntology + Timeline Ontology (TimelineOntology) + The Timeline Ontology is centered around the notion of timeline, seen here as a way to identify a temporal backbone. A timeline may support a signal, a video, a score, a work, etc. + Christopher Sutton, Yves Raimond, Matthias Mauch + RDF + 25th October 2007 + Creative Commons 1.0 + https://github.com/motools/timelineontology + Arts and Humanities + Music Theory + 1.0 + + + YAGO + YAGO Ontology (YAGO) + YAGO is a large semantic knowledge base, derived from Wikipedia, WordNet, and GeoNames. It contains knowledge about more than 10 million entities and contains more than 120 million facts about these entities. YAGO is special in several ways: It has a clean taxonomy, which was manually built, and it is the only knowledge base with such a large coverage, the clean taxonomy, and the extraction from Wikipedia, WordNet, and GeoNames. + Max Planck Institute for Informatics + TTL + April, 2024 + Creative Commons 3.0 + https://yago-knowledge.org/downloads/yago-4-5 + General Knowledge + People, Cities, Countries, Movies, Organizations + 4.5 + + + GeoNames + GeoNames Ontology (GeoNames) + The Geonames ontologies provides elements of description for geographical features, in particular those defined in the geonames.org database. + Bernard Vatant + RDF + 2022-01-30 + Creative Commons 3.0 + https://www.geonames.org/ontology + Geography + Geographic Knowledge + 3.3 + + + TribAIn + Tribology and Artificial Intelligence Ontology (TribAIn) + TribAIn is an ontology for the description of tribological experiments and their results. It is designed to be used in the context of the TribAIn project, which aims to develop a knowledge-based system for the design of tribological systems. + Patricia Kügler + TTL + Creative Commons 4.0 + https://github.com/snow0815/tribAIn + Scholarly Knowledge + + + SEPIO + Scientific Evidence and Provenance Information Ontology (SEPIO) + The SEPIO ontology is in its early stages of development, undergoing iterative refinement as new requirements emerge and alignment with existing standards is explored. The SEPIO core file imports two files which can be resolved at the URLs below: IAO ontology-metadata import: https://raw.githubusercontent.com/monarch-initiative/SEPIO-ontology/master/src/ontology/imports/ontology-metadata.owl bfo mireot: https://raw.githubusercontent.com/monarch-initiative/SEPIO-ontology/master/src/ontology/mireots/bfo-mireot.owl OWL - 2025-02-01 + 2015-02-23 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/PATO - Biology and Life Sciences - Biology - 1.2 + https://terminology.tib.eu/ts/ontologies/SEPIO + Scholarly Knowledge + Scientific Evidence + + + RXNO + Reaction Ontology (RXNO) + RXNO is the name reaction ontology. It contains more than 500 classes representing organic reactions such as the Diels–Alder cyclization. + OWL + 2021-12-16 + Creative Commons 4.0 + https://github.com/rsc-ontologies/rxno + Chemistry + + + MechanicalTesting + Mechanical Testing Ontology (MechanicalTesting) + A domain ontology for mechanical testing based on EMMO. + Fraunhofer IWM + OWL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/domain-mechanical-testing + Materials Science and Engineering + Mechanical Testing + 1.0.0 + + + FRAPO + Funding, Research Administration and Projects Ontology (FRAPO) + The Funding, Research Administration and Projects Ontology (FRAPO) is an ontology for describing the administrative information of research projects, e.g., grant applications, funding bodies, project partners, etc. + David Shotton + RDF + Creative Commons 4.0 + http://www.sparontologies.net/ontologies/frapo + Scholarly Knowledge + Administration \ No newline at end of file diff --git a/ontolearner/VERSION b/ontolearner/VERSION index 079d7f69..26ca5946 100644 --- a/ontolearner/VERSION +++ b/ontolearner/VERSION @@ -1 +1 @@ -1.4.11 +1.5.1 diff --git a/pyproject.toml b/pyproject.toml index 9ab23c55..7488d86f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,10 +28,10 @@ huggingface-hub = "^0.34.4" torch = "^2.8.0" transformers = "^4.56.0" sentence-transformers = "^5.1.0" -dspy = "^2.6.14" +dspy = "^3.1.3" bitsandbytes = { version = ">=0.45.1,<1.0.0", markers = "platform_system == 'Linux'" } mistral-common = { version = "^1.8.5", extras = ["sentencepiece"] } -protobuf = "<5" +protobuf = "^6.33.5" Levenshtein = "*" gensim="*"