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

Houda Khrouf; Pedro Fillastre; Sebastiao Correia

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

Artificial Intelligence 2026-07-02 v1

Authors: Houda Khrouf , Pedro Fillastre , Sebastiao Correia

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.

Cite

@article{arxiv.2607.20506,
  title  = {Optimizing Hypergraph-Based RAG: Toward Better Fact Extraction and Chunk Retrieval},
  author = {Houda Khrouf and Pedro Fillastre and Sebastiao Correia},
  journal= {arXiv preprint arXiv:2607.20506},
  year   = {2026}
}

Comments

APIA 2026 conference