• DocumentCode
    1983002
  • Title

    Discipline-Ontology Based Learning Resources Semantic Retrieval Algorithm

  • Author

    Yang Qing ; Xiao Jiaquan

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Wuhan Univ. of Technol., Wuhan, China
  • fYear
    2010
  • fDate
    20-22 Aug. 2010
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    There is much difference among learners and learning resource providers when the semantic of learning resources is understood and expressed, and it is the important reason which causes lower accuracy in learning resources retrieval. In order to resolve the problem above, three mechanisms are applied as follows: 1) Discipline Ontology is constructed, which is the formalization for concepts and the relationships between concepts existing in some discipline domain. OWL is adopted as Discipline Ontology description language; 2) Inference rules are defined on the basis of Discipline Ontology. Semantic extension on keyword from user is performed by using Jena inference engine and inference rules , so as to better interpret and describe the requirement; 3) Learning resource metadata is extracted and defined by following Learning Resource Meta-data Specification, so as to provide formal description for learning resources. A semantic retrieval framework for learning resources is presented, and the process of learning resource semantic retrieval algorithm is discussed in detail. Firstly, the semantic extension on inquiry keyword from user is performed on the basis of Discipline Ontology; secondly, by using the improved similarity calculating formula, the keywords produced by semantic extension are sequenced. A set of keywords which have higher similarity with inquiry keyword are sorted out, and are used as inquiry keywords; then, search is performed on the basis of inquiry keywords and learning resource metadata. A set of descriptions for learning resources, which probably meet the requirement of user, is sent to user. The algorithm provides an approach for learning resource retrieval, and is able to support the effective access on learning resources.
  • Keywords
    inference mechanisms; information retrieval; learning (artificial intelligence); ontologies (artificial intelligence); semantic Web; Jena inference engine; OWL; discipline ontology description language; formal description; learning resources semantic retrieval algorithm; meta data specification; Educational institutions; Electronic learning; Java; OWL; Ontologies; Semantics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Internet Technology and Applications, 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-5142-5
  • Electronic_ISBN
    978-1-4244-5143-2
  • Type

    conf

  • DOI
    10.1109/ITAPP.2010.5566554
  • Filename
    5566554