融合主题模型的图卷积神经网络知识图谱实体对齐  

Knowledge Graph Entity Alignment via Graph Convolutional Neural Network with Topic Model

作　　者：李腾腾 杨光 LI Teng-Teng;YANG Guang(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京211106

出　　处：《计算机系统应用》2025年第4期207-217,共11页Computer Systems & Applications

摘　　要：实体对齐技术旨在识别并匹配不同知识图谱中指代同一实体的项,对于知识图谱的融合具有至关重要的作用,其在知识补全、社交网络分析等多个领域已经展现出广泛的应用潜力与显著的实用价值.随着基于知识表征学习的实体对齐方法的不断演进,研究者们开始探索利用实体之间的多种信息维度来计算相似度,从而评估源实体与目标实体之间的相似性.尽管如此,实体的部分属性信息在目前已有的方法中仍未得到充分利用,尤其是实体属性中的主题信息,通过主题模型能够识别出实体间更为显著的语义联系.针对这一研究,以实体属性的主题信息为核心,提出了一种实体对齐框架EAGT(knowledge graph entity alignment via graph convolutional network with biterm topic model),通过实体主题结合图卷积神经网络进行实体对齐.为了验证所提方法的有效性,在开源的数据集上进行了实验,结果表明,EAGT在大多数情况下均实现了性能提升.Entity alignment technology aims to identify and match items that refer to the same entity across different knowledge graphs.It plays a crucial role in the integration of knowledge graphs and demonstrates broad application potential and significant practical value in multiple fields such as knowledge completion and social network analysis.With the continuous evolution of entity alignment methods based on knowledge representation learning,researchers begin to explore the use of multiple information dimensions among entities to calculate similarity,thereby evaluating the similarity between source and target entities.Nonetheless,some of the attribute information of entities is not fully exploited in existing methods,especially the thematic information within entity attributes.By using topic models,more prominent semantic connections between entities can be identified.Focusing on this research,with the thematic information of entity attributes as the core,this study proposes an entity alignment framework called EAGT(knowledge graph entity alignment via graph convolutional networks with biterm topic model),which aligns entities by combining entity topics with graph convolutional neural networks.To verify the effectiveness of the proposed method,experiments are conducted on open-source datasets.The results show that EAGT achieves performance improvements in most cases.

关 键 词：知识图谱 实体对齐 主题模型 图卷积神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]