Joint representation learning over multi-sourced knowledge graphs (KGs) yields transferable and expressive embeddings that improve downstream tasks. Entity alignment (EA) is a critical step in this process. Despite recent considerable research progress in embedding-based EA, how it works remains to be explored. In this paper, we provide a similarity flooding perspective to explain existing translation-based and aggregation-based EA models. We prove that the embedding learning process of these models actually seeks a fixpoint of pairwise similarities between entities. We also provide experimental evidence to support our theoretical analysis. We propose two simple but effective methods inspired by the fixpoint computation in similarity flooding, and demonstrate their effectiveness on benchmark datasets. Our work bridges the gap between recent embedding-based models and the conventional similarity flooding algorithm. It would improve our understanding of and increase our faith in embedding-based EA.

翻译：联合表示学习在多源知识图谱上可生成具有迁移性和表达力的嵌入，从而改进下游任务。实体对齐是该过程中的关键步骤。尽管近年来基于嵌入的实体对齐研究取得了显著进展，但其内在机制仍有待探索。本文从相似性泛化视角出发，对现有基于平移与基于聚合的实体对齐模型进行解释。我们证明这些模型的嵌入学习过程实质上是寻求实体间成对相似性的不动点，并通过实验证据支持理论分析。受相似性泛化中不动点计算的启发，我们提出两种简易有效的方法，并在基准数据集上验证其有效性。本研究弥合了近期基于嵌入的模型与传统相似性泛化算法之间的鸿沟，有助于加深我们对基于嵌入的实体对齐的理解与信任。