Entity alignment, as an important research direction in knowledge graph research, aims to connect different entities pointing to the same real-world object in different knowledge graphs, and thus to achieve the expansion of knowledge graphs. At present, there are two mainstream research approaches in this field. One is to analyze the structural characteristics of knowledge graphs, and the other is to analyze the element information (such as entity name, relation name, attribute name) of knowledge graphs. In this article, a novel entity alignment model EAFF (Entity Alignment based on Feature Fusion) is proposed to analyze the features of knowledge graphs from the perspectives of graph structure and element information. First, a graph neural networkbased entity alignment algorithm was designed to obtain aligned entity pairs based on graph structures. Then, an entity alignment algorithm based on element information was designed to obtain aligned entity pairs based on element information. Finally, using feature transformation and sorting algorithms, two sets of aligned entity pairs are sorted to obtain aligned entities in the knowledge graph. In the experiment, EAFF achieved relatively good results, surpassing current mainstream algorithms.

knowledge graph  /  entity alignment  /  graph neural network  /  feature fusion