Runjie Zhu


A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment
Zhiwen Xie | Runjie Zhu | Kunsong Zhao | Jin Liu | Guangyou Zhou | Jimmy Xiangji Huang
Proceedings of the 28th International Conference on Computational Linguistics

Cross-lingual entity alignment, which aims to match equivalent entities in KGs with different languages, has attracted considerable focus in recent years. Recently, many graph neural network (GNN) based methods are proposed for entity alignment and obtain promising results. However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned links between two KGs. In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. We conduct extensive experiments on three benchmark cross-lingual entity alignment datasets. The experimental results demonstrate that our proposed method obtains remarkable performance gains compared to state-of-the-art methods.