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Type Hierarchy Enhanced Heterogeneous Network Embedding for Fine-Grained Entity Typing in Knowledge Bases

  • Hailong Jin
  • Lei Hou
  • Juanzi Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11221)

Abstract

Type information is very important in knowledge bases, but some large knowledge bases are lack of type information due to the incompleteness of knowledge bases. In this paper, we propose to use a well-defined taxonomy to help complete the type information in some knowledge bases. Particularly, we present a novel embedding based hierarchical entity typing framework which uses learning to rank algorithm to enhance the performance of word-entity-type network embedding. In this way, we can take full advantage of labeled and unlabeled data. Extensive experiments on two real-world datasets of DBpedia show that our proposed method significantly outperforms 4 state-of-the-art methods, with 2.8% and 4.2% improvement in Mi-F1 and Ma-F1 respectively.

Keywords

Entity typing Knowledge base completion Heterogeneous network embedding 

Notes

Acknowledgment

The work is supported by the national key research and development program of China (No. 2017YFB1002101), NSFC key project (U1736204, 61661146007), Fund of Online Education Research Center, Ministry of Education (No. 2016ZD102), and THU-NUS NExT Co-Lab.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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