Entity alignment seeks identical entities in different knowledge graphs, which is a long-standing task in the database research. Recent work leverages deep learning to embed entities in vector space and align them via nearest neighbor search. Although embedding-based entity alignment has gained marked success in recent years, it lacks explanations for alignment decisions. In this paper, we present the first framework that can generate explanations for understanding and repairing embedding-based entity alignment results. Given an entity alignment pair produced by an embedding model, we first compare its neighbor entities and relations to build a matching subgraph as a local explanation. We then construct an alignment dependency graph to understand the pair from an abstract perspective. Finally, we repair the pair by resolving three types of alignment conflicts based on dependency graphs. Experiments on five datasets demonstrate the effectiveness and generalization of our framework in explaining and repairing embedding-based entity alignment results.

翻译：实体对齐旨在识别不同知识图谱中的相同实体，这是数据库研究中一项长期存在的任务。近期工作利用深度学习将实体嵌入到向量空间，并通过最近邻搜索进行对齐。尽管基于嵌入的实体对齐近年来取得了显著成功，但其对齐决策缺乏可解释性。本文提出了首个能够生成解释以理解与修复基于嵌入的实体对齐结果的框架。给定嵌入模型生成的实体对齐对，我们首先比较其邻居实体与关系，构建匹配子图作为局部解释；接着构造对齐依赖图，从抽象视角理解该对齐对；最后基于依赖图解决三类对齐冲突以修复该对齐对。在五个数据集上的实验表明，本框架在解释与修复基于嵌入的实体对齐结果方面具有有效性与泛化能力。