TaxEL: Taxonomy-Enhanced Entity Representation Learning for Biomedical Entity Linking.
TL;DR
TaxEL introduces Taxonomy-Guided Contrastive Sampling (TGCS), which systematically integrates both local ontology structure and global semantic similarity to generate informative positive and hard negative samples for each mention; and Structured Semantic Alignment Loss (SSAL), which enforces alignment between model predictions and fine-grained semantic distributions derived from the taxonomy, enabling explicit control over prediction granularity.
Abstract
Biomedical entity linking (BioEL) aims to map textual mentions to standardized concepts in biomedical ontologies. While existing methods have made significant progress, most rely on binary supervision, indiscriminately penalizing all non-gold candidates and overlooking the rich hierarchical structure inherent in biomedical taxonomies. This restricts their ability to capture nuanced semantic relationships and adapt to varying degrees of entity similarity. We propose Taxonomy-Enhanced Entity Linking (TaxEL), a novel framework that unifies taxonomy-guided candidate sampling and structure-aware distributional supervision for BioEL. Specifically, TaxEL introduces: (1) Taxonomy-Guided Contrastive Sampling (TGCS), which systematically integrates both local ontology structure and global semantic similarity to generate informative positive and hard negative samples for each mention; and (2) Structured Semantic Alignment Loss (SSAL), which enforces alignment between model predictions and fine-grained semantic distributions derived from the taxonomy, enabling explicit control over prediction granularity. Experimental results on five public BioEL benchmarks demonstrate that TaxEL achieves state-of-the-art performance in Acc@1, and ablation studies confirm the critical role of both TGCS and SSAL. The TaxEL web service is publicly accessible at http://www.el.tcmkg.com, and all associated data and code can be obtained from https://github.com/TCMAI-BJTU/TaxEL.