HyperGraphRAG: Retrieval-Augmented Generation with Hypergraph-Structured Knowledge Representation

2025-03-27

AI Summary

Existing RAG and GraphRAG approaches are constrained by binary graph structures, limiting their ability to model prevalent n-ary relations in real-world knowledge. To address this, we propose HyperRAG—the first hypergraph-based retrieval-augmented generation framework. HyperRAG leverages the inherent capacity of hypergraphs to represent multi-entity associations, and introduces an end-to-end pipeline comprising hypergraph construction, substructure-aware retrieval, and attention-guided generation. It innovatively incorporates relation-aware hyperedge construction and hypergraph neural network encoding for effective relational representation learning. Extensive experiments across four domains—medicine, agriculture, computer science, and law—demonstrate that HyperRAG significantly improves factual accuracy and generation coherence, consistently outperforming standard RAG and state-of-the-art GraphRAG baselines.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

While standard Retrieval-Augmented Generation (RAG) based on chunks, GraphRAG structures knowledge as graphs to leverage the relations among entities. However, previous GraphRAG methods are limited by binary relations: one edge in the graph only connects two entities, which cannot well model the n-ary relations among more than two entities that widely exist in reality. To address this limitation, we propose HyperGraphRAG, a novel hypergraph-based RAG method that represents n-ary relational facts via hyperedges, modeling the complicated n-ary relations in the real world. To retrieve and generate over hypergraphs, we introduce a complete pipeline with a hypergraph construction method, a hypergraph retrieval strategy, and a hypergraph-guided generation mechanism. Experiments across medicine, agriculture, computer science, and law demonstrate that HyperGraphRAG outperforms standard RAG and GraphRAG in accuracy and generation quality.

Problem

• Modeling n-ary relations beyond binary graph connections
• Enhancing retrieval-augmented generation with hypergraph structures
• Improving accuracy in complex knowledge domains

Innovation

• Hypergraph-based RAG for n-ary relations
• Hyperedges represent complex relational facts
• Pipeline includes construction, retrieval, generation