A Probability relevance classification approach for service information discovery using semantic domain knowledge

  • Authors

    • V Srilakshmi
    • Dr K. Anuradha
    • Dr C. Shoba Bindu
    https://doi.org/10.14419/ijet.v7i3.29.19308
  • Information Discovery, Classification, Probability Relevance, Domain Knowledge.

  • Abstract

    The intense growth of information systems and domain services has made it difficult to provide accurate and relevant information in relation to queries and domain service needs. Conventional domain service categorization facilitates searching for related services and helps to determine classification with defined domain service knowledge and taxonomy, but it fails to relate the service which is conceptually related as such. The nonexistence of any automated mechanism for domain knowledge and taxonomy enhancement causing a high number of irrelevant services information discovery for a requested query. This paper proposes a Probability Relevance Classification (PRC) approach to overcoming the constraint of automatic classification and conceptual knowledge enhancement through constructing relevance domain knowledge semantically in support of Domain Ontology Model (DOM). The proposed PRC approach classifies the information in support of a customized Naive Bayes method and Semantic Terms Similarity method in association to DOM constructed. The experimental assessment of the recommended approach shows an improvement in the service classification and achieves better relevance results in related to the service query request. The classification accuracy in comparison with the existing classifiers shows an improvisation of the proposal.

     

     

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  • How to Cite

    Srilakshmi, V., K. Anuradha, D., & C. Shoba Bindu, D. (2018). A Probability relevance classification approach for service information discovery using semantic domain knowledge. International Journal of Engineering & Technology, 7(3.29), 543-549. https://doi.org/10.14419/ijet.v7i3.29.19308

    Received date: 2018-09-09

    Accepted date: 2018-09-09