Entity Alignment with Global Information Aggregation

Entity alignment (EA) is a critical task in knowledge graph fusion, aiming to associate equivalent entities across disparate knowledge graphs (KGs). Current methods typically leverage entity representations derived from triples or neighboring entities, employing similarity matching for alignment. These approaches, however, tend to be overly reliant on proximal neighbor information and struggle with graph over-smoothing introduced by neighborhood aggregation. In response to these limitations, this study introduces a Global Information Aggregator (GIA), a new method that aims to enhance entity representation and simultaneously alleviate over-smoothing by merging the global structural information of the entire knowledge graph. Specifically, we propose a PageRank-based method to aggregate the global structural information of KGs. In addition, the GIA generates a diffusion-augmented graph by propagating and integrating the global information of the KGs. This graph is subsequently compared with a structural perturbation-augmented graph to yield more robust and comprehensive entity representations, thus further improving the model’s alignment performance. Extensive experiments on four benchmark datasets show that the GIA model is highly competitive with current state-of-the-art entity alignment frameworks.