用超图结构增强知识表示，让大模型更准地回答复杂问题。
HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation
- 用超边表示多实体间复杂关系，突破传统图的二元连接限制。
- 在医学、法律等领域测试中，答案准确率显著高于现有方法。
- 适合需要理解多实体关联的复杂问答场景，如医疗诊断或法律分析。
标准检索增强生成（RAG）依赖于文本块检索，而图RAG通过基于图的知识表示加以改进。然而，现有图结构RAG受限于二元关系——普通图中每条边仅连接两个实体，难以表达现实世界中n元关系（n ≥ 2）。本文提出HyperGraphRAG，一种基于超图的新型RAG方法，通过超边表示n元关系事实，包含知识超图构建、检索与生成三个环节。在医学、农业、计算机科学和法律领域的实验表明，HyperGraphRAG在答案准确率、检索效率和生成质量方面均优于标准RAG及以往图结构RAG方法。代码与数据已公开于https://github.com/LHRLAB/HyperGraphRAG。
原文摘要 · Abstract (English)
Standard Retrieval-Augmented Generation (RAG) relies on chunk-based retrieval, whereas GraphRAG advances this approach by graph-based knowledge representation. However, existing graph-based RAG approaches are constrained by binary relations, as each edge in an ordinary graph connects only two entities, limiting their ability to represent the n-ary relations (n >= 2) in real-world knowledge. In this work, we propose HyperGraphRAG, a novel hypergraph-based RAG method that represents n-ary relational facts via hyperedges, and consists of knowledge hypergraph construction, retrieval, and generation. Experiments across medicine, agriculture, computer science, and law demonstrate that HyperGraphRAG outperforms both standard RAG and previous graph-based RAG methods in answer accuracy, retrieval efficiency, and generation quality. Our data and code are publicly available at https://github.com/LHRLAB/HyperGraphRAG.
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