Entity alignment (EA) 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 EA 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 EA results. Given an EA 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 a variety of EA datasets demonstrate the effectiveness, generalization, and robustness of our framework in explaining and repairing embedding-based EA results.

翻译：实体对齐（EA）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任务。近期工作利用深度学习将实体嵌入向量空间，并通过最近邻搜索实现对齐。尽管基于嵌入的EA近年来取得了显著成功，但其对齐决策缺乏可解释性。本文提出首个能够生成解释以理解和修复基于嵌入的EA结果的框架。给定嵌入模型产生的EA对，我们首先比较其邻居实体与关系以构建匹配子图作为局部解释；随后构造对齐依赖图从抽象视角理解该EA对；最后通过解析依赖图中的三类对齐冲突实现对EA对的修复。在多种EA数据集上的实验表明，本框架在解释和修复基于嵌入的EA结果方面具有有效性、泛化性和鲁棒性。