Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks (Extended Abstract)

Abstract

Knowledge graph (KG) entity alignment is the task of identifying corresponding entities across different KGs. Existing alignment techniques often require large amounts of labelled data, are unable to encode multi-modal data simultaneously, and enforce only a few consistency constraints. In this paper, we propose an end-to-end, unsupervised entity alignment framework for cross-lingual KGs using multi-order graph convolutional networks. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of accuracy, efficiency, and label saving.