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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- OntoLearner |
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- ontology-learning |
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- upper-ontology |
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pretty_name: Upper Ontology |
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--- |
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<div align="center"> |
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<img src="https://raw.githubusercontent.com/sciknoworg/OntoLearner/main/images/logo.png" alt="OntoLearner" |
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style="display: block; margin: 0 auto; width: 500px; height: auto;"> |
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<h1 style="text-align: center; margin-top: 1em;">Upper Ontology Domain Ontologies</h1> |
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<a href="https://github.com/sciknoworg/OntoLearner"><img src="https://img.shields.io/badge/GitHub-OntoLearner-blue?logo=github" /></a> |
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</div> |
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## Overview |
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The upper ontology, also known as a foundational ontology, encompasses a set of highly abstract, domain-independent concepts that serve as the building blocks for more specialized ontologies. These ontologies provide a structured framework for representing fundamental entities such as objects, processes, and relations, facilitating interoperability and semantic integration across diverse domains. By establishing a common vocabulary and set of principles, upper ontologies play a crucial role in enhancing the consistency and coherence of knowledge representation systems. |
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## Ontologies |
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| Ontology ID | Full Name | Classes | Properties | Last Updated | |
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|-------------|-----------|---------|------------|--------------| |
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| BFO | Basic Formal Ontology (BFO) | 84 | 40 | 2020| |
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| DOLCE | Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) | 44 | 70 | None| |
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| FAIR | FAIR Vocabulary (FAIR) | 7 | 1 | None| |
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| GFO | General Formal Ontology (GFO) | 94 | 67 | 2024-11-18| |
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| SIO | Semanticscience Integrated Ontology (SIO) | 1726 | 212 | 03/25/2024| |
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| SUMO | Suggested Upper Merged Ontology (SUMO) | 4525 | 587 | 2025-02-17| |
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## Dataset Files |
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Each ontology directory contains the following files: |
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1. `<ontology_id>.<format>` - The original ontology file |
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2. `term_typings.json` - A Dataset of term-to-type mappings |
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3. `taxonomies.json` - Dataset of taxonomic relations |
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4. `non_taxonomic_relations.json` - Dataset of non-taxonomic relations |
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5. `<ontology_id>.rst` - Documentation describing the ontology |
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## Usage |
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These datasets are intended for ontology learning research and applications. Here's how to use them with OntoLearner: |
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First of all, install the `OntoLearner` library via PiP: |
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```bash |
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pip install ontolearner |
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``` |
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**How to load an ontology or LLM4OL Paradigm tasks datasets?** |
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``` python |
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from ontolearner import BFO |
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ontology = BFO() |
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# Load an ontology. |
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ontology.load() |
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# Load (or extract) LLMs4OL Paradigm tasks datasets |
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data = ontology.extract() |
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``` |
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**How use the loaded dataset for LLM4OL Paradigm task settings?** |
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``` python |
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# Import core modules from the OntoLearner library |
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from ontolearner import BFO, LearnerPipeline, train_test_split |
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# Load the BFO ontology, which contains concepts related to wines, their properties, and categories |
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ontology = BFO() |
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ontology.load() # Load entities, types, and structured term annotations from the ontology |
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data = ontology.extract() |
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# Split into train and test sets |
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train_data, test_data = train_test_split(data, test_size=0.2, random_state=42) |
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# Initialize a multi-component learning pipeline (retriever + LLM) |
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# This configuration enables a Retrieval-Augmented Generation (RAG) setup |
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pipeline = LearnerPipeline( |
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retriever_id='sentence-transformers/all-MiniLM-L6-v2', # Dense retriever model for nearest neighbor search |
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llm_id='Qwen/Qwen2.5-0.5B-Instruct', # Lightweight instruction-tuned LLM for reasoning |
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hf_token='...', # Hugging Face token for accessing gated models |
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batch_size=32, # Batch size for training/prediction if supported |
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top_k=5 # Number of top retrievals to include in RAG prompting |
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) |
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# Run the pipeline: training, prediction, and evaluation in one call |
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outputs = pipeline( |
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train_data=train_data, |
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test_data=test_data, |
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evaluate=True, # Compute metrics like precision, recall, and F1 |
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task='term-typing' # Specifies the task |
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# Other options: "taxonomy-discovery" or "non-taxonomy-discovery" |
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) |
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# Print final evaluation metrics |
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print("Metrics:", outputs['metrics']) |
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# Print the total time taken for the full pipeline execution |
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print("Elapsed time:", outputs['elapsed_time']) |
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# Print all outputs (including predictions) |
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print(outputs) |
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``` |
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For more detailed documentation, see the [](https://ontolearner.readthedocs.io) |
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## Citation |
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If you find our work helpful, feel free to give us a cite. |
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```bibtex |
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@inproceedings{babaei2023llms4ol, |
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title={LLMs4OL: Large language models for ontology learning}, |
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author={Babaei Giglou, Hamed and D’Souza, Jennifer and Auer, S{\"o}ren}, |
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booktitle={International Semantic Web Conference}, |
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pages={408--427}, |
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year={2023}, |
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organization={Springer} |
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} |
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``` |
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