Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects. Recent embedding-based EA methods have achieved state-of-the-art performance in EA yet faced interpretability challenges as they purely rely on the embedding distance and neglect the logic rules behind a pair of aligned entities. In this paper, we propose the Align-Subgraph Entity Alignment (ASGEA) framework to exploit logic rules from Align-Subgraphs. ASGEA uses anchor links as bridges to construct Align-Subgraphs and spreads along the paths across KGs, which distinguishes it from the embedding-based methods. Furthermore, we design an interpretable Path-based Graph Neural Network, ASGNN, to effectively identify and integrate the logic rules across KGs. We also introduce a node-level multi-modal attention mechanism coupled with multi-modal enriched anchors to augment the Align-Subgraph. Our experimental results demonstrate the superior performance of ASGEA over the existing embedding-based methods in both EA and Multi-Modal EA (MMEA) tasks.

翻译：实体对齐旨在识别不同知识图谱中表示同一真实世界对象的实体。近年来基于嵌入的实体对齐方法虽取得最优性能，但因纯粹依赖嵌入距离而忽视对齐实体对背后的逻辑规则，面临可解释性挑战。本文提出对齐子图实体对齐框架，通过从对齐子图中挖掘逻辑规则实现实体对齐。ASGEA以锚点链接为桥梁构建对齐子图，并沿跨知识图谱路径进行传播，这与基于嵌入的方法存在本质区别。我们进一步设计了可解释的基于路径的图神经网络ASGNN，以有效识别并整合跨知识图谱的逻辑规则。同时引入结合多模态增强锚点的节点级多模态注意力机制，用于增强对齐子图。实验结果表明，ASGEA在实体对齐与多模态实体对齐任务上均显著优于现有基于嵌入的方法。