Optimizing Hypergraph-Based RAG: Toward Better Fact Extraction and Chunk Retrieval

Abstract:GraphRAG enables deeper reasoning by structuring knowledge as graphs but struggles with n-ary facts. HyperGraphRAG uses hypergraphs for richer semantics, improving accuracy, yet relies on error-prone LLM extraction and inefficient standard chunk retrieval. We address this by employing self-consistency prompting to improve the extraction, and Personalized PageRank algorithm over hypergraph to enhance chunk retrieval.

Artificial Intelligence (cs.AI)

arXiv:2607.20506 [cs.AI]

(or arXiv:2607.20506v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.20506 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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