A retrieval-augmented knowledge mining method with deep thinking LLMs for biomedical research and clinical support.

ABSTRACT:

Background

Knowledge graphs and large language models (LLMs) are key tools for biomedical knowledge integration and reasoning, facilitating structured organization of scientific articles and discovery of complex semantic relationships. However, current methods face challenges: knowledge graph construction is limited by complex terminology, data heterogeneity, and rapid knowledge evolution, while LLMs show limitations in retrieval and reasoning, making it difficult to uncover cross-document associations and reasoning pathways.

Results

We propose a pipeline that uses LLMs to construct a Biomedical Stratified Knowledge Graph (BioStrataKG) from large-scale articles and builds the Biomedical Cross-Document Question Answering Dataset (BioCDQA) to evaluate latent knowledge retrieva

SUBMITTER: Feng Y 

PROVIDER: S-EPMC12448786 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature