Knowledge Graph-Guided Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2502.06864v1
• Date: Sat, 08 Feb 2025 02:14:31 GMT
• Title: Knowledge Graph-Guided Retrieval Augmented Generation
• Authors: Xiangrong Zhu, Yuexiang Xie, Yi Liu, Yaliang Li, Wei Hu,
• Abstract summary: We propose a Knowledge Graph-Guided Retrieval Augmented Generation framework.<n> KG$2$RAG provides fact-level relationships between chunks, improving the diversity and coherence of the retrieved results.
• Score: 34.83235788116369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-augmented generation (RAG) has emerged as a promising technology for addressing hallucination issues in the responses generated by large language models (LLMs). Existing studies on RAG primarily focus on applying semantic-based approaches to retrieve isolated relevant chunks, which ignore their intrinsic relationships. In this paper, we propose a novel Knowledge Graph-Guided Retrieval Augmented Generation (KG$^2$RAG) framework that utilizes knowledge graphs (KGs) to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results. Specifically, after performing a semantic-based retrieval to provide seed chunks, KG$^2$RAG employs a KG-guided chunk expansion process and a KG-based chunk organization process to deliver relevant and important knowledge in well-organized paragraphs. Extensive experiments conducted on the HotpotQA dataset and its variants demonstrate the advantages of KG$^2$RAG compared to existing RAG-based approaches, in terms of both response quality and retrieval quality.

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