Evaluation of a Self-Adapting Method for Resource Classification in Folksonomies – KMO2012


Nowadays, folksonomies are currently the simplest way to classify information in Web 2.0. However, such folksonomies increase continuously their amount of information without any centralized control, complicating the knowledge representation. In this paper we analyse a method to group resources of collaborative social tagging systems in semantic categories. The main goal is to self-adapt folksonomies to represent the current knowledge. The related method is able to automatically create the classification categories 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.


José Javier Astrain, Alberto Córdoba, Francisco Echarte, Jesús Villadangos. 2012. In proceedings of the 7th International Conference on Knowledge Management in Organizations (KMO ’12): Service and Cloud Computing. Advances in Intelligent Systems and Computing Volume 172, 2013, pp 1-12.