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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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- geography |
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pretty_name: Geography |
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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;">Geography 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 geography domain encompasses the structured representation and analysis of spatial, environmental, and geopolitical phenomena, focusing on the precise modeling of physical locations, territorial boundaries, and place names. This domain is pivotal in knowledge representation as it facilitates the integration and interoperability of geographic information across diverse systems, enabling advanced spatial reasoning and decision-making. By providing a formal framework for understanding the complex relationships between geographic entities, this domain supports a wide range of applications, from urban planning to environmental monitoring. |
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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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| GEO | Geographical Entities Ontology (GEO) | 397 | 75 | 2019-02-17| |
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| GeoNames | GeoNames Ontology (GeoNames) | 7 | 30 | 2022-01-30| |
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| GTS | Geologic Timescale model (GTS) | 40 | 12 | 2020-05-31| |
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| Juso | Juso Ontology (Juso) | 30 | 24 | 2015-11-10| |
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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 GEO |
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ontology = GEO() |
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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 GEO, LearnerPipeline, train_test_split |
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# Load the GEO ontology, which contains concepts related to wines, their properties, and categories |
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ontology = GEO() |
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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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