A Survey on Translating Embedding based Entity Alignment in Knowledge Graphs
Jin Ping Jiang, Mohan Li, Zhaoquan Gu · 2021
Knowledge Graph (KG) as an ideal knowledge base can effectively support the mining, analysis and reasoning of complex relational data. It has been widely used by academia and industry. Entity alignment (EA) is one of the basic tasks of KG fusion. Its main goal is to align heterogeneous entities that refer to the same but from different sources. In recent years, a lot of researches have focused on this task. This paper presents a systematic survey of the KG EA based translating embeddings. The purpose is to provide a complete and systematic overview of these methods and challenges. Furthermore, we discuss the future research trends and the correlation with MDATA. Our detailed review can offer technical assistance for researchers or engineers who want to quickly have a comprehensive understanding about the KG EA and the trend lines.