Journal of the American Medical Informatics Association : JAMIA · 2026
Human-Large Language Model collaboration for systematic ontology updates: a case study in the domain of dietary lifestyle.
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Jung Jinsun, Taira Ricky, Kim Hyeoneui
Аннотация
OBJECTIVES: To develop and evaluate a human-LLM (Large Language Model) collaborative approach for systematic ontology updating, demonstrated with the Dietary Lifestyle Ontology (DILON).
MATERIALS AND METHODS: 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 Protégé to ensure accuracy and consistency.
RESULTS: 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.
DISCUSSION: These findings highlight the complementary strengths of LLMs and humans: LLMs accelerate repetitive and local updates, whereas humans maintain overall structural integrity.
CONCLUSION: Human-LLM collaboration improves efficiency, scalability, and sustainability in ontology engineering, supporting the maintenance of complex biomedical ontologies.
MATERIALS AND METHODS: 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 Protégé to ensure accuracy and consistency.
RESULTS: 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.
DISCUSSION: These findings highlight the complementary strengths of LLMs and humans: LLMs accelerate repetitive and local updates, whereas humans maintain overall structural integrity.
CONCLUSION: Human-LLM collaboration improves efficiency, scalability, and sustainability in ontology engineering, supporting the maintenance of complex biomedical ontologies.