A Survey of Entity Alignment of Knowledge Graph Based on Embedded Representation

Abstract This paper summarizes the main methods of knowledge representation learning. Representation learning represents the entity information of the knowledge graph as a low dimensional vector. Its vector dimension is low, which helps to improve the computational efficiency and make full use of the semantic information between entities. In order to embed two KGs into a unified semantic space, joint embedding is used to achieve this goal. With the development of research, there are many improved embedding methods, such as iteration, multi view embedding, knowledge graph entity alignment based on graph neural network and so on.