Research About Knowledge Graph Completion Based on Active Learning

Jisuanji kexue yu tansuo

CHEN Qinkuang, CHEN Ke, WU Sai, SHOU Lidan, CHEN Gang¹ 

[1] 1. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China 2. Key Laboratory of Big Data Intelligent Computing of Zhejiang Province (Zhejiang University)，Hangzhou 310027, China;

关键词: active learning;    knowledge graph completion;    link prediction;    relationship verification;   

DOI  :  10.3778/j.issn.1673-9418.1908026

来源: DOAJ

【 摘 要 】

Knowledge graph completion focuses on how to improve the missing information in knowledge graph. Knowledge graph completion task has many applications, for example, it can be applied to the knowledge graph of rail transit system to support the system design and maintenance optimization. The existing algorithm has high time com-plexity for the real knowledge graph, and it does not make good use of the data out of the knowledge graph. In view of the above two limitations, this paper proposes a knowledge graph completion framework based on active learning. Combined with the idea of active learning, this framework uses link prediction to predict the top-k pairs of entities which are most likely to generate links in the missing knowledge graph. The framework fully considers the internal and external information, and uses the combination of the internal data of the knowledge graph and the external data to realize the missing completion of the knowledge graph. Based on Freebase and DBpedia datasets, a comparative experiment is carried out for the existing work. Experiment results show that, the enhance link prediction (ELP) algorithm has better effect and active learning ability. The relationship verification combining internal data and exter-nal data of the knowledge graph can verify the triples more effectively.

【 授权许可】

Unknown