We study the problem of embedding-based entity alignment between knowledge
graphs (KGs). Previous works mainly focus on the relational structure of
entities. Some further incorporate another type of features, such as
attributes, for refinement. However, a vast of entity features are still
unexplored or not equally treated together, which impairs the accuracy and
robustness of embedding-based entity alignment. In this paper, we propose a
novel framework that unifies multiple views of entities to learn embeddings for
entity alignment. Specifically, we embed entities based on the views of entity
names, relations and attributes, with several combination strategies.
Furthermore, we design some cross-KG inference methods to enhance the alignment
between two KGs. Our experiments on real-world datasets show that the proposed
framework significantly outperforms the state-of-the-art embedding-based entity
alignment methods. The selected views, cross-KG inference and combination
strategies all contribute to the performance improvement.

本研究提出一种新颖的框架，通过利用实体的多个视角来学习实体嵌入，从而增强知识图谱之间的嵌入式实体对齐，并在真实数据集上验证实验证明，该框架显著优于现有的嵌入式实体对齐方法。