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3 results for “Ontology Learning”
Supporting Data for "Exploring ChatGPT-4 for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Development"
<p>Data from the trials with ChatGPT to generate OWL files for taxonomic data from the GBIF Backbone Taxonomy.</p> <p>Updates of version 2: additional prompts from the experiments with Gemini and DeepSeek.</p>
Bio-ML: Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
<p> </p> <blockquote> <p><strong>This version is used in the Bio-ML track of the OAEI 2024; the only change compared to the OAEI 2023 is the deletion of certain training subsumption mappings.</strong></p> </blockquote> <p> </p> <h3><strong>Overview</strong></h3> <p>The purpose of these datasets is to support <em>equivalence</em> and <em>subsumption</em> ontology matching.</p> <p>There are five ontology pairs extracted from MONDO and UMLS:</p> <table> <tbody> <tr> <td>Source</td> <td>Task</td> <td>Category</td> <td>#SrcCls</td> <td>#TgtCls</td> <td>#Ref (equiv)</td> <td>#Ref (subs)</td> </tr> <tr> <td>Mondo</td> <td>OMIM-ORDO</td> <td>Disease</td> <td>9,648</td> <td>9,275</td> <td>3,721</td> <td>103</td> </tr> <tr> <td>Mondo</td> <td>NCIT-DOID</td> <td>Disease</td> <td>15,762</td> <td>8,465</td> <td>4,686</td> <td>3,338 (-1)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-FMA</td> <td>Body</td> <td>34,418</td> <td>88,955</td> <td>7,256</td> <td>5,453 (-53)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-NCIT</td> <td>Pharm</td> <td>29,500</td> <td>22,136</td> <td>5,803</td> <td>4,224 (-1)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-NCIT</td> <td>Neoplas</td> <td>22,971</td> <td>20,247</td> <td>3,804</td> <td>213</td> </tr> </tbody> </table> <p>The "-" numbers reflect the changes due to lthe deletion of certain training subsumption mappings.</p> <p>The main track is available at "bio-ml", where each pair is associated with a task folder, containing the source and target ontologies, reference equivalence mappings (in "refs_equiv"), reference subsumption mappings ("refs_subs"). </p> <p>The special sub-track is available at "bio-llm", where each pair is associated with a task folder, containing the source and target ontologies, and the test candidate mappings. </p> <p> </p> <h3><strong>Citation</strong></h3> <p><strong>Bio-ML (Main Track)</strong></p> <pre>```<br>@inproceedings{he2022machine, title={Machine learning-friendly biomedical datasets for equivalence and subsumption ontology matching}, author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Jim{\'e}nez-Ruiz, Ernesto and Hadian, Ali and Horrocks, Ian}, booktitle={International Semantic Web Conference}, pages={575--591}, year={2022}, organization={Springer} }<br>```</pre> <p><strong>Bio-LLM (Sub-track)</strong></p> <pre>```<br>@article{he2023exploring, title={Exploring large language models for ontology alignment}, author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Horrocks, Ian}, journal={arXiv preprint arXiv:2309.07172}, year={2023} }<br>```</pre> <p> </p> <h3><strong>Important Links</strong></h3> <ul> <li>See detailed documentation at: <a href="https://krr-oxford.github.io/DeepOnto/bio-ml">https://krr-oxford.github.io/DeepOnto/bio-ml</a>.</li> <li>See the OAEI Bio-ML track at: <a href="https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/">https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/</a></li> <li>See our resource paper for the original Bio-ML at <a href="https://arxiv.org/abs/2205.03447">arxiv</a> or <a href="https://link.springer.com/chapter/10.1007/978-3-031-19433-7_33">springer</a> (accepted at <em>ISWC-2022</em> and nominated as the <em>best resource paper candidate</em>). See our poster paper for the Bio-LLM sub-track at <a href="https://arxiv.org/abs/2309.07172">arxiv </a>(accepted at <em>ISWC-2023 Posters & Demos</em>).</li> </ul> <p> </p> <h3><strong>Changelog</strong></h3> <p>The only change in this version compared to the OAEI 2023 is the deletion of certain training subsumption mappings that can be directly exploited through deductive reasoning.</p>
LLMs4OL 2024 Datasets: Toward Ontology Learning with Large Language Models
<p>Ontology learning (OL) from unstructured data has evolved significantly, with recent advancements integrating large language models (LLMs) to enhance various aspects of the process. The LLMs4OL 2024 datasets, were developed to benchmark and advance research in OL using LLMs. This dataset as a key component of the LLMs4OL Challenge, targets three primary OL tasks: Term Typing, Taxonomy Discovery, and Non-Taxonomic Relation Extraction. It encompasses seven domains, i.e. lexosemantics and biological functions, offering a comprehensive resource for evaluating LLM-based OL approaches Each task within the dataset is carefully crafted to facilitate both Few-Shot (FS) and Zero-Shot (ZS) evaluation scenarios, allowing for robust assessment of model performance across different knowledge domains to address a critical gap in the field by offering standardized benchmarks for fair comparison for evaluating LLM applications in OL. </p>
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