IBGraphRAG: Enhancing Medical Knowledge Graph Retrieval Based on Semantic Consistency and Information Bottleneck

Large Language Models (LLMs) have achieved remarkable progress in recent years, but they still struggle to generate reliable and precise responses in domains such as medicine that rely heavily on specialized knowledge. Retrieval-Augmented Generation (RAG) offers a scalable solution for integrating external knowledge into LLMs without additional training, and while it performs well in general domains, its effectiveness is limited in medical settings that demand high terminological precision and factual consistency. To address this, we propose a medical-domain-oriented RAG framework, IBGraphRAG, which integrates medical knowledge graphs with two key technical innovations: (1) Medical Semantic Consistency Alignment, which improves entity recognition and linking by enforcing semantic consistency with a structured medical knowledge base; and (2) Information Bottleneck-based Reasoning Path, which prioritizes retaining highly relevant contextual information during knowledge graph retrieval while avoiding irrelevant or superficial paths. Experimental results show that IBGraphRAG achieves state-of-the-art performance on multiple medical question-answering benchmark datasets, effectively improves the specialization and accuracy of the retriever in selecting reasoning paths over the knowledge graph, helping the LLM better identify relevant knowledge for reasoning. Here is our code.