@inproceedings{zhang-etal-2025-da,
    title = "大语言模型和知识图谱协同的查询扩展方法",
    author = "Zhang, Kuang  and
      Tu, Xinhui  and
      Liuhan, Liuhan",
    editor = "Sun, Maosong  and
      Duan, Peiyong  and
      Liu, Zhiyuan  and
      Xu, Ruifeng  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-1.31/",
    pages = "422--433",
    abstract = "``查询扩展旨在通过丰富查询来提升检索效果。在大语言模型结合伪相关反馈的查询扩展方法中,伪相关文档中的噪声及不连贯信息严重影响了大语言模型的扩展质量。为此,本文提出一种大语言模型和知识图谱协同的查询扩展方法(LKQE)。LKQE 首先检索出相关文档并提取关键句,然后利用大语言模型从中抽取知识三元组,并补全实体关系构建出知识图谱,最终在知识图谱指导下生成高质量扩展文本。实验结果表明,与基线模型相比,LKQE 在 DL19 与 DL20 数据集上的表现具有显著优势。''"
}