KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908521913647104 |
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| author | Bui, Chi Minh Thieu, Ngoc Mai Nguyen, Van Vinh Jung, Jason J. Bui, Khac-Hoai Nam |
| author_facet | Bui, Chi Minh Thieu, Ngoc Mai Nguyen, Van Vinh Jung, Jason J. Bui, Khac-Hoai Nam |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20417 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval Bui, Chi Minh Thieu, Ngoc Mai Nguyen, Van Vinh Jung, Jason J. Bui, Khac-Hoai Nam Computation and Language Databases 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 |
| title | KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval |
| topic | Computation and Language Databases |
| url | https://arxiv.org/abs/2508.20417 |