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A Property-Based Method for Acquiring Commonsense Knowledge

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Knowledge Science, Engineering and Management (KSEM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12815))

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Abstract

Commonsense knowledge is crucial in a variety of AI applications. However, one kind of commonsense knowledge that has not received attention is that of properties of actions denoted by verbs. To address this limitation, we propose an approach to acquiring commonsense knowledge about action properties. In this paper, we take self-motion actions as an example to present our method. We first identify commonsense properties of actions from their definitions. We then introduce a list of dimensions for acquiring commonsense knowledge based on adjectives. Finally, we extract commonsense knowledge from text by parsing sentences that involve actions. Experiments show that our method allows to obtain high-quality commonsense knowledge.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Ya Wang, Cungen Cao & Shi Wang

  2. University of Chinese Academy of Sciences, Beijing, China

    Ya Wang

  3. Department of Computer Science and Information Technology, Guangxi Normal University, Guilin, 541004, China

    Yuting Cao

Authors
  1. Ya Wang
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  2. Cungen Cao
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  3. Yuting Cao
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  4. Shi Wang
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Corresponding author

Correspondence to Shi Wang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Han Qiu

  2. Ibaraki University, Hitachi, Japan

    Cheng Zhang

  3. University of Kentucky, Lexington, KY, USA

    Zongming Fei

  4. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

  5. Princeton University, Princeton, NJ, USA

    Sun-Yuan Kung

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Wang, Y., Cao, C., Cao, Y., Wang, S. (2021). A Property-Based Method for Acquiring Commonsense Knowledge. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12815. Springer, Cham. https://doi.org/10.1007/978-3-030-82136-4_5

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  • DOI: https://doi.org/10.1007/978-3-030-82136-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82135-7

  • Online ISBN: 978-3-030-82136-4

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