Improving Tag-Clouds as Visual Information Retrieval Interfaces

Abstract

Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud's tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.

References in corpus (2)

• Collaborative Tagging and Semiotic Dynamics
• Collaborative thesaurus tagging the Wikipedia way

Cited by in corpus (10)

• Tag-Cloud Drawing: Algorithms for Cloud Visualization
• Web 2.0 OLAP: From Data Cubes to Tag Clouds
• Word Storms: Multiples of Word Clouds for Visual Comparison of Documents
• Tag Clusters as Information Retrieval Interfaces
• Measuring Similarity in Large-scale Folksonomies
• Incorporating Text Analysis into Evolution of Social Groups in Blogosphere
• QuViS -- The Question of Visual Site Selection
• Tagged Documents Co-Clustering
• Document Visualization using Topic Clouds
• Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation