Entity alignment (EA) refers to the task of linking entities in different knowledge graphs (KGs). Existing EA methods rely heavily on structural isomorphism. However, in real-world KGs, aligned entities usually have non-isomorphic neighborhood structures, which paralyses the application of these structure-dependent methods. In this paper, we investigate and tackle the problem of entity alignment between heterogeneous KGs. First, we propose two new benchmarks to closely simulate real-world EA scenarios of heterogeneity. Then we conduct extensive experiments to evaluate the performance of representative EA methods on the new benchmarks. Finally, we propose a simple and effective entity alignment framework called Attr-Int, in which innovative attribute information interaction methods can be seamlessly integrated with any embedding encoder for entity alignment, improving the performance of existing entity alignment techniques. Experiments demonstrate that our framework outperforms the state-of-the-art approaches on two new benchmarks.

翻译：实体对齐（EA）是指在不同知识图谱（KGs）中链接实体的任务。现有的EA方法高度依赖于结构同构性。然而，在现实世界的知识图谱中，对齐的实体通常具有非同构的邻域结构，这导致这些依赖结构的方法无法有效应用。本文研究并解决了异构知识图谱间的实体对齐问题。首先，我们提出了两个新的基准数据集，以紧密模拟现实世界中异构性实体对齐场景。随后，我们进行了大量实验，以评估代表性EA方法在新基准上的性能。最后，我们提出了一种简单高效的实体对齐框架Attr-Int，其中创新的属性信息交互方法能够与任何用于实体对齐的嵌入编码器无缝集成，从而提升现有实体对齐技术的性能。实验表明，我们的框架在两个新基准上优于现有最先进方法。