Cross-knowledge-graph entity alignment via relation prediction

Abstract

The entity alignment task aims to align entities corresponding to the same object in different KGs. The recent work focuses on applying knowledge embedding or graph neural networks to obtain entity embedding for entity alignment. However, there are two challenges encountered by these models: one is some models need to design hyper-parameter to balance embedding loss and alignment loss, the other is the limited training data size. In this paper, we propose a novel entity alignment framework named RpAlign (Relation prediction based cross-knowledge-graph entity Alignment) to address these two issues. Specifically, RpAlign transforms the entity alignment task to the KG completion task to solve and does not need to design any extra alignment component. Unlike the existing models that predict aligned entities by using entity vector distance, the RpAlign defines a new relation called ‘anchor’ for aligned entities, and it predicts new aligned entities based on the relational predictions between the entities. RpAlign employs several data augmentation and improved self-training techniques to mitigate the impact of the data limitation. We conduct experiments on two datasets, and the experimental results show that the RpAlign model significantly outperforms the current state-of-the-art models.