mlsanityData to 14 Oct 2026

KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval

arXiv 2508.20417 ↗First posted 28 Aug 2025cs.CL cs.DBcited by 1

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

The integration of knowledge graphs (KGs) with large language models (LLMs) offers significant potential to improve the retrieval phase of retrieval-augmented generation (RAG) systems. In this study, we propose KG-CQR, a novel framework for Contextual Query Retrieval (CQR) that enhances the retrieval phase by enriching the contextual representation of complex input queries using a corpus-centric KG. Unlike existing methods that primarily address corpus-level context loss, KG-CQR focuses on query enrichment through structured relation representations, extracting and completing relevant KG subgraphs to generate semantically rich query contexts. Comprising subgraph extraction, completion, and contextual generation modules, KG-CQR operates as a model-agnostic pipeline, ensuring scalability across LLMs of varying sizes without additional training. 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. Furthermore, evaluations on challenging RAG tasks such as multi-hop question answering show that, by incorporating KG-CQR, the performance consistently outperforms the existing baseline in terms of retrieval effectiveness

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 35 of its 46 references that are in the map. First posted on arXiv 28 Aug 2025.

Builds on

35 of 46 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  2. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval31 Jan

Other references · 33

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  4. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  5. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 894
  6. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 668
  7. 2023C-Pack: Packed Resources For General Chinese Embeddingscited by 578
  8. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  9. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 476
  10. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 328
  11. 2023Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graphcited by 238
  12. 2025From RAG to Memory: Non-Parametric Continual Learning for Large Language Modelscited by 231
21 more
  1. 2022Unified Structure Generation for Universal Information Extractioncited by 221
  2. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 147
  3. 2023Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sourcescited by 122
  4. 2024RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systemscited by 117
  5. 2024HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extractioncited by 93
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  9. 2024Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answeringcited by 60
  10. 2022An Analysis of Fusion Functions for Hybrid Retrievalcited by 51
  11. 2022Generative Knowledge Graph Construction: A Reviewcited by 49
  12. 2024RaFe: Ranking Feedback Improves Query Rewriting for RAGcited by 49
  13. 2017Query expansion techniques for information retrieval: A surveycited by 44
  14. 2022Multi-View Document Representation Learning for Open-Domain Dense Retrievalcited by 41
  15. 2024Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphscited by 32
  16. 2024Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendationscited by 31
  17. 2024Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrievalcited by 26
  18. 2024Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern Retrievalcited by 16
  19. 2024Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understandingcited by 16
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  21. 2025ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation with Lightweight Specialized LLMcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generation
  2. 2024KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models
  3. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
  4. 2025A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
  5. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation
  6. 2025Query-Centric Graph Retrieval Augmented Generation
  7. 2026ContextRAG: Extraction-Free Hierarchical Graph Construction for Retrieval-Augmented Generation
  8. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation