Global Entity Alignment with Gated Latent Space Neighborhood Aggregation

Abstract

Existing entity alignment models mainly use the topology structure of the original knowledge graph and have achieved promising performance. However, they are still challenged by the heterogeneous topological neighborhood structures, which could cause the models to produce different representations of counterpart entities. In the paper, we propose a global entity alignment model with gated latent space neighborhood aggregation (LatsEA) to address this challenge. Latent space neighborhood is formed by calculating the similarity between the entity embeddings, it can introduce long-range neighbors to expand the topological neighborhood and reconcile the heterogeneous neighborhood structures. Meanwhile, it uses vanilla GCN to aggregate the topological neighborhood and latent space neighborhood respectively. Then, it uses an average gating mechanism to aggregate topological neighborhood information and latent space neighborhood information of the central entity. In order to further consider the interdependence between entity alignment decisions, we propose a global entity alignment strategy, i.e., formulate entity alignment as the maximum bipartite matching problem, which is effectively solved by Hungarian algorithm. Our experiments with ablation studies on three real-world entity alignment datasets prove the effectiveness of the proposed model. Latent space neighborhood information and global entity alignment decisions both contributes to the entity alignment performance improvement.