Abstract: A novel supervised method for knowledge graph entity alignment based on representation learning was proposed， which is different from the existing methods due to the similarity of structural information or attributive characters. First， the method automatically learns the semantic representations for the entities and relations of a knowledge graph in a low-dimensional vector space was proposed， and these embeddings contain the intrinsically structural information of a knowledge graph and the attributive features of entities. Afterwards， taking the manually aligned entity pairs as prior knowledge， the cross-KG mapping relationship between entities could be learned， which will be used for predicting entity alignment. Experiments conducted on real datasets demonstrated that our method can effectively improve the precision of knowledge graph entity alignment while keeping a high F1 score， when compared with the feature matching based method SiGMa.

Key words: machine learning, representation learning, knowledge graph, knowledge fusion, entity alignment