Paper · arXiv
Optimizing Hypergraph-Based RAG: Toward Better Fact Extraction and Chunk Retrieval
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.
| Authors | Houda Khrouf, Pedro Fillastre, Sebastiao Correia |
|---|---|
| Primary category | cs.AI |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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