• DocumentCode
    1602748
  • Title

    A Semantic Similarity Measure for Conceptual Web Services Classification

  • Author

    Abid, Ahmed ; Messai, Nizar ; Rouached, Mohsen ; Devogele, Thomas ; Abid, Mohamed

  • Author_Institution
    LI, Francois Rabelais Univ., Tours, France
  • fYear
    2015
  • Firstpage
    128
  • Lastpage
    133
  • Abstract
    Classifying Web services into functionally similar groups is an efficient way to enhance services discovery, composition, and substitution processes. In order to ensure such efficiency, the classification process should rely on adequate semantic similarity measures. This paper presents a practical approach to measure the similarity of Web services. Both semantic and syntactic descriptions are integrated through specific techniques for computing similarity measures between services. Formal Concept Analysis (FCA) is used then to classify Web services into concept lattices in order to facilitate relevant services identification for composition and/or substitution purposes. The proposed similarity measure is evaluated and compared to some of the best-known existing ones. Results show a significant improvement of precision and recall of relevant services discovery for further composition and substitution tasks.
  • Keywords
    Web services; classification; formal concept analysis; semantic Web; FCA; concept lattices; conceptual Web service classification; formal concept analysis; semantic similarity measure; service composition; service discovery; service substitution processes; Context; Feature extraction; Lattices; Ontologies; Semantics; Syntactics; Web services; Formal Concept Analysis; Semantic Similarity; Semantic Web Services; Service Oriented Architecture;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 2015 IEEE 24th International Conference on
  • Conference_Location
    Larnaca
  • Type

    conf

  • DOI
    10.1109/WETICE.2015.48
  • Filename
    7194344