Scholarlink

<p>Document retrieval refers to the identification of document contents that are semantically most relevant within a corpus. Existing matching algorithms may not recognize the synonymy of all documents for vocabulary, leading to retrieval inaccuracies. To address this limitation, this paper proposes Knowledge Based Document Retrieval (KBDR) that utilizes graph-based knowledge expansion. Firstly, named entity recognition and relation extraction are used to construct document graphs. Secondly, it expands entities using external knowledge bases such as HowNet and ConceptNet to enrich semantic connections. Thirdly, applies a Graph Attention Network (GAT) to model inter-entity dependencies for relevance ranking. Furthermore, we use five (i.e., CIPP, CNSE, CNSS, AAN, and OC) datasets to evaluate, KBDR outperforms baselines, achieving the highest <i>Recall</i>@1 of 25.88% on the CNSE. Overall, the KBDR can enhance efficiency and robustness in document retrieval, addressing conceptual deviations and advancing the field toward more complicated retrieval systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

KBDR: document retrieval based on graph matching with knowledge enhancement

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月27日

查看全文
  • Jingxuan Liu,
  • Yihan Huang,
  • Jialuoyi Tan,
  • Zhen Hua

摘要

Document retrieval refers to the identification of document contents that are semantically most relevant within a corpus. Existing matching algorithms may not recognize the synonymy of all documents for vocabulary, leading to retrieval inaccuracies. To address this limitation, this paper proposes Knowledge Based Document Retrieval (KBDR) that utilizes graph-based knowledge expansion. Firstly, named entity recognition and relation extraction are used to construct document graphs. Secondly, it expands entities using external knowledge bases such as HowNet and ConceptNet to enrich semantic connections. Thirdly, applies a Graph Attention Network (GAT) to model inter-entity dependencies for relevance ranking. Furthermore, we use five (i.e., CIPP, CNSE, CNSS, AAN, and OC) datasets to evaluate, KBDR outperforms baselines, achieving the highest Recall@1 of 25.88% on the CNSE. Overall, the KBDR can enhance efficiency and robustness in document retrieval, addressing conceptual deviations and advancing the field toward more complicated retrieval systems.