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Column-Type Prediction for Web Tables Powered by Knowledge Base and Text

Author

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  • Junyi Wu

    (College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Chen Ye

    (College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    Jubang Group Co., Ltd., Yueqing 325600, China)

  • Haoshi Zhi

    (College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Shihao Jiang

    (College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China)

Abstract

Web tables are essential for applications such as data analysis. However, web tables are often incomplete and short of some critical information, which makes it challenging to understand the web table content. Automatically predicting column types for tables without metadata is significant for dealing with various tables from the Internet. This paper proposes a CNN-Text method to deal with this task, which fuses CNN prediction and voting processes. We present data augmentation and synthetic column generation approaches to improve the CNN’s performance and use extracted text to get better predictions. The experimental result shows that CNN-Text outperforms the baseline methods, demonstrating that CNN-Text is well qualified for the table column type prediction.

Suggested Citation

  • Junyi Wu & Chen Ye & Haoshi Zhi & Shihao Jiang, 2023. "Column-Type Prediction for Web Tables Powered by Knowledge Base and Text," Mathematics, MDPI, vol. 11(3), pages 1-15, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:560-:d:1042851
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