Articlenpj health systems2026
OntoCodex: a multi-agent biomedical ontology enrichment framework.
Article in npj health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
11 authors.
Funding
Abstract
Ontology enrichment is a critical but labor-intensive step in semantic knowledge representation. To address this challenge, we propose OntoCodex, a multi-agent framework that integrates large language models (LLMs), ontologies, curated knowledge sources, and standard vocabularies to support semi-automated ontology enrichment with formal OWL-based integration and a feedback loop. OntoCodex consists of five coordinated agents for ontology parsing, task decision-making, knowledge retrieval, terminology normalization, and automated script generation. We evaluated OntoCodex using a ChatGPT-4o-powered implementation to enrich concepts across five chronic diseases, including stroke, chronic obstructive pulmonary disease, atrial fibrillation, osteoporosis, and Parkinson's disease. Compared with baseline ChatGPT-4o, OntoCodex improved concept extraction across most domains, achieving higher precision, recall, and F1 scores, including perfect performance in laboratory test extraction, and demonstrated greater accuracy in standardized terminology mapping, particularly for medications, while showing lower performance in laboratory test mapping. Automatically generated Python scripts successfully enriched the MCC-CDO with new concepts and annotations without errors. These results demonstrate that OntoCodex substantially improves ontology enrichment and has strong potential to accelerate clinical and translational research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.