TagClusters: Enhancing Semantic Understanding of Collaborative Tags

  • Authors:

  • Ya-Xi Chen;Rodrigo Santamaría;Andreas Butz;Roberto Therón

  • Affiliations:

  • Media Informatics, University of Munich, Germany;Department of Informatics and Automatics, University of Salamanca, Spain;Media Informatics, University of Munich, Germany;Department of Informatics and Automatics, University of Salamanca, Spain

  • Venue:
  • International Journal of Creative Interfaces and Computer Graphics
  • Year:
  • 2010

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Abstract

Many online communities use TagClouds, an aesthetic and easy to understand visualization, to represent popular tags collaboratively generated by their users. However, due to the free nature of tagging, such collaborative tags have linguistic problems and limitations, such as high semantic density. Moreover, the alphabetical order of TagClouds poorly supports a hierarchical exploration among tags. This paper presents an exploration to support semantic understanding of collaborative tags beyond TagClouds. Based on the results of the authors' survey of practical usages of collaborative tags, they developed a visualization named TagClusters, in which tags are clustered into different groups, with font size representing tag popularity and the spatial distance indicating the semantic similarity between tags. The subgroups in each group and the overlap between groups are highlighted, illustrating the underlying hierarchical structure and semantic relations between groups. The authors conducted a comparative evaluation with TagClouds and TagClusters based on the same tag set. The results confirmed the advantage of TagClusters in facilitating browsing, comparing and comprehending semantic relations between tags.