An automatic approach for tagging Web services using machine learning techniques.

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  • Additional Information
    • Author-Supplied Keywords:
      classification
      clustering
      recommendation
      tagging
      Web services
    • NAICS/Industry Codes:
      518210 Data Processing, Hosting, and Related Services
    • Abstract:
      Web services have become popular and increasingly important in e-business and e-commerce applications especially in large scale distributed systems. As a result, an increasing number of Web services have been developed. However, this huge collection of Web services makes the task of locating a suitable one more challenging as well as more difficult. Automatic clustering ofWeb services groups services with similar functions together. Clustering could greatly boost the power of Web service search engines and generate tags to improve the search accuracy of tag-based service recommendation. In this paper, we propose a Web service clustering technique based on Carrot search clustering and K-means to group similar services together. These clustered groups are then tagged. We also develop a tag-based service recommendation for WSDL documents using naive bayes algorithm to classify Web services into different tags. We demonstrate that the proposed clustering approach is effective for Web service discovery through two sets of real data. [ABSTRACT FROM AUTHOR]
    • Abstract:
      Copyright of Web Intelligence (2405-6456) is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
    • Author Affiliations:
      1Department of Computer Science, University of Hong Kong, Pokfulam Road, Hong Kong
    • ISSN:
      2405-6456
    • Accession Number:
      10.3233/WEB-160334
    • Accession Number:
      114969009
  • Citations
    • ABNT:
      LIN, M.; CHEUNG, D. W. An automatic approach for tagging Web services using machine learning techniques. Web Intelligence (2405-6456), [s. l.], v. 14, n. 2, p. 99–118, 2016. Disponível em: . Acesso em: 21 ago. 2019.
    • AMA:
      Lin M, Cheung DW. An automatic approach for tagging Web services using machine learning techniques. Web Intelligence (2405-6456). 2016;14(2):99-118. doi:10.3233/WEB-160334.
    • APA:
      Lin, M., & Cheung, D. W. (2016). An automatic approach for tagging Web services using machine learning techniques. Web Intelligence (2405-6456), 14(2), 99–118. https://doi.org/10.3233/WEB-160334
    • Chicago/Turabian: Author-Date:
      Lin, Maria, and David W. Cheung. 2016. “An Automatic Approach for Tagging Web Services Using Machine Learning Techniques.” Web Intelligence (2405-6456) 14 (2): 99–118. doi:10.3233/WEB-160334.
    • Harvard:
      Lin, M. and Cheung, D. W. (2016) ‘An automatic approach for tagging Web services using machine learning techniques’, Web Intelligence (2405-6456), 14(2), pp. 99–118. doi: 10.3233/WEB-160334.
    • Harvard: Australian:
      Lin, M & Cheung, DW 2016, ‘An automatic approach for tagging Web services using machine learning techniques’, Web Intelligence (2405-6456), vol. 14, no. 2, pp. 99–118, viewed 21 August 2019, .
    • MLA:
      Lin, Maria, and David W. Cheung. “An Automatic Approach for Tagging Web Services Using Machine Learning Techniques.” Web Intelligence (2405-6456), vol. 14, no. 2, Apr. 2016, pp. 99–118. EBSCOhost, doi:10.3233/WEB-160334.
    • Chicago/Turabian: Humanities:
      Lin, Maria, and David W. Cheung. “An Automatic Approach for Tagging Web Services Using Machine Learning Techniques.” Web Intelligence (2405-6456) 14, no. 2 (April 2016): 99–118. doi:10.3233/WEB-160334.
    • Vancouver/ICMJE:
      Lin M, Cheung DW. An automatic approach for tagging Web services using machine learning techniques. Web Intelligence (2405-6456) [Internet]. 2016 Apr [cited 2019 Aug 21];14(2):99–118. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=114969009&custid=s8280428