Sense induction in folksonomies: a review

  • Authors:

  • Pierre Andrews;Juan Pane

  • Affiliations:

  • Departamento de Ciência da Computação, Universidade de São Paulo, São Paulo, Brazil;Dipartimento di Ingegneria e Scienza dell'Informazione, The University of Trento, Trento, Italy and Universidad Católica Nuestra Señora de la Asunción, DEI, Asunción, Paraguay

  • Venue:
  • Artificial Intelligence Review
  • Year:
  • 2013

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Abstract

Folksonomies, often known as tagging systems, such as the ones used on the popular Delicious or Flickr websites, use a very simple Knowledge Organisation System. Users have thus been quick to adopt this system and create extensive annotations on the Web. However, because of the simplicity of the folksonomy model, the semantics of the tags used is not explicit and can only be inferred from their context of use. This is a barrier for the automatic use of such Knowledge Organisation Systems by computers and new techniques have been developed to extract the semantic of the tags. In this article we discuss the drawbacks of some of these approaches and propose a generalization of the different approaches to detect new senses of terms in a folksonomy. Another weak point of the current state of the art in the field is the lack of formal evaluation methodology; we thus propose a novel evaluation framework. We introduce a dataset and evaluation methodology that enable the comparison of results between different approaches to sense induction in folksonomies. Finally we discuss the performances of different approaches to the task of homonymous/polysemous tag detection and synonymous identification.