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

• URL: http://arxiv.org/abs/2506.09542v2
• Date: Sat, 18 Oct 2025 13:51:56 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 incorporates pre-existing large-scale knowledge graphs into RAG.<n> KG-Infused RAG directly performs spreading activation over external KGs to retrieve relevant structured knowledge.<n>Experiments show that KG-Infused RAG consistently outperforms vanilla RAG.
• Score: 58.12674907593879
• 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 RAG methods either rely solely on text corpora and neglect structural knowledge, or build ad-hoc knowledge graphs (KGs) at high cost and low reliability. To address these issues, we propose KG-Infused RAG, a framework that incorporates pre-existing large-scale KGs into RAG and applies spreading activation to enhance both retrieval and generation. KG-Infused RAG directly performs spreading activation over external KGs to retrieve relevant structured knowledge, which is then used to expand queries and integrated with corpus passages, enabling interpretable and semantically grounded multi-source retrieval. We further improve KG-Infused RAG through preference learning on sampled key stages of the pipeline. Experiments on five QA benchmarks show that KG-Infused RAG consistently outperforms vanilla RAG (by 3.9% to 17.8%). Compared with KG-based approaches such as GraphRAG and LightRAG, our method obtains structured knowledge at lower cost while achieving superior performance. Additionally, integrating KG-Infused RAG with Self-RAG and DeepNote yields further gains, demonstrating its effectiveness and versatility as a plug-and-play enhancement module for corpus-based RAG methods.

Related papers

• Multi-hop Reasoning via Early Knowledge Alignment [68.28168992785896]
  Early Knowledge Alignment (EKA) aims to align Large Language Models with contextually relevant retrieved knowledge.<n>EKA significantly improves retrieval precision, reduces cascading errors, and enhances both performance and efficiency.<n>EKA proves effective as a versatile, training-free inference strategy that scales seamlessly to large models.
  arXiv  Detail & Related papers  (2025-12-23T08:14:44Z)
• Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need? [57.28763506780752]
  GraphFlow is a framework that efficiently retrieves accurate and diverse knowledge required for real-world queries from text-rich KGs.<n>It outperforms strong KG-RAG baselines, including GPT-4o, by 10% on average in hit rate and recall.<n>It also shows strong generalization to unseen KGs, demonstrating its effectiveness and robustness.
  arXiv  Detail & Related papers  (2025-10-18T17:06:49Z)
• Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning [18.9814789695716]
  Knowledge-graph retrieval-augmented generation (KG-RAG) couples large language models (LLMs) with structured, verifiable knowledge graphs (KGs) to reduce hallucinations and expose reasoning traces.<n>We introduce KG-R1, an agentic KG retrieval-augmented generation (KG-RAG) framework through reinforcement learning (RL)<n> KG-R1 utilizes a single agent that interacts with KGs as its environment, learning to retrieve at each step and incorporating the retrieved information into its reasoning and generation.
  arXiv  Detail & Related papers  (2025-09-30T15:14:24Z)
• Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching [61.824094419641575]
  Large Language Models (LLMs) struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA)<n>We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures.<n>Existing methods usually employ resource-intensive, non-scalable reasoning on vanilla KGs, but overlook this gap.<n>We propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs' prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries.
  arXiv  Detail & Related papers  (2025-09-25T06:48:52Z)
• KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval [5.263064605350636]
  We propose KG-CQR, a novel framework for Contextual Query Retrieval (CQR)<n> KG-CQR focuses on query enrichment through structured relation representations, extracting and completing relevant KG subgraphs to generate semantically rich query contexts.<n> Experimental results on RAGBench and MultiHop-RAG datasets demonstrate KG-CQR's superior performance, achieving a 4-6% improvement in mAP and a 2-3% improvement in Recall@25 over strong baseline models.
  arXiv  Detail & Related papers  (2025-08-28T04:37:15Z)
• Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks [2.717314422130497]
  Large Language Models (LLMs) have showcased impressive reasoning abilities, but often suffer from hallucinations or outdated knowledge.<n> Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) remedies these shortcomings by grounding responses in structured external information from a knowledge base.<n>However, many KG-based RAG approaches struggle with (i) aligning KG and textual representations, (ii) balancing retrieval accuracy and efficiency, and (iii) adapting to dynamically updated KGs.
  arXiv  Detail & Related papers  (2025-05-22T16:11:35Z)
• Know3-RAG: A Knowledge-aware RAG Framework with Adaptive Retrieval, Generation, and Filtering [9.814926166669366]
  We propose Know3-RAG, a knowledge-aware RAG framework that leverages structured knowledge from knowledge graphs (KGs) to guide three core stages of the RAG process, including retrieval, generation, and filtering.<n> Experiments on multiple open-domain QA benchmarks demonstrate that Know3-RAG consistently outperforms strong baselines, significantly reducing hallucinations and enhancing answer reliability.
  arXiv  Detail & Related papers  (2025-05-19T03:25:18Z)
• RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration [4.604003661048267]
  RAG-KG-IL is a novel multi-agent hybrid framework designed to enhance the reasoning capabilities of Large Language Models.<n>It integrates Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs) with an Incremental Learning (IL) approach.<n>We evaluate the framework using real-world case studies involving health-related queries.
  arXiv  Detail & Related papers  (2025-03-14T11:50:16Z)
• Knowledge Graph-Guided Retrieval Augmented Generation [34.83235788116369]
  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.
  arXiv  Detail & Related papers  (2025-02-08T02:14:31Z)
• FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs [17.477161619378332]
  We propose a novel flexible modular KG-RAG framework, termed FRAG, which synergizes the advantages of both approaches.<n>By using the query text instead of the Knowledge Graph, FRAG improves retrieval quality while maintaining flexibility.
  arXiv  Detail & Related papers  (2025-01-17T05:19:14Z)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively.<n>We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains [66.55612528039894]
  Knowledge Graphs (KGs) can serve as reliable knowledge sources for question answering (QA) We present DoG (Decoding on Graphs), a novel framework that facilitates a deep synergy between LLMs and KGs. Experiments across various KGQA tasks with different background KGs demonstrate that DoG achieves superior and robust performance.
  arXiv  Detail & Related papers  (2024-10-24T04:01:40Z)
• RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation [61.14660526363607]
  We propose a fine-grained evaluation framework, RAGChecker, that incorporates a suite of diagnostic metrics for both the retrieval and generation modules. RAGChecker has significantly better correlations with human judgments than other evaluation metrics. The metrics of RAGChecker can guide researchers and practitioners in developing more effective RAG systems.
  arXiv  Detail & Related papers  (2024-08-15T10:20:54Z)
• Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering [87.67177556994525]
  We propose a training-free method called Generate-on-Graph (GoG) to generate new factual triples while exploring Knowledge Graphs (KGs) GoG performs reasoning through a Thinking-Searching-Generating framework, which treats LLM as both Agent and KG in IKGQA.
  arXiv  Detail & Related papers  (2024-04-23T04:47:22Z)
• ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents [49.30553350788524]
  Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to leverage external knowledge. Existing RAG models often treat LLMs as passive recipients of information. We introduce ActiveRAG, a multi-agent framework that mimics human learning behavior.
  arXiv  Detail & Related papers  (2024-02-21T06:04:53Z)
• Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus [59.20235923987045]
  It is challenging to enrich a Knowledge Graph with newly harvested triples while maintaining the quality of the knowledge representation. This paper proposes a system to refine a KG using information harvested from an additional corpus.
  arXiv  Detail & Related papers  (2022-06-15T12:16:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.