Iterative Knowledge Graph Refinement and Integration for Medical Question Answering

Large Language Models (LLMs) are challenged by generating hallucinations and factually incorrect responses, particularly in complex and specialized medical question answering (QA). Integrating knowledge graphs (KGs) through retrieval-augmented generation (RAG) methods has emerged as a promising direction. However, existing graph-based RAG methods heuristically retrieve and refine questionrelevant subgraphs, potentially introducing redundant and noisy factual information that is difficult for LLMs to process, ultimately limiting reasoning capability. To incorporate a concise yet informative evidence subgraph, we propose an iterative medical QA framework. It optimizes graph-based RAG methods by selectively retrieving focused knowledge from KGs to construct a precise evidence subgraph and progressively pruning it utilizing structured feature representations. The targeted KG integration maintains coherent and reliable inference. Experiments on three medical QA benchmark datasets demonstrate that the framework achieves state-of-the-art performance against representative baseline competitors, highlighting the importance of efficient KG integration.