超越实体对齐：通过实体-关系协同实现完整的知识图谱对齐
Beyond Entity Alignment: Towards Complete Knowledge Graph Alignment via Entity-Relation Synergy
AI总结：
针对现有知识图谱对齐忽略关系对齐的问题，提出基于期望最大化算法的模型EREM，将实体对齐与关系对齐解耦并迭代优化，在两项任务上均优于现有模型。
摘要：
知识图谱对齐（KGA）旨在整合来自多个来源的知识，以解决单个知识图谱（KGs）在覆盖率和深度方面的局限性。然而，当前的KGA模型在实现“完整”的知识图谱对齐方面存在不足。现有模型主要强调跨图实体的链接，但忽略了对齐跨KGs的关系，因此仅为KGA提供了部分解决方案。关系中嵌入的语义相关性在很大程度上被忽略，这可能限制了对跨KG信号的全面理解。在本文中，我们提出将关系对齐概念化为一个独立的任务，并通过将其分解为两个不同但高度相关的子任务：实体对齐和关系对齐来执行KGA。为了捕捉这些目标之间相互增强的关联性，我们提出了一种新颖的基于期望最大化的模型EREM，该模型迭代地优化这两个子任务。在真实世界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始终优于最先进的模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
