Biomedical entity linking improves without training on special data
Training-Free BioNEL-RAG: Advancing Biomedical Entity Linking through Retrieval-Augmented Generation and Large Language Model
Computation and Language
Summary
Linking names of things like genes or diseases in medical texts to their official entries is important but usually needs lots of specific training. The authors propose a method called BioNEL-RAG that doesn't require extra training for different medical topics. It works by first finding possible matches and then using a large language model to pick the best one based on the text around the mention. This approach performs just as well as other methods that need tailored training for each topic.
What this means in practice
- •For biomedical software developers: Develop systems that link biomedical terms to standard databases across multiple subfields without retraining for each domain.
- •For healthcare data engineers: Integrate entity linking that adapts to diverse biomedical data sources without costly fine-tuning on specialized datasets.
Authors
Ziyi Chen, Hui Liu, Ziyang Ling, Ronald X. Xu, Mingzhai Sun
Abstract
Biomedical Named Entity Linking (BioNEL) is the task of mapping mentions extracted from biomedical texts to canonical entities or concepts in the standardized knowledge base. Although existing methods have achieved remarkable performance in mapping accuracy, most rely on training with domain-specific datasets, which fundamentally limits their cross-domain adaptability. To address this limitation, we propose BioNEL-RAG, a novel training-free framework that combines retrieval-augmented generation (RAG) with large language models (LLMs). Within this framework, the RAG module focuses on retrieving candidate entities, while the large language model module plays the role of selecting the most appropriate candidate for entity normalization, which is based on the analysis of the mention's context, with the aid of its remarkable text comprehension capabilities. Experimental results demonstrate that BioNEL-RAG achieves performance comparable to existing methods without requiring training on subdomain datasets, whereas these methods necessitate fine-tuning on specific subdomains such as genes, diseases, or chemical compounds.