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To overcome lexical variation and subtle hierarchical distinctions in biomedical concept normalization, the authors created OntologyAligner alongside PhenoNormBench, a standardized 13,390-sample benchmark spanning seven Human Phenotype Ontology datasets. The system couples ontology-aligned retrieval with LLM candidate reranking and graph-hierarchy refinement to resolve fine-grained ambiguities among closely related terms. It establishes a new state-of-the-art on HPO normalization with 88.78% Macro Top-1 and 86.75% Micro Top-1 accuracy鈥攐utperforming prior methods by roughly 5 percentage points鈥攚hile generalizing effectively to MONDO, MEDIC, and NCBITaxon.
Grounding LLM candidate reranking in explicit ontology hierarchies closes a stubborn 5% accuracy gap in fine-grained biomedical concept normalization.
Biomedical ontology normalization maps free-text expressions to standardized concepts, enabling consistent integration and analysis of biomedical data. This task remains challenging because lexical variation and subtle distinctions among hierarchically related concepts can obscure concept boundaries. We present OntologyAligner, a three-stage framework that combines ontology-aligned retrieval, large language model candidate reranking, and selective hierarchy-guided refinement. We also construct PhenoNormBench, a unified benchmark comprising 13,390 samples from seven Human Phenotype Ontology datasets. OntologyAligner achieved state-of-the-art performance on HPO normalization, with 88.78% Macro Top-1 Accuracy and 86.75% Micro Top-1 Accuracy, exceeding the strongest baseline by 4.85 and 5.07 percentage points, respectively. Ablation analyses showed complementary contributions from all three stages, and sensitivity analyses demonstrated stability across candidate-set sizes and model backbones. Applications to MONDO, MEDIC, and NCBITaxon further established portability to other ontologies. OntologyAligner offers a generalizable framework for accurate mapping of biomedical text to structured ontology concepts. PhenoNormBench and the code are publicly available at https://github.com/zhelishisongjie/OntologyAligner.
Amazon Science