BioELX: Cross-lingual Biomedical Entity Linking via Alias-based Retrieval and LLM Ranking

Researchers have developed BioELX, a two-stage cross-lingual biomedical entity linking framework that requires no task-specific annotated training data. The system improves candidate retrieval by enriching SapBERT with multilingual aliases from Wikidata and performs context-aware disambiguation using a pre-trained LLM ranker. BioELX achieved state-of-the-art performance across five benchmarks, with Recall@1 gains of up to +30.8 on low-resource languages like Thai and +22.1 on Korean.

arXiv:2605.27380v1 Announce Type: new Abstract: Cross-lingual biomedical entity linking BEL maps mentions in any language to unique identifiers in a biomedical knowledge base KB , supporting clinical and biomedical NLP applications. However, expert-annotated training data for BEL are costly, especially for low-resource languages. Moreover, many cross-lingual BEL systems rely on SapBERT-based retrievers trained on predominantly English aliases in the KB, leading to poor generalization to unseen non-English mentions and limited context-aware disambiguation. We propose BioELX, a two-stage cross-lingual BEL framework that requires no task-specific annotated training corpora. In Stage~1, we enrich SapBERT training with Wikidata-derived multilingual aliases and use the resulting retriever to improve cross-lingual candidate retrieval. In Stage~2, we perform context-aware disambiguation with a pre-trained LLM ranker that jointly considers the mention context and candidate, eliminating the need for supervised training. Experiments on five benchmarks XL-BEL, EMEA, Patent, WikiMed-DE, and MedMentions show that BioELX achieves new state-of-the-art performance. It improves average Recall@1 on XL-BEL by +19.2, with especially large gains for low-resource languages, e.g., +21.6 on Turkish, +22.1 on Korean, +30.8 on Thai, and delivers consistent improvements on EMEA +6.2 , Patent +5.4 , and WikiMed-DE +12.8 . Code and resources will be released upon publication.