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2 results for “Biomedical Ontology”
Ontology Enrichment from Texts (OET): A Biomedical Dataset for Concept Discovery and Placement
<p>A biomedical dataset supporting ontology enrichment from texts, by concept discovery and placement, adapting the MedMentions dataset (PubMed abstracts) with SNOMED CT of versions in 2014 and 2017 under the Diseases (disorder) sub-category and the broader categories of Clinical finding, Procedure, and Pharmaceutical / biologic (CPP) product.</p> <p>The dataset is documented in the work, <em>Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement</em>, on arXiv: <a href="https://arxiv.org/abs/2306.14704">https://arxiv.org/abs/2306.14704</a> (CIKM 2023). The companion code is available at https://github.com/KRR-Oxford/OET.</p> <p>Out-of-KB mention discovery (including the settings of mention-level data) is further partly documented in the work, <em>Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking</em>, on arXiv: <a href="https://arxiv.org/abs/2302.07189">https://arxiv.org/abs/2302.07189</a> (CIKM 2023).</p> <p>ver4: we made a version of mention-level data for out-of-KB discovery and concept placement separately: the former (for out-of-KB discovery) has out-of-KB mentions in training data, while the latter (for concept placement) has only out-of-KB mentions during the evaluation (validation and test) and not in the training data. Also, we split the original "test-NIL.jsonl" (now "test-NIL-all.jsonl") into "valid-NIL.jsonl" and "test-NIL.jsonl" for a better evaluation.</p> <p>ver3: we revised and updated mention-level data (syn_full, synonym augmentation setting) and the folder structure, and also updated the edge catalogues with complex edges.</p> <p>ver2: we revised the mention-level data by only keeping out-of-KB mentions (or "NIL" mentions) associated with one-hop edges (including leaf nodes, as <leaf node, NULL>) and two-hop edges in the ontology (SNOMED CT 20140901).</p> <p>Acknowledgement of data sources and tools below:</p> <p>* SNOMED CT https://www.nlm.nih.gov/healthit/snomedct/archive.html (and use snomed-owl-toolkit to form .owl files)<br>* UMLS https://www.nlm.nih.gov/research/umls/licensedcontent/umlsarchives04.html (and mainly use MRCONSO for mapping UMLS to SNOMED CT)<br>* MedMentions https://github.com/chanzuckerberg/MedMentions (source of entity linking)</p> <p>* Protégé http://protegeproject.github.io/protege/<br>* snomed-owl-toolkit https://github.com/IHTSDO/snomed-owl-toolkit<br>* DeepOnto https://github.com/KRR-Oxford/DeepOnto (based on OWLAPI https://owlapi.sourceforge.net/) for ontology processing and complex concept verbalisation</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>
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