Cross-lingual entity alignment method based on attribute weight updating network

Abstract

Due to the large number of attributes and the low repetition rate in a cross-lingual knowledge graph, it is difficult for an alignment task to embed attribute information efficiently. To solve the problem, an entity alignment model based on attribute weight updating network was proposed. Firstly, in order to embed attribute information efficiently, attribute embedding is approximately constructed with entity embedding through a constructor, thus avoiding their separate training. Secondly, based on the fact that different attributes make different contributions to entity alignment, an attribute weight updating module based on graph attention network was proposed to update the weight of each attribute through using attention scores in the process of training. Finally, attribute embedding and attribute weight information were aggregated into entity embedding with an attribute aggregation module to strengthen the representation of entity embedding and improve the entity alignment performance. The experimental results show that the proposed model achieves 0.751, 0.805 and 0.915 scores respectively from the Hits@1 score in three cross-lingual datasets. Its alignment performance is better than that of the current mainstream entity alignment method.