融合知识图谱和大模型的医疗智能问答方法研究

Research on Medical Intelligent Question Answering Method Combining Knowledge Graph and Big Model

摘要

医疗领域的自动问答对答案的准确性有很高的要求.尽管大语言模型(LLM)提供了通用的问答能力,但是无法满足医疗领域对答案准确性的要求.与此相对,基于知识图谱检索的自动问答依赖于客观的知识表达对回答质量提供了可靠性保证,然而目前的方法中存在知识图谱检索效率不高、检索不充分、检索过多冗余信息,以及对较复杂的问题理解不充分进而影响检索的质量等问题.为此,本文中将知识图谱与LLM相结合,基于LLM对用户的提问进行问题分解,对每一个子问题在知识图谱中进行子图搜索,再将融合后的子图交给LLM以生成可靠的答案.文中的方法在医疗数据集GenMedGPT-5k、LiveQA、HealthCareMagic-100k和知识图谱FB15k-237上进行了实验.实验表明,文中的方法取得了较好的性能.

Abstract

In the medical area,automated question answering places high demands on the accuracy of answers.Although large language models (LLMs) provide general-purpose question-answering capabilities,they cannot meet the stringent accuracy requirements of the medical domain.In contrast,knowledge graph-based automated question answering relies on objective knowledge representation to ensure answer reliability.However,current methods suffer from issues such as inefficient knowledge graph retrieval,insufficient coverage,excessive redundant information,and inadequate understanding of complex questions,which negatively impact retrieval quality.To address these challenges,this paper integrates knowledge graphs with LLMs.Specifically,the LLM is used to decompose user questions into sub-questions,each of which is then subjected to subgraph retrieval in the knowledge graph.The merged subgraphs are then fed back to the LLM to generate reliable answers.The proposed method is evaluated on medical datasets (GenMedGPT-5k,LiveQA,HealthCareMagic-100k) and the knowledge graph FB15k-237.Experimental results demonstrate that the proposed approach achieves superior performance.

关键词

大语言模型 / 知识图谱 / 检索增强 / 知识子图

Key words

Large Language Model (LLM) / knowledge graph / search enhancement / knowledge subgraph

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基金资助

上海交通大学医工交叉项目(YG2024QNB05)资助.