@inproceedings{xu-etal-2025-ji,
    title = "基于关系结构感知增强的知识图谱规则挖掘方法",
    author = "Xu, Huiqin  and
      Huang, Qi  and
      Zhang, Cheng  and
      Liu, Xiangqi  and
      Luo, Wenbing  and
      Wang, Mingwen",
    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-1.25/",
    pages = "323--334",
    abstract = "``知识图谱推理(KGR)旨在通过对知识图谱中蕴含的逻辑规则进行挖掘和应用,进而推断和发现新事实。该任务广泛应用于智能问答、语义搜索和推荐系统等领域。近年来,由于基于嵌入的知识图谱推理算法缺乏可解释性,一些研究者开始研究基于规则的知识图谱推理方法。然而,现有基于规则的推理方法在理解关系语义时难以处理关系之间的隐式关联信息且容易陷入局部最优解。为此,本文提出了一种基于关系结构感知增强的规则挖掘模型ReSA。该方法通过构建关系图,显式地建模关系之间的层次结构,提高规则挖掘的效率。同时,ReSA还通过全局规则融合模块和相对关系编码器,结合全局语义建模和局部结构建模,增强模型对规则体整体逻辑的感知能力。实验表明,ReSA模型在WN18RR等数据集上取得了显著的性能提升,MRR指标相较于现有最新规则挖掘方法提升了4个百分点。''"
}