提出Attr-Int框架，用属性交互提升异构知识图谱对齐效果
Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphs
- 通过属性信息交互增强嵌入编码器的对齐能力
- 在两个新基准上超越现有最优方法
- 适合处理结构不一致的异构知识图谱对齐任务
实体对齐（EA）旨在链接不同知识图谱中的实体。现有方法高度依赖结构同构性，但在真实场景中，对齐实体的邻域结构常不一致，导致依赖结构的方法失效。本文研究异构知识图谱的实体对齐问题，首先构建两个新基准以更贴近真实场景；其次在新基准上评估代表性方法性能；最后提出简单有效的对齐框架Attr-Int，其创新的属性信息交互机制可无缝集成于任意嵌入编码器，显著提升现有技术表现。实验表明，该框架在两个新基准上均优于当前最优方法。
原文摘要 · Abstract (English)
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.
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