Abstract: Entity alignment is a key step in knowledge fusion,which is used to solve the problem of entity redundancy and unknown reference in multi-source knowledge graph.At present,most of the entity alignment methods mainly rely on the neighborhood network,but ignore the connectivity and attribute information between the relationships.As a result,the model cannot capture the complex relationships,and the additional information is not fully utilized.To solve the above problems,an entity alignment method based on iterative graph reasoning and attribute semantic embedding is proposed.The 〈head,relation,tail〉 is transposed to generate 〈head,relation,tail〉 to construct the corresponding relationship graph with the entity graph,and then the attention mechanism is used to encode the entity and relation representation.The two can represent the entity better through iteration.The refusion property indicates the final determination of whether the two entities are aligned.Experimental results show that this model is significantly superior to the other six methods in the three cross-language data sets of DBP15K,and the index increases by 4% compared with the best method Hit@1,which proves the effectiveness of relational reasoning and attribute semantics.

Key words: Knowledge graph, Entity alignment, Graph convolutional neural network, Relationship matching, Attribute semantic