Aligning entity of knowledge graph based on graph convolutional network with maximum translated similarity

Entity alignment is a pivotal technology for integrating heterogeneous knowledge graphs, yet most embedding-based alignment methods destroy the inherent structural embedding information of knowledge graphs when unifying cross-graph embedding spaces, resulting in limited alignment accuracy and stability. To tackle this issue, this paper proposes a novel model for entity alignment tasks, which named as Graph Convolutional Network with Maximum Translated Similarity (GCN-MTS). GCN-MTS innovatively leverages a maximum translated similarity mechanism to optimize the adjacency matrix, degree matrix and loss function. This core design effectively maintains the original relative embedding structure of cross-graph entities during embedding space unification, which is the key improvement over existing approaches. Comprehensive experiments on 3 pairs of real-world knowledge graph dataset demonstrate the superior performance of GCN-MTS against advanced baseline methods. It achieves average performance gains of 5%-15% in Hits@1 and 4%-16% in Hits@10. Ablation results confirm that the optimized module brings a nearly 22% improvement in alignment metrics compared with the vanilla GCN. Furthermore, GCN-MTS presents excellent robustness and stability across different seed alignment quantities and seed combinations.