Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need?

• URL: http://arxiv.org/abs/2510.16582v1
• Date: Sat, 18 Oct 2025 17:06:49 GMT
• Title: Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need?
• Authors: Junchi Yu, Yujie Liu, Jindong Gu, Philip Torr, Dongzhan Zhou,
• Abstract summary: 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.
• Score: 57.28763506780752
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) based on knowledge graphs (KGs) enhances large language models (LLMs) by providing structured and interpretable external knowledge. However, existing KG-based RAG methods struggle to retrieve accurate and diverse information from text-rich KGs for complex real-world queries. Process Reward Models (PRMs) offer a way to align the retrieval process of KG-based RAG with query-specific knowledge requirements, but they heavily rely on process-level supervision signals that are expensive and hard to obtain on KGs. To address this challenge, we propose GraphFlow, a framework that efficiently retrieves accurate and diverse knowledge required for real-world queries from text-rich KGs. GraphFlow employs a transition-based flow matching objective to jointly optimize a retrieval policy and a flow estimator. The flow estimator factorizes the reward of the retrieval outcome into the intermediate retrieval states. Such reward factorization guides the retrieval policy to retrieve candidates from KGs in proportion to their reward. This allows GraphFlow to explore high-quality regions of KGs that yield diverse and relevant results. We evaluate GraphFlow on the STaRK benchmark, which includes real-world queries from multiple domains over text-rich KGs. GraphFlow outperforms strong KG-RAG baselines, including GPT-4o, by 10% on average in hit rate and recall. It also shows strong generalization to unseen KGs, demonstrating its effectiveness and robustness.

Related papers

• 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)
• BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering [38.3732958942896]
  BYOKG-RAG is a framework that enhances knowledge graph question answering.<n>It combines Large Language Model (LLM) agents with specialized graph retrieval tools.<n>By retrieving context from different graph tools, BYOKG-RAG offers a more general and robust solution for QA over custom KGs.
  arXiv  Detail & Related papers  (2025-07-05T18:47:14Z)
• KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs [58.12674907593879]
  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.
  arXiv  Detail & Related papers  (2025-06-11T09:20:02Z)
• Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness [26.18941757096083]
  Knowledge Graph based Retrieval-Augmented Generation (KG-RAG) is a technique that enhances Large Language Model (LLM) inference in tasks like Question Answering (QA)<n>Existing benchmarks do not adequately capture the impact of KG incompleteness on KG-RAG performance.<n>We demonstrate that KG-RAG methods are sensitive to KG incompleteness, highlighting the need for more robust approaches in realistic settings.
  arXiv  Detail & Related papers  (2025-04-07T15:08:03Z)
• Distill-SynthKG: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency [59.6772484292295]
  Knowledge graphs (KGs) generated by large language models (LLMs) are increasingly valuable for Retrieval-Augmented Generation (RAG) applications. Existing KG extraction methods rely on prompt-based approaches, which are inefficient for processing large-scale corpora. We propose SynthKG, a multi-step, document-level synthesis KG workflow based on LLMs. We also design a novel graph-based retrieval framework for RAG.
  arXiv  Detail & Related papers  (2024-10-22T00:47:54Z)
• 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)
• Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs [53.03085605769093]
  We propose to learn Federated Neural Graph DataBase (FedNGDB), a pioneering systematic framework that empowers privacy-preserving reasoning over multi-source graph data.<n>FedNGDB leverages federated learning to collaboratively learn graph representations across multiple sources, enriching relationships between entities, and improving the overall quality of graph data.
  arXiv  Detail & Related papers  (2024-02-22T14:57:44Z)
• DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN [59.160401038969795]
  We propose differentiable sampling on Knowledge Graph for Recommendation with GNN (DSKReG) We devise a differentiable sampling strategy, which enables the selection of relevant items to be jointly optimized with the model training procedure. The experimental results demonstrate that our model outperforms state-of-the-art KG-based recommender systems.
  arXiv  Detail & Related papers  (2021-08-26T16:19:59Z)

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.