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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2019: Advances in Knowledge Discovery and Data Mining pp 501–513Cite as

Knowledge Base Completion by Inference from Both Relational and Literal Facts

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11441))

Abstract

Knowledge base (KB) completion predicts new facts in a KB by performing inference from the existing facts, which is very important for expanding KBs. Most previous KB completion approaches infer new facts only from the relational facts (facts containing object properties) in KBs. Actually, there are large number of literal facts (facts containing datatype properties) besides the relational ones in most KBs; these literal facts are ignored in the previous approaches. This paper studies how to take the literal facts into account when making inference, aiming to further improve the performance of KB completion. We propose a new approach that consumes both relational and literal facts to predict new facts. Our approach extracts literal features from literal facts, and incorporates them with path-based features extracted from relational facts; a predictive model is then trained on all the features to infer new facts. Experiments on YAGO KB show that our approach outperforms the compared approaches that only take relational facts as input.

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Notes

  1. 1.

    http://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/downloads/.

  2. 2.

    Not tested relations: Earth, hasGender, wasBornIn, isLeaderOf, participatedIn, isAffiliatedTo.

  3. 3.

    https://github.com/matt-gardner/pra.

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Acknowledgments

The work is supported by the National Key Research and Development Program of China (No. 2017YFC0804000) and the National Natural Science Foundation of China (No. 61772079).

Author information

Authors and Affiliations

  1. Beijing Normal University, Beijing, China

    Zhichun Wang

  2. China UnionPay Co., Ltd., Shanghai, China

    Yong Huang

Authors
  1. Zhichun Wang
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  2. Yong Huang
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Corresponding author

Correspondence to Zhichun Wang .

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Qiang Yang

  2. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  3. University of Macau, Taipa, Macau, China

    Zhiguo Gong

  4. Southeast University, Nanjing, China

    Min-Ling Zhang

  5. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Sheng-Jun Huang

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Wang, Z., Huang, Y. (2019). Knowledge Base Completion by Inference from Both Relational and Literal Facts. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_39

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  • DOI: https://doi.org/10.1007/978-3-030-16142-2_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16141-5

  • Online ISBN: 978-3-030-16142-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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