基于动态邻居选择的知识图谱事实错误检测方法  

Factual error detection in knowledge graphs based on dynamic neighbor selection

作　　者：桂梁 徐遥 何世柱 张元哲[1,2] 刘康 赵军[1,2] GUI Liang;XU Yao;HE Shizhu;ZHANG Yuanzhe;LIU Kang;ZHAO Jun(The Laboratory of Cognition and Decision Intelligence for Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院自动化研究所复杂系统认知与决策实验室,北京100190 [2]中国科学院大学人工智能学院,北京100049

出　　处：《山东大学学报（理学版）》2024年第7期76-84,共9页Journal of Shandong University(Natural Science)

基　　金：国家重点研发计划项目(2022YFF0711900);国家自然科学基金资助项目(62376270,62276264)。

摘　　要：由于知识图谱(knowledge graph,KG)的构建和更新通常依赖大量网络数据和自动化方法,因此其中建模和获取的知识内容难免存在各种事实错误。为了解决这个问题,提出一种新知识图谱事实错误检测方法。该方法动态选择待检测事实的邻居节点,通过捕捉头尾实体之间的复杂关系来判断事实是否存在错误。首先利用图结构信息确定每个实体的潜在邻居;然后根据实体的上下文信息动态地选择相关邻居,进而使用高效的图注意力网络编码节点的特性;最终通过计算节点的头尾实体表示的一致性,判断待检测事实是否存在错误,并在多个公开的知识图谱数据集上进行实验。结果表明,该方法在错误检测方面表现优于现有的方法。The construction and updating of the knowledge graph(KG)usually depend on a wide range of web data and automated methods,inevitably resulting in factual inaccuracies in the modeled and acquired knowledge.To tackle this problem,a novelapproach for identifying factual inaccuracies within the knowledge graph is proposed.This method actively selects adjacent nodes of the facts to be checked,detecting errors by measuring the intricate associations linking the head and tail entities.More specifically,it first utilizes graph structure information to identify potential neighbors for each entity.Then,based on contextual information,it dynamically selects relevant neighbors and uses an efficient graph attention network to encode node features.Finally,by calculating the consistency of head and tail entity representations,it determines if the fact under consideration is erroneous.Experimental results on multiple public KG datasets demonstrate that this method outperforms existing approaches in error detection.

关 键 词：知识图谱 事实错误检测 知识图谱嵌入 质量控制 动态邻居选择 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]