• DocumentCode
    1774001
  • Title

    Semantic ranking based on Computer Science Ontology weight

  • Author

    Boonyoung, Thanyaporn ; Mingkhwan, Anirach

  • Author_Institution
    Fac. of Inf. Technol., King Mongkut´s Univ. of Technol. North Bangkok, Bangkok, Thailand
  • fYear
    2014
  • fDate
    Sept. 29 2014-Oct. 1 2014
  • Firstpage
    86
  • Lastpage
    91
  • Abstract
    Document Ranking retrieval systems are the top documents ordering and particularly appropriate for user´s query. Most existing assigned based on the information retrieval term frequency (tf) that appears in the document. Although the number of times that the term occurrence is more relevant, but not meant for rank documents according to their proximity to user´s query. So this paper, we presented a new document semantic ranking process for the semantic ranking that proposes a new weight of query term in the document based on Computer Science Ontology weight. The experimental results show that the new document similarity score between a user´s query and the paper suggests that the new measures were effectively ranked.
  • Keywords
    computer science; query processing; computer science ontology weight; document ranking retrieval systems; document semantic ranking process; document similarity score; information retrieval term frequency; user query; Computational modeling; Computer science; Decision making; Information retrieval; Ontologies; Semantics; Vectors; Computer Science Ontology; Cosine Similarity; Semantic Ranking; Vector Space Model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Digital Information Management (ICDIM), 2014 Ninth International Conference on
  • Conference_Location
    Phitsanulok
  • Type

    conf

  • DOI
    10.1109/ICDIM.2014.6991426
  • Filename
    6991426