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A Semi-structured Data Classification Model with Integrating Tag Sequence and Ngram

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Database Systems for Advanced Applications (DASFAA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12682))

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Abstract

Many collaboratively building resources, such as Wikipedia, Weibo and Quora, exist in the form of semi-structured data and semi-structured data classification plays an important role in many data analysis applications. In addition to content information, semi-structured data also contain structural information. Thus, combining the structure and content features is a crucial issue in semi-structured data classification. In this paper, we propose a supervised semi-structured data classification approach that utilizes both the structural and content information. In this approach, generalized tag sequences are extracted from the structural information, and nGrams are extracted from the content information. Then the tag sequences and nGrams are combined into features called TSGram according to their link relation, and each semi-structured document is represented as a vector of TSGram features. Based on the TSGram features, a classification model is devised to improve the performance of semi-structured data classification. Because TSGram features retain the association between the structural and content information, they are helpful in improving the classification performance. Our experimental results on two real datasets show that the proposed approach is effective.

This work was supported in part by the National Natural Science Foundation of China under Grant 61972317, Grant 61672432 and Grant 61732014, and in part by the Fundamental Research Funds for the Central Universities of China under Grant 3102015JSJ0004.

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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China

    Lijun Zhang, Ning Li, Wei Pan & Zhanhuai Li

  2. Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, 710072, China

    Lijun Zhang, Ning Li, Wei Pan & Zhanhuai Li

Authors
  1. Lijun Zhang
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  2. Ning Li
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  3. Wei Pan
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  4. Zhanhuai Li
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Corresponding author

Correspondence to Lijun Zhang .

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Christian S. Jensen

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  4. The Pennsylvania State University, University Park, PA, USA

    Wang-Chien Lee

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  6. Athens University of Economics and Business, Athens, Greece

    Vana Kalogeraki

  7. National Cheng Kung University, Tainan City, Taiwan

    Jen-Wei Huang

  8. National Tsing Hua University, Hsinchu, Taiwan

    Chih-Ya Shen

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Zhang, L., Li, N., Pan, W., Li, Z. (2021). A Semi-structured Data Classification Model with Integrating Tag Sequence and Ngram. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12682. Springer, Cham. https://doi.org/10.1007/978-3-030-73197-7_14

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  • DOI: https://doi.org/10.1007/978-3-030-73197-7_14

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-73197-7

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