{"ID":2876997,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.20417","arxiv_id":"2508.20417","title":"KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval","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","short_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...","url_abs":"https://arxiv.org/abs/2508.20417","url_pdf":"https://arxiv.org/pdf/2508.20417v3","authors":"[\"Chi Minh Bui\",\"Ngoc Mai Thieu\",\"Van Vinh Nguyen\",\"Jason J. Jung\",\"Khac-Hoai Nam Bui\"]","published":"2025-08-28T04:37:15Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.DB\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false}