# Ontology Definition Generation

Biomedical ontologies need new textual definitions continuously, and a large language model can write definitions that read fluently and resemble curated text while still naming entities absent from the ontology or violating its logical constraints. A quality-control agentic workflow turns definition generation into an iterative process: draft a definition, extract predicate-object axioms, check whether entities resolve in the target ontology, and test logical consistency with a reasoner, then rewrite using the diagnostics. On Disease Ontology DO 2024, the Qwen2.5-14B base model's entity error rate fell from 67.0% to 37.8% after the existence-repair loop, while text-similarity scores stayed roughly level.

Biomedical ontologies need new textual definitions continuously, and a large language model can write definitions that read fluently and resemble curated text while still naming entities absent from the ontology or violating its logical constraints. A quality-control agentic workflow turns definition generation into an iterative process: draft a definition, extract predicate-object axioms, check whether entities resolve in the target ontology, and test logical consistency with a reasoner, then rewrite using the diagnostics. On Disease Ontology DO 2024, the Qwen2.5-14B base model's entity error rate fell from 67.0% to 37.8% after the existence-repair loop, while text-similarity scores stayed roughly level.