Work

Human-Large Language Model collaboration for systematic ontology updates: a case study in the domain of dietary lifestyle

Year: 2026

Type: article

Abstract: 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.

Source: Journal of the American Medical Informatics Association

DOI: 10.1093/jamia/ocag015

Authors: Jinsun Jung, Ricky Taira, Hyeoneui Kim

Institutions: Seoul National University, University of California, Los Angeles

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Research Foundation of Korea

Award: G8867870189