A comprehensive survey of entity alignment for knowledge graphs
Kaisheng Zeng, Chengjiang Li, Lei Hou, Juanzi Li, Ling Feng
Abstract
Knowledge Graphs (KGs), as a structured human knowledge, manage data in an ease-of-store, recognizable, and understandable way for machines and provide a rich knowledge base for different artificial intelligence applications. However, current multi-source KGs have heterogeneity and complementarity, and it is necessary to fuse heterogeneous knowledge from different data sources or different languages into a unified and consistent KG. Entity alignment aims to find equivalence relations between entities in different knowledge graphs but semantically represent the same real-world object, which is the most fundamental and essential technology in knowledge fusion. This paper investigated almost all the latest knowledge graph representations learning and entity alignment methods and summarized their core technologies and features from different aspects. Our full investigation gives a comprehensive outlook on several promising research directions for future work. We also provide an efficient and efficiency entity alignment toolkit to help researchers quickly start their own entity alignment models.