NeurIPS Conference 2025 Conference Paper
HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation
- Haoran Luo
- Haihong E
- Guanting Chen
- Yandan Zheng
- Xiaobao Wu
- Yikai Guo
- Qika Lin
- Yu Feng
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, the first hypergraph-based RAG method that represents n-ary relational facts via hyperedges. HyperGraphRAG consists of a comprehensive pipeline, including 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.