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

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Book cover Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (CCL 2018, NLP-NABD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Notes

  1. 1.

    https://github.com/Tsinghua-PhD/EFHET.

  2. 2.

    http://downloads.dbpedia.org/2016-10/.

  3. 3.

    https://github.com/yyaghoobzadeh/figment.

  4. 4.

    https://github.com/yyaghoobzadeh/figment-multi.

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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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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Hailong Jin, Lei Hou & Juanzi Li

Authors
  1. Hailong Jin
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  2. Lei Hou
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  3. Juanzi Li
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Corresponding author

Correspondence to Lei Hou .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Harbin Institute of Technology, Harbin, China

    Ting Liu

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Xiaojie Wang

  4. Tsinghua University, Beijing, China

    Zhiyuan Liu

  5. Tsinghua University, Beijing, China

    Yang Liu

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Jin, H., Hou, L., Li, J. (2018). Type Hierarchy Enhanced Heterogeneous Network Embedding for Fine-Grained Entity Typing in Knowledge Bases. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_15

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  • DOI: https://doi.org/10.1007/978-3-030-01716-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01715-6

  • Online ISBN: 978-3-030-01716-3

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