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Evaluation of a Self-adapting Method for Resource Classification in Folksonomies

  • José Javier Astrain
  • Alberto Córdoba
  • Francisco Echarte
  • Jesús Villadangos
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 172)

Abstract

Nowadays, folksonomies are currently the simplest way to classify information inWeb 2.0. However, such folksonomies increase continuously their amount of information without any centralized control, complicating the knowledge representation. We analyse a method to group resources of collaborative-social tagging systems in semantic categories. It is able to automatically create the classification categories to represent the current knowledge and to self-adapt to the changes of the folksonomies, classifying the resources under categories and creating/deleting them. As opposed to current proposals that require the re-evaluation of the whole folksonomy to maintain updated the categories, our method is an incremental aggregation technique which guarantees its adaptation to highly dynamic systems without requiring a full reassessment of the folksonomy.

Keywords

Component Representation Cluster Component Initial Centroid Concept Creation Social Bookmark System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José Javier Astrain
    • 1
  • Alberto Córdoba
    • 1
  • Francisco Echarte
    • 1
  • Jesús Villadangos
    • 1
  1. 1.Dept. Ingeniería Matemática e InformáticaUniversidad Pública de NavarraPamplonaSpain

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