• DocumentCode
    2253175
  • Title

    ECKDF: Extended conceptual knowledge discovery in folksonomy

  • Author

    Hao, Fei ; Zhong, Shengtong

  • Author_Institution
    Dept. of Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea
  • fYear
    2010
  • fDate
    3-5 Dec. 2010
  • Firstpage
    71
  • Lastpage
    76
  • Abstract
    Social bookmarking tools are rapidly emerging on the Web. A conceptual structure called folksonomy plays an important role in such systems. The folksonomy is constitute of tagging data(users, tags, resources) which organizing and classifying information on the Web. Tagging data stored in the folksonomy includes a lot of very useful information and knowledge. Unlike ontologies, shared conceptualizations in folksonomy are not formalized and it is rather implicit. The hidden knowledge Discovering from folksonomy is becoming the main research task among the social sharing resources systems. In this paper, we propose an approach of folksonomy data mining based on Variable Precise Concepts (VPC) for discovering the extended conceptual knowledge(tag recommendation, resources suggestion) from folksonomy. Finally, the feasibility and efficiency of our approach are demonstrated by experiments.
  • Keywords
    data mining; social networking (online); ECKDF; extended conceptual knowledge discovery; folksonomy data mining; hidden knowledge Discovering; information classifying; ontologies; shared conceptualizations; social bookmarking tools; social sharing resources systems; tagging; variable precise concepts; Artificial intelligence; Artificial neural networks; Context; Data mining; Delta modulation; Lattices; Tagging;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Problem-Solving (ICCP), 2010 International Conference on
  • Conference_Location
    Lijiang
  • Print_ISBN
    978-1-4244-8654-0
  • Type

    conf

  • Filename
    5696014