Application of Knowledge Graph-Based GraphRAG in Intelligent Question Answering Systems

Abstract

Large language models (LLMs) are developing rapidly and have been widely applied in intelligent question answering, knowledge retrieval, education, healthcare, enterprise services, and other fields. However, LLMs still exhibit limitations in knowledge updating, understanding complex relationships, and tracing answer sources. This paper adopts a literature review approach to survey knowledge graph-enhanced GraphRAG. It analyzes the indexing, retrieval, and generation workflows, as well as key techniques including graph traversal, community summarization, hybrid retrieval, and graph-augmented generation, and discusses applications in medical question answering and enterprise knowledge base question answering. Existing studies indicate that GraphRAG can use entity relations and hierarchical summaries to mitigate the fragmented context, limited multi-hop reasoning, and insufficient global information coverage of conventional RAG. Nevertheless, challenges remain in dynamic graph maintenance, semantic alignment, cost, and governance for trustworthiness.