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Crowd-Type: A Crowdsourcing-Based Tool for Type Completion in Knowledge Bases

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Advances in Conceptual Modeling (ER 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11158))

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Abstract

Entity type completion in Knowledge Bases (KBs) is an important and challenging problem. In our recent work, we have proposed a hybrid framework which combines the human intelligence of crowdsourcing with automatic algorithms to address the problem. In this demo, we have implemented the framework in a crowdsourcing-based system, named Crowd-Type, for fine-grained type completion in KBs. In particular, Crowd-Type firstly employs automatic algorithms to select the most representative entities and assigns them to human workers, who will verify the types for assigned entities. Then, the system infers and determines the correct types for all entities utilizing both the results of crowdsourcing and machine-based algorithms. Our system gives a vivid demonstration to show how crowdsourcing significantly improves the performance of automatic type completion algorithms.

This work is supported by National Natural Science Foundation of China (No. 61602488, No. 61632016 and No. 61472427), the Research Funds of Renmin University of China (No. 18XNLG18) and Academy of Finland (No. 310321).

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Helsinki, Helsinki, Finland

    Jiaheng Lu

  2. DEKE, MOE and School of Information, Renmin University of China, Beijing, China

    Zhaoan Dong, Jianhong Tu, Ju Fan, Jiaheng Lu & Xiaoyong Du

  3. School of Computing, National University of Singapore, Singapore, Singapore

    Tok Wang Ling

Authors
  1. Zhaoan Dong
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  2. Jianhong Tu
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  3. Ju Fan
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  4. Jiaheng Lu
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  5. Xiaoyong Du
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  6. Tok Wang Ling
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Corresponding author

Correspondence to Ju Fan .

Editor information

Editors and Affiliations

  1. University of British Columbia, Vancouver, BC, Canada

    Carson Woo

  2. University of Helsinki, Helsinki, Finland

    Jiaheng Lu

  3. Northwestern Polytechnical University, Xian, China

    Zhanhuai Li

  4. Department of Computer Science, National University of Singapore, Singapore, Singapore

    Tok Wang Ling

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

    Guoliang Li

  6. National University of Singapore, Singapore, Singapore

    Mong Li Lee

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Dong, Z., Tu, J., Fan, J., Lu, J., Du, X., Ling, T.W. (2018). Crowd-Type: A Crowdsourcing-Based Tool for Type Completion in Knowledge Bases. In: Woo, C., Lu, J., Li, Z., Ling, T., Li, G., Lee, M. (eds) Advances in Conceptual Modeling. ER 2018. Lecture Notes in Computer Science(), vol 11158. Springer, Cham. https://doi.org/10.1007/978-3-030-01391-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-01391-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01390-5

  • Online ISBN: 978-3-030-01391-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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