Tail Entity Recognition and Linking for Knowledge Graphs

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Conference Paper

Zhang, Dalei
Qiang, Yang
Li, Zhixu
Fang, Junhua
He, Ying
Zheng, Xin
Chen, Zhigang

KAUST Department
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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This paper works on a new task - Tail Entity Recognition and Linking (TERL) for Knowledge Graphs (KG), i.e., recognizing ambiguous entity mentions from the tails of some relational triples, and linking these mentions to their corresponding KG entities. Although plenty of work has been done on both entity recognition and entity linking, the TERL problem in this specific scenario is untouched. In this paper, we work towards the TERL problem by fully leveraging KG information with two neural models for solving the two sub-problems, i.e., tail entity recognition and tail entity linking respectively. We finally solve the TERL problem end-to-end by proposing a joint learning mechanism with the two proposed neural models, which could further improve both tail entity recognition and linking results. To the best of our knowledge, this is the first effort working towards TERL for KG. Our empirical study conducted on real-world datasets shows that our models can effectively expand KG and improve the quality of KG.

Zhang, D., Qiang, Y., Li, Z., Fang, J., He, Y., Zheng, X., & Chen, Z. (2020). Tail Entity Recognition and Linking for Knowledge Graphs. Lecture Notes in Computer Science, 286–301. doi:10.1007/978-3-030-60259-8_22

This research is partially supported by National Key R&D Program of China (No. 2018AAA0101900), Natural Science Foundation of Jiangsu Province (No. BK2019 1420), National Natural Science Foundation of China (Grant No. 61632016), Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA5 20003), the Suda-Toycloud Data Intelligence Joint Laboratory and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Springer Nature

Conference/Event Name
4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020


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