• DocumentCode
    605
  • Title

    A Generalized Flow-Based Method for Analysis of Implicit Relationships on Wikipedia

  • Author

    Zhang, Xinpeng ; Asano, Yasuhito ; Yoshikawa, Masatoshi

  • Author_Institution
    Dept. of Social Inf., Kyoto Univ., Kyoto, Japan
  • Volume
    25
  • Issue
    2
  • fYear
    2013
  • fDate
    Feb. 2013
  • Firstpage
    246
  • Lastpage
    259
  • Abstract
    We focus on measuring relationships between pairs of objects in Wikipedia whose pages can be regarded as individual objects. Two kinds of relationships between two objects exist: in Wikipedia, an explicit relationship is represented by a single link between the two pages for the objects, and an implicit relationship is represented by a link structure containing the two pages. Some of the previously proposed methods for measuring relationships are cohesion-based methods, which underestimate objects having high degrees, although such objects could be important in constituting relationships in Wikipedia. The other methods are inadequate for measuring implicit relationships because they use only one or two of the following three important factors: distance, connectivity, and cocitation. We propose a new method using a generalized maximum flow which reflects all the three factors and does not underestimate objects having high degree. We confirm through experiments that our method can measure the strength of a relationship more appropriately than these previously proposed methods do. Another remarkable aspect of our method is mining elucidatory objects, that is, objects constituting a relationship. We explain that mining elucidatory objects would open a novel way to deeply understand a relationship.
  • Keywords
    Web sites; data mining; Wikipedia; cohesion-based methods; elucidatory object mining; generalized maximum flow-based method; implicit relationship analysis; link structure; Data mining; Electronic publishing; Encyclopedias; Internet; Wikipedia; Link analysis; Wikipedia mining; generalized flow; relationship;
  • fLanguage
    English
  • Journal_Title
    Knowledge and Data Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1041-4347
  • Type

    jour

  • DOI
    10.1109/TKDE.2011.227
  • Filename
    6081862