Entity alignment (EA) is the task to discover entities referring to the same real-world object from different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs. The majority of the existing embeddings-based entity alignment methods embed entities and relations into a vector space based on relation triples of KGs for local alignment. As these methods insufficiently consider the multiple relations between entities, the structure information of KGs has not been fully leveraged. In this paper, we propose a novel framework based on Relation-aware Graph Attention Networks to capture the interactions between entities and relations. Our framework adopts the self-attention mechanism to spread entity information to the relations and then aggregate relation information back to entities. Furthermore, we propose a global alignment algorithm to make one-to-one entity alignments with a fine-grained similarity matrix. Experiments on three real-world cross-lingual datasets show that our framework outperforms the state-of-the-art methods.
翻译:实体对齐(EA)是发现不同知识图表(KGs)中提及相同真实世界天体的实体的任务,这是整合多源KGs的最重要步骤。现有的基于嵌入实体的对齐方法,大多数基于嵌入实体的对齐方法将实体和关系嵌入以三重KGs关系为基础的矢量空间,用于本地对齐。由于这些方法没有充分考虑到实体之间的多重关系,KGs的结构信息尚未得到充分利用。在本文件中,我们提议了一个基于关系认知图形关注网络的新框架,以捕捉实体和关系之间的互动。我们的框架采用自我注意机制,将实体信息传播到关系中,然后将关联信息汇总到实体。此外,我们提议采用全球对齐算法,使一对一的实体与细微的类似矩阵保持一致。对三个真实世界跨语言数据集的实验表明,我们的框架超越了最新方法。