Knowledge Graph Entity Alignment of Fusing TransD and Edge Embedding
Entity alignment is used to represent two entities of the same object in the real world in two different knowledge graphs.However,the entity alignment method based on the TransE model has defects in dealing with one-to-many,many-to-one,many-to-many complex relations,and has low accuracy in dealing with complex relations.The inability to accurately extrapolate entities with the same relationship ignores the diversity of entities.In order to solve the above problems,the TransD model is proposed to deal with one-to-one and many-to-one entity relationships,but the disadvantage is that the intrinsic correlation of relationships is ignored.The edge embedding model is further used to solve the relational triples of entity alignment,the TransDCP-Align model is used to embed the relational structure,and the vector representation of each entity is obtained by triplet embedding.And iteratively update each entity vector to indicate completion of entity alignment.Experiments on real world data sets show that the proposed TransDCP-Align model has better entity alignment performance than the traditional representation learning model.
万方数据
维普
