@inproceedings{897ed99950f8443f94d8b83a5c1f8066,
title = "A Survey on Translating Embedding based Entity Alignment in Knowledge Graphs",
abstract = "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.",
keywords = "entity alignment, knowledge graph, translating embeddings",
author = "Jin Jiang and Mohan Li and Zhaoquan Gu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 6th IEEE International Conference on Data Science in Cyberspace, DSC 2021 ; Conference date: 09-10-2021 Through 11-10-2021",
year = "2021",
doi = "10.1109/DSC53577.2021.00033",
language = "英语",
series = "Proceedings - 2021 IEEE 6th International Conference on Data Science in Cyberspace, DSC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "187--194",
booktitle = "Proceedings - 2021 IEEE 6th International Conference on Data Science in Cyberspace, DSC 2021",
address = "美国",
}