Résumé: Cross-language entity alignment forms an important component of building a Knowledge Graph. The task of cross-lingual entity alignment is to match entities in a source language with their counterparts in target languages. In practice, there is an imbalance of attribute information in corresponding entities at the same level, and the problem of neighboring point weight assignment is not considered, which not only loses the association information between entities but also limits the utilization of entity attributes in the alignment process, making this task challenging. In this paper, we propose a cross-lingual entity alignment method based on Graph convolutinal neural network and Graph attention network. Specifically, it can capture more spatial information by assigning respective weights to the neighbors of different nodes through multi-level learning of entity structure, attributes, and attention. In addition, the weights of neighboring node features depend entirely on the node features, which gets rid of the dependence on the graph. The experiments show that our models outperform state-of-the-art methods at a fraction of the cost.

Langue: Anglais