{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:57Z","timestamp":1761176277401,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Despite the growing availability of biomedical ontologies, gaps in their semantic coverage still hinder effective integration and knowledge discovery. Many existing matching and enrichment strategies rely heavily on manual labor or shallow lexical similarity, which limits their ability to capture nuanced semantic connections. In this work, we propose a method to semantically enrich biomedical ontologies such as those related to diseases, symptoms, and drugs and to integrate diverse biomedical concepts into systems like the UMLS. Our approach leverages BioSTransformers, a set of siamese neural network models that we trained specifically on biomedical corpora, enabling the identification of previously unknown, meaningful relations between concepts. The validation of the new relations relies on the use of domain knowledge sources, a large language model, and manual validation by domain experts. Results obtained show encouraging potential for advancing automated ontology matching and enrichment.<\/jats:p>","DOI":"10.3233\/faia251345","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:58:53Z","timestamp":1761127133000},"source":"Crossref","is-referenced-by-count":0,"title":["Biomedical Ontologies Matching and Enrichment with Language Models"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2204-7786","authenticated-orcid":false,"given":"Safaa","family":"Menad","sequence":"first","affiliation":[{"name":"Univ Rouen Normandie, Normandie Univ, LITIS UR 4108, Rouen, F-76000, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7521-7955","authenticated-orcid":false,"given":"Sa\u00efd","family":"Abdedda\u00efm","sequence":"additional","affiliation":[{"name":"Univ Rouen Normandie, Normandie Univ, LITIS UR 4108, Rouen, F-76000, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7668-2819","authenticated-orcid":false,"given":"Lina F.","family":"Soualmia","sequence":"additional","affiliation":[{"name":"Univ Rouen Normandie, Normandie Univ, LITIS UR 4108, Rouen, F-76000, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251345","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:58:53Z","timestamp":1761127133000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251345","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}