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China Conference on Knowledge Graph and Semantic Computing

CCKS 2017: Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence pp 1–6Cite as

Knowledge Base Completion by Learning to Rank Model

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 784))

Abstract

Knowledge base (KB) completion aims to predict new facts from the existing ones in KBs. There are many KB completion approaches, one of the state-of-art approaches is Path Ranking Algorithm (PRA), which predicts new facts based on path types connecting entities. PRA treats the relation prediction as a classification problem, and logistic regression is used as the classification model. In this work, we consider the relation prediction as a ranking problem; learning to rank model is trained on path types to predict new facts. Experiments on YAGO show that our proposed approach outperforms approaches using classification models.

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Notes

  1. 1.

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

  2. 2.

    http://www.mpi-inf.mpg.de/.

  3. 3.

    http://scikit-learn.org/stable.

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Acknowledgement

The work is supported by project of Beijing Advanced Innovation Center for Future Education (BJAICFE2016IR-002).

Author information

Authors and Affiliations

  1. Beijing Advanced Innovation Center for Future Education, Beijing Normal University, XinJieKouWai St. 19, HaiDian District, Beijing, 100875, People’s Republic of China

    Yong Huang & Zhichun Wang

  2. College of Information Science and Technology, Beijing Normal University, XinJieKouWai St. 19, HaiDian District, Beijing, 100875, People’s Republic of China

    Yong Huang & Zhichun Wang

Authors
  1. Yong Huang
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  2. Zhichun Wang
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Corresponding author

Correspondence to Zhichun Wang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Juanzi Li

  2. Beijing Xigema Center, Beijing, China

    Ming Zhou

  3. School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China

    Guilin Qi

  4. Google, Mountain View, California, USA

    Ni Lao

  5. East China University of Science and Technology, Shanghai, China

    Tong Ruan

  6. Guangdong University of Foreign Studies, Guangzhou, China

    Jianfeng Du

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Huang, Y., Wang, Z. (2017). Knowledge Base Completion by Learning to Rank Model. In: Li, J., Zhou, M., Qi, G., Lao, N., Ruan, T., Du, J. (eds) Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence. CCKS 2017. Communications in Computer and Information Science, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-10-7359-5_1

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  • DOI: https://doi.org/10.1007/978-981-10-7359-5_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7358-8

  • Online ISBN: 978-981-10-7359-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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