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ZoomRDF: semantic fisheye zooming on RDF data

Published: 26 April 2010 Publication History
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  • Abstract

    With the development of Semantic Web in recent years, an increasing amount of semantic data has been created in form of Resource Description Framework (RDF). Current visualization techniques help users quickly understand the underlying RDF data by displaying its structure in an overview. However, detailed information can only be accessed by further navigation. An alternative approach is to display the global context as well as the local details simultaneously in a unified view. This view supports the visualization and navigation on RDF data in an integrated way. In this demonstration, we present ZoomRDF, a framework that: i) adapts a space-optimized visualization algorithm for RDF, which allows more resources to be displayed, thus maximizes the utilization of display space, ii) combines the visualization with a fisheye zooming concept, which assigns more space to some individual nodes while still preserving the overview structure of the data, iii) considers both the importance of resources and the user interaction on them, which offers more display space to those elements the user may be interested in. We implement the framework based on the Gene Ontology and demonstrate that it facilitates tasks like RDF data exploration and editing.

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    1. ZoomRDF: semantic fisheye zooming on RDF data

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      Published In

      cover image ACM Other conferences
      WWW '10: Proceedings of the 19th international conference on World wide web
      April 2010
      1407 pages
      ISBN:9781605587998
      DOI:10.1145/1772690

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 26 April 2010

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      Author Tags

      1. RDF data
      2. information visualization
      3. semantic web

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      • Demonstration

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      WWW '10
      WWW '10: The 19th International World Wide Web Conference
      April 26 - 30, 2010
      North Carolina, Raleigh, USA

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      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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