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Interactive visualization for opportunistic exploration of large document collections
Abstract Finding relevant information in a large anFinding relevant information in a large and comprehensive collection of cross-referenced documents like Wikipedia usually requires a quite accurate idea where to look for the pieces of data being sought. A user might not yet have enough domain-specific knowledge to form a precise search query to get the desired result on the first try. Another problem arises from the usually highly cross-referenced structure of such document collections. When researching a subject, users usually follow some references to get additional information not covered by a single document. With each document, more opportunities to navigate are added and the structure and relations of the visited documents gets harder to understand. This paper describes the interactive visualization Wivi which enables users to intuitively navigate Wikipedia by visualizing the structure of visited articles and emphasizing relevant other topics. Combining this visualization with a view of the current article results in a custom browser specially adapted for exploring large information networks. By visualizing the potential paths that could be taken, users are invited to read up on subjects relevant to the current point of focus and thus opportunistically finding relevant information. Results from a user study indicate that this visual navigation can be easily used and understood. A majority of the participants of the study stated that this method of exploration supports them finding information in Wikipedia.rts them finding information in Wikipedia.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Conclusion Our approach combines both a visualizationOur approach combines both a visualization of visited articles and articles that could be immediately reached from all visited articles. It also calculates a degree of interest of the unvisited articles based on the structure and history of the article graph. With Wivi2 we created a browser for the Wikipedia or other wikis, which implements the visualization of already visited articles in a hierarchical tree layout and shows the related unvisited articles weighted by their degree of interest on circles around the visited articles. As the result of a user test shows, this approach is generally accepted and positively perceived as a viable interface to browse and search the Wikipedia. Especially the visualization of the visited part was well received, but also our concept of weighting and displaying the unvisited articles to enable opportunistic exploration appears to be promising.istic exploration appears to be promising.
Data source Experiment responses  +
Doi 10.1016/j.is.2009.10.004 +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Interactive%2Bvisualization%2Bfor%2Bopportunistic%2Bexploration%2Bof%2Blarge%2Bdocument%2Bcollections%22  +
Has author Simon Lehmann + , Ulrich Schwanecke + , Ralf Dörner +
Has domain Computer science +
Has topic Ontology building +
Issue 2  +
Pages 260-269  +
Peer reviewed Yes  +
Publication type Journal article  +
Published in Information Systems +
Research design Design science  + , Experiment  + , Statistical analysis  +
Research questions This paper describes the interactive visuaThis paper describes the interactive visualization Wivi which enables users to intuitively navigate Wikipedia by visualizing the structure of visited articles and emphasizing relevant other topics. Combining this visualization with a view of the current article results in a custom browser specially adapted for exploring large information networks. By visualizing the potential paths that could be taken, users are invited to read up on subjects relevant to the current point of focus and thus opportunistically finding relevant information. Results from a user study indicate that this visual navigation can be easily used and understood. A majority of the participants of the study stated that this method of exploration supports them finding information in Wikipedia.rts them finding information in Wikipedia.
Revid 10,831  +
Theories Undetermined
Theory type Design and action  +
Title Interactive visualization for opportunistic exploration of large document collections
Unit of analysis N/A  +
Url http://dx.doi.org/10.1016/j.is.2009.10.004  +
Volume 35  +
Wikipedia coverage Sample data  +
Wikipedia data extraction Dump  +
Wikipedia language Not specified  +
Wikipedia page type N/A  +
Year 2010  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:29:14  +
Categories Ontology building  + , Computer science  + , Publications with missing comments  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:29:05  +
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