Knowledge Graph Alignment (KGA) aims to integrate knowledge from multiple sources to address the limitations of individual Knowledge Graphs (KGs) in terms of coverage and depth. However, current KGA models fall short in achieving a ``complete'' knowledge graph alignment. Existing models primarily emphasize the linkage of cross-graph entities but overlook aligning relations across KGs, thereby providing only a partial solution to KGA. The semantic correlations embedded in relations are largely overlooked, potentially restricting a comprehensive understanding of cross-KG signals. In this paper, we propose to conceptualize relation alignment as an independent task and conduct KGA by decomposing it into two distinct but highly correlated sub-tasks: entity alignment and relation alignment. To capture the mutually reinforcing correlations between these objectives, we propose a novel Expectation-Maximization-based model, EREM, which iteratively optimizes both sub-tasks. Experimental results on real-world datasets demonstrate that EREM consistently outperforms state-of-the-art models in both entity alignment and relation alignment tasks.

翻译：知识图谱对齐旨在整合多源知识，以解决单一知识图谱在覆盖范围和深度上的局限性。然而，当前的知识图谱对齐模型未能实现“完整”的知识图谱对齐。现有模型主要强调跨图谱实体的链接，却忽视了跨图谱关系的对齐，因此仅提供了知识图谱对齐的部分解决方案。关系中所蕴含的语义关联在很大程度上被忽略，这可能限制对跨图谱信号的全面理解。本文提出将关系对齐概念化为一项独立任务，并通过将知识图谱对齐分解为两个不同但高度相关的子任务来进行：实体对齐与关系对齐。为了捕捉这两个目标之间相互增强的关联，我们提出了一种基于期望最大化的新型模型EREM，该模型迭代地优化这两个子任务。在真实数据集上的实验结果表明，EREM在实体对齐和关系对齐任务中均持续优于现有最先进模型。