IDEAS home Printed from https://ideas.repec.org/a/igg/jwsr00/v18y2021i3p63-81.html
   My bibliography  Save this article

Web Service Clustering Approach Based on Network and Fused Document-Based and Tag-Based Topics Similarity

Author

Listed:
  • Deng Li Ping

    (Institute of Public Safety and Big Data, College of Data Science, Taiyuan University of Technology, China)

  • Guo Bing

    (Department of Computer Science and Technology, Taiyuan Normal University, China)

  • Zheng Wen

    (Institute of Public Safety and Big Data, College of Data Science, Taiyuan University of Technology, China)

Abstract

To produce a web services clustering with values that satisfy many requirements is a challenging focus. In this article, the authors proposed a new approach with two models, which are helpful to the service clustering problem. Firstly, a document-tag LDA model (DTag-LDA) is proposed that considers the tag information of web services, and the tag can describe the effective information of documents accurately. Based on the first model, this article further proposes an efficient document weight and tag weight-LDA model (DTw-LDA), which fused multi-modal data network. To further improve the clustering accuracy, the model constructs the network for describing text and tag respectively and then merges the two networks to generate web service network clustered. In addition, this article also designs experiments to verify that the used auxiliary information can help to extract more accurate semantics by conducting service classification. And the proposed method has obvious advantages in precision, recall, purity, and other performance.

Suggested Citation

  • Deng Li Ping & Guo Bing & Zheng Wen, 2021. "Web Service Clustering Approach Based on Network and Fused Document-Based and Tag-Based Topics Similarity," International Journal of Web Services Research (IJWSR), IGI Global, vol. 18(3), pages 63-81, July.
  • Handle: RePEc:igg:jwsr00:v:18:y:2021:i:3:p:63-81
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWSR.2021070104
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jwsr00:v:18:y:2021:i:3:p:63-81. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.