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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 recentconsiderable research progress in embedding-based EA, how it works remains tobe explored. In this paper, we provide a similarity flooding perspective toexplain existing translation-based and aggregation-based EA models. We provethat the embedding learning process of these models actually seeks a fixpointof pairwise similarities between entities. We also provide experimentalevidence to support our theoretical analysis. We propose two simple buteffective methods inspired by the fixpoint computation in similarity flooding,and demonstrate their effectiveness on benchmark datasets. Our work bridges thegap between recent embedding-based models and the conventional similarityflooding algorithm. It would improve our understanding of and increase ourfaith in embedding-based EA.

What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings