• DocumentCode
    1532194
  • Title

    Uncertainty Reduction for Knowledge Discovery and Information Extraction on the World Wide Web

  • Author

    Ji, Heng ; Deng, Hongbo ; Han, Jiawei

  • Author_Institution
    Department of Computer Science, City University of New York, New York City, NY, USA
  • Volume
    100
  • Issue
    9
  • fYear
    2012
  • Firstpage
    2658
  • Lastpage
    2674
  • Abstract
    In this paper, we give an overview of knowledge discovery (KD) and information extraction (IE) techniques on the World Wide Web (WWW). We intend to answer the following questions: What kind of additional uncertainty challenges are introduced by the WWW setting to basic KD and IE techniques? What are the fundamental techniques that can be used to reduce such uncertainty and achieve reasonable KD and IE performance on the WWW? What is the impact of each novel method? What types of interactions can be conducted between these techniques and information networks to make them benefit from each other? In what way can we utilize the results in more interesting applications? What are the remaining challenges and what are the possible ways to address these challenges? We hope this can provide a road map to advance KD and IE on the WWW to a higher level of performance, portability and utilization.
  • Keywords
    Analytical models; Hidden Markov models; Natural language processing; Text mining; Text processing; Uncertainty; World Wide Web; natural language processing; text analysis; text mining;
  • fLanguage
    English
  • Journal_Title
    Proceedings of the IEEE
  • Publisher
    ieee
  • ISSN
    0018-9219
  • Type

    jour

  • DOI
    10.1109/JPROC.2012.2190489
  • Filename
    6212297