Ontology Hosting¶
Ontology Repositories¶
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.
Hint
Each ontology repository on Hugging Face includes comprehensive documentation:
README.md: Contains information about the domain and available ontologies
Usage Examples: Code snippets showing how to use the ontologies
See the SciKnowOrg/ontolearner-agriculture repository as an example.
The complete list of OntoLearner domain repositories is shown below:
🏷️ Domain |
🤗 Hugging Face Repository |
📝 Description |
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Ontologies about farming systems, crops, food production, and agricultural vocabularies. |
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Ontologies that describe music, iconography, cultural artifacts, and humanistic content. |
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Ontologies about biological entities, systems, organisms, and molecular biology. |
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Ontologies describing chemical entities, reactions, methods, and computational chemistry models. |
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Ontologies about ecological systems, environments, biomes, and sustainability science. |
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Ontologies describing learning content, educational programs, competencies, and teaching resources. |
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Ontologies for representing events, time, schedules, and calendar-based occurrences. |
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Ontologies describing economic indicators, e-commerce, trade, and financial instruments. |
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Ontologies related to food, beverages, ingredients, and culinary products. |
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Broad-scope ontologies and upper vocabularies used across disciplines for general-purpose semantic modeling. |
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Ontologies for modeling spatial and geopolitical entities, locations, and place names. |
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Ontologies describing industrial processes, smart buildings, manufacturing systems, and equipment. |
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Ontologies dealing with legal processes, regulations, and rights (e.g., copyright). |
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Ontologies used in cataloging, archiving, and authority control of cultural and scholarly resources. |
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Ontologies related to materials, their structure, properties, processing, and engineering applications. |
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Ontologies covering clinical knowledge, diseases, drugs, treatments, and biomedical data. |
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Ontologies that model journalism, broadcasting, creative works, and media metadata. |
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Ontologies modeling the structure, process, and administration of scholarly research, publications, and infrastructure. |
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Ontologies for modeling societal structures, behavior, identity, and social interaction. |
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Ontologies defining scientific units, quantities, dimensions, and observational models. |
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Foundational ontologies that provide abstract concepts like objects, processes, and relations. |
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Ontologies that model web semantics, linked data, APIs, and online communication standards. |
Metrics Space¶
OntoLearner offers an interactive Metrics Space on HuggingFace that visualizes ontology metrics across many benchmarked domains. Available at the SciKnowOrg/OntoLearner‑Benchmark‑Metrics space, it displays structured metrics for each ontology—such as total nodes, edges, classes, hierarchy depth, breadth, and dataset benchmarks like term types and relations Researchers can explore ontology complexity and coverage visually, compare across domains, and validate suitability before using ontologies in learning tasks.
Note
You can download the full ontology metrics Excel file from this link.
Hint
This file is automatically updated whenever a new ontology is modularized, ensuring you always have the latest benchmark metrics.