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Knowledge Graph-Guided Retrieval Augmented Generation
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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.
Forward citations
Cited by 4 Pith papers
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KGRAG-Ex: Explainable Retrieval-Augmented Generation with Knowledge Graph-based Perturbations
KGRAG-Ex retrieves answer-relevant paths through a knowledge graph, turns them into natural-language paragraphs, and explains each answer by removing individual graph nodes, edges, or sub-paths and observing whether t...
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SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs
SCAIR, a training-free schema-conditioned agentic KG-RAG method, substantially outperforms existing KG-RAG approaches on a new enterprise CMDB benchmark, but the evaluation has notable confounds.
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A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
QMKGF builds multi-path knowledge graph subgraphs from LLM-extracted entities, fuses the highest-scoring subgraph with query-relevant triples, and expands the query to improve RAG answer quality.
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