以表窥里:聚焦表层信息的通用实体对齐方法  

Surface to Deeper:Universal Entity Alignment Approach Focusing on Surface Information

作　　者：郑百川 陈凯[1] 李升辉 李冰倩 张宁[1] ZHENG Bai-Chuan;CHEN Kai;LI Sheng-Hui;LI Bing-Qian;ZHANG Ning(School of Cyber Science and Engineering,Huazhong University of Science and Technology,Wuhan 430062,China)

机构地区：[1]华中科技大学网络空间安全学院,武汉430062

出　　处：《计算机系统应用》2025年第4期286-297,共12页Computer Systems & Applications

摘　　要：在知识图谱的整合过程中,实体对齐(EA)任务至关重要.最先进的研究引入了外部知识(属性文本、时间戳、图像信息等)以及多模态方法,取得了较高的精度,但这些方法往往对特定结构有较强的依赖性,这限制了它们在不同结构知识图谱实体对齐任务中的适用性.为了解决这一问题,本文提出了一种通用的知识图谱实体对齐方法,该方法利用知识图谱共有的实体、关系与图结构等信息工作,上述部分在知识图谱中可被直接观察到,因此统称为表层信息.本文方法包含嵌入生成模块和对齐模块,其中嵌入模块使用Transformer模型捕捉实体的固有语义及其邻居的贡献,对齐模块则通过匹配算法实现高性能且稳定的对齐.实验结果表明,我们的方法在多个主流知识图谱间的对齐场景中实现了最先进的性能,展现出稳定和可解释性强的特点.我们的代码可在https://github.com/zb1tree/TGEA获取.Entity alignment(EA)tasks are pivotal in the integration of knowledge graphs.The most advanced research has introduced external knowledge(attribute texts,timestamps,image information,etc.)and multimodal methods,achieving relatively high accuracy.However,these methods often have a strong dependence on specific structures,which limits their applicability in the entity alignment tasks of knowledge graphs with different structures.Therefore,this study proposes a universal knowledge graph alignment approach that utilizes the information of shared entity,relationship,and graph structure of knowledge graphs which are called surface information as they can be directly observed in knowledge graphs.An embedding generation module and an alignment module are included in the proposed method,and the former uses the Transformer model to capture the inherent semantics of entities and the contributions of their neighbors while the latter achieves high-performance and stable alignment through a matching algorithm.Experiment results show that the proposed method has achieved the best performance in the alignment scenarios among multiple mainstream knowledge graphs,demonstrating stability and strong interpretability.The code used in this study can be obtained at https://github.com/zb1tree/TGEA.

关 键 词：知识图谱 实体对齐 TRANSFORMER 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]