{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T01:38:31Z","timestamp":1777772311271,"version":"3.51.4"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:00:00Z","timestamp":1771545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022R1A2C201136011"],"award-info":[{"award-number":["2022R1A2C201136011"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop and evaluate a human-LLM (Large Language Model) collaborative approach for systematic ontology updating, demonstrated with the Dietary Lifestyle Ontology (DILON).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>One hundred dietary questionnaire items from English and Korean sources were semantically annotated by 4 state-of-the-art language models, which generated candidate concepts for inclusion into DILON. Outputs were refined through cross-model reconciliation, followed by expert review. The model curated the concept within DILON and experts reviewed and refined the outputs in Prot\u00e9g\u00e9 to ensure accuracy and consistency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Claude Sonnet 4 effectively supported local tasks, including harvesting new concepts, detecting redundancies, and refining hierarchical segments. Global optimization of ontology, however, required systematic examination by human experts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings highlight the complementary strengths of LLMs and humans: LLMs accelerate repetitive and local updates, whereas humans maintain overall structural integrity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Human-LLM collaboration improves efficiency, scalability, and sustainability in ontology engineering, supporting the maintenance of complex biomedical ontologies.<\/jats:p>\n                  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