{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T23:10:27Z","timestamp":1779405027421,"version":"3.53.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686615","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"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":[[2026,5,21]]},"abstract":"<jats:p>SNOMED CT is one of the largest and most widely used medical ontologies. It is a collection of medical concepts with a large number of relationships between concept pairs. One of the most laborious tasks for SNOMED CT curators is placing concepts in the correct hierarchical positions with accurate relationships. We present a method that utilizes domain-specific BERT models integrated with a Relational Graph Convolutional Network (RGCN) for classifying the relationships between SNOMED CT concept pairs. We generate node embeddings using five domain-specific BERT models, BioBERT, ClinicalBERT, SapBERT, SciBERT, and BioMedBERT, and feed them individually into an RGCN model for relationship classification. We apply a three-layer RGCN to capture and utilize the graph-structured dependencies among concepts, enabling the model to propagate relational information across connected nodes before performing classification. We demonstrate that each BERT embedding integrated with RGCN outperforms both the Message-Passing Neural Network (MPNN) and Neural Network (NN) baselines. Our work shows that SapBERT-RGCN achieves the best performance across all tested encoders, with an accuracy of 0.9394 and an F1-weighted score of 0.9412. Compared to the best-performing domain-specific BERT-NN baseline, this is a 4% accuracy gain with a notable improvement of F1-macro.<\/jats:p>","DOI":"10.3233\/shti260706","type":"book-chapter","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T22:13:29Z","timestamp":1779401609000},"source":"Crossref","is-referenced-by-count":0,"title":["Relational Graph Convolutional Network with BERT Embeddings for Ontology Relationship Classification"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5720-450X","authenticated-orcid":false,"given":"T.M. Rubaith","family":"Bashar","sequence":"first","affiliation":[{"name":"Department of Data Science, New Jersey Institute of Technology, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9120-525X","authenticated-orcid":false,"given":"James","family":"Geller","sequence":"additional","affiliation":[{"name":"Department of Data Science, New Jersey Institute of Technology, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3627-8261","authenticated-orcid":false,"given":"Mengjia","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Data Science, New Jersey Institute of Technology, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Opening the Personal Gate between Technology and Health Care"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260706","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T22:13:29Z","timestamp":1779401609000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260706"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,21]]},"ISBN":["9781643686615"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260706","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,21]]}}}