Review of Deep Learning-Based Entity Alignment Methods

Entity alignment aims to discover different references to the same entity in different graphs, and it is a key technique for solving graph-related problems. It has developed into one of the important tasks in knowledge graphs and has received extensive attention from scholars in recent years. Through entity alignment, data from multiple isolated knowledge graphs with different sources and modes can be summarized and classified, forming a more information-rich knowledge base. In early research on entity alignment, researchers first proposed a class of alignment methods based on knowledge representation learning and verified that these methods have significant improvements over traditional methods. However, entity alignment still has many defects and challenges to be addressed, such as a lack of scalability, differences in language and relationship type definitions in knowledge graphs from different sources, which make entity alignment difficult. There are also problems such as the need to improve the quality of large-scale knowledge graph data and optimize computational efficiency. Thus, it is difficult to perform entity alignment on multiple knowledge graphs through simple translation and transformation. This paper discusses the deep learning-based methods that have emerged in the field of entity alignment based on the definition of entity alignment and data sets as standards, summarizes the shortcomings and limitations of these methods, and introduces commonly used data sets in the entity alignment task.