• DocumentCode
    425861
  • Title

    Resource association discovery in semantic Web

  • Author

    Bangyong, Liang ; Jie, Tang ; Juanzi, Li ; Kehong, Wang

  • Author_Institution
    Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing
  • fYear
    2004
  • fDate
    15-15 Sept. 2004
  • Firstpage
    266
  • Lastpage
    269
  • Abstract
    Resources on the Web have direct and hidden relations. Direct relations mean the visible link between two resources like hyperlinks. Direct relations are easy to discover while hidden relations are not. Current discovery methods are mostly based on the text learning and user feedback methods. The text contents are not fit for inference and they are also lack of semantics. The Web pages are annotated with ontologies in semantic Web. The annotations are useful for inference. We propose a framework for resource association discovery in semantic Web. The framework uses the annotation for inference and the annotation hierarchy can be solved by mapping different ontologies. The experiment shows the satisfied results. Finally, the conclusion and future work are discussed
  • Keywords
    data mining; hypermedia; ontologies (artificial intelligence); semantic Web; text analysis; annotation hierarchy; hyperlinks; inference annotation; resource association discovery; semantic Web ontology; text learning method; user feedback method; Collaboration; Computer science; Feedback; Keyboards; Knowledge engineering; Mice; Ontologies; Relational databases; Semantic Web; Web pages;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    E-Commerce Technology for Dynamic E-Business, 2004. IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    0-7695-2206-8
  • Type

    conf

  • DOI
    10.1109/CEC-EAST.2004.56
  • Filename
    1388334