Entity alignment is an effective approach for multi-source database fusion with the aim of identifying co-referring entities in multi-source knowledge graphs.Recently,Graph Convolutional Network(GCN)have emerged as a new paradigm for entity alignment representation learning.However,there are significant differences in the objectives and rules for constructing knowledge graphs in different organizations,which require entity alignment models to accurately explore the long-tail entity features among knowledge graphs.Moreover,existing GCN entity alignment models focus overly on the structural representation of relationship triplets and neglect the rich semantic information of the attribute triplets.Accordingly,an entity alignment model is proposed that introduces a dynamic graph attention network to aggregate the attribute structure triplet representations and reduce the impact of irrelevant attribute structures on the entity representations.Simultaneously,to alleviate the problem of heterogeneous relationships in knowledge graphs,multi-dimensional label propagation is introduced to compress the different dimensions of the entity adjacency matrix.The entity features are propagated along the compressed knowledge graph adjacency relationship to obtain a relationship structure representation.Finally,a linear programming algorithm is used to iterate the entity representation similarity matrix to obtain the final alignment result.Experiments are conducted on publicly available datasets EN-FR-15K,EN-ZH-15K,and the Chinese medical dataset MED-BBK-9K,and the results demonstrate that the Hits@1 of the model are 0.942,0.926,and 0.427,the Hits@10 are 0.963,0.952,and 0.604,and the Mean Reciprocal Rank(MRR)values are 0.949,0.939,and 0.551,respectively.The ablation experimental results verify the effectiveness of each module in the model.
维普
万方数据
