论文标题
关系反思实体对齐
Relational Reflection Entity Alignment
论文作者
论文摘要
实体对齐旨在从不同的知识图（kgs）中识别等效实体对，这对于整合多源kgs至关重要。最近，随着GNNS将GNN引入实体一致性，最近模型的架构变得越来越复杂。我们甚至在这些方法中发现了两个违反直觉现象：（1）GNN中的标准线性转换效果不佳。 （2）许多用于链接预测任务的高级KG嵌入模型在实体对齐中的表现不佳。在本文中，我们将现有实体对准方法抽到统一的框架，形状构建器和对齐方式，不仅成功地解释了上述现象，而且还为理想转换操作提供了两个关键标准。此外，我们提出了一种基于GNN的新方法，关系反射实体对准（RREA）。 RREA利用关系反思转换以更有效的方式为每个实体获得关系特定的嵌入。现实世界数据集的实验结果表明，我们的模型大大胜过最新方法，命中@1超过5.8％-10.9％。
Entity alignment aims to identify equivalent entity pairs from different Knowledge Graphs (KGs), which is essential in integrating multi-source KGs. Recently, with the introduction of GNNs into entity alignment, the architectures of recent models have become more and more complicated. We even find two counter-intuitive phenomena within these methods: (1) The standard linear transformation in GNNs is not working well. (2) Many advanced KG embedding models designed for link prediction task perform poorly in entity alignment. In this paper, we abstract existing entity alignment methods into a unified framework, Shape-Builder & Alignment, which not only successfully explains the above phenomena but also derives two key criteria for an ideal transformation operation. Furthermore, we propose a novel GNNs-based method, Relational Reflection Entity Alignment (RREA). RREA leverages Relational Reflection Transformation to obtain relation specific embeddings for each entity in a more efficient way. The experimental results on real-world datasets show that our model significantly outperforms the state-of-the-art methods, exceeding by 5.8%-10.9% on Hits@1.
