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

• URL: http://arxiv.org/abs/2508.20417v3
• Date: Sat, 06 Sep 2025 04:31:35 GMT
• Title: KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval
• Authors: Chi Minh Bui, Ngoc Mai Thieu, Van Vinh Nguyen, Jason J. Jung, Khac-Hoai Nam Bui,
• Abstract summary: 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.
• Score: 5.263064605350636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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