KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs

• URL: http://arxiv.org/abs/2506.09542v1
• Date: Wed, 11 Jun 2025 09:20:02 GMT
• Title: KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs
• Authors: Dingjun Wu, Yukun Yan, Zhenghao Liu, Zhiyuan Liu, Maosong Sun,
• Abstract summary: KG-Infused RAG is a framework that integrates KGs into RAG systems to implement spreading activation.<n> KG-Infused RAG retrieves KG facts, expands the query accordingly, and enhances generation by combining corpus passages with structured facts.
• Score: 66.35046942874737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) improves factual accuracy by grounding responses in external knowledge. However, existing methods typically rely on a single source, either unstructured text or structured knowledge. Moreover, they lack cognitively inspired mechanisms for activating relevant knowledge. To address these issues, we propose KG-Infused RAG, a framework that integrates KGs into RAG systems to implement spreading activation, a cognitive process that enables concept association and inference. KG-Infused RAG retrieves KG facts, expands the query accordingly, and enhances generation by combining corpus passages with structured facts, enabling interpretable, multi-source retrieval grounded in semantic structure. We further improve KG-Infused RAG via preference learning on sampled key stages in the pipeline. Experiments on five QA benchmarks show that KG-Infused RAG consistently outperforms vanilla RAG (by 3.8% to 13.8%). Additionally, when integrated into Self-RAG, KG-Infused RAG brings further performance gains, demonstrating its effectiveness and versatility as a plug-and-play enhancement module for corpus-based RAG methods.

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