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01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 

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Volumes > Volume 8 > No. 1

 
   

Scalable Visualization of Semantic Nets using Power-Law Graphs

PP: 355-367
Author(s)
Ajaz Hussain, Khalid Latif, Aimal Tariq Rextin, Amir Hayat, Masoon Alam,
Abstract
Scalability and performance implications of semantic net visualization techniques are open research challenges. This paper focuses on developing a visualization technique that mitigates these challenges.We present a novel approach that exploits the underlying concept of power-law degree distribution as many realistic semantic nets seems to possess a power law degree distribution and present a small world phenomenon. The core concept is to partition the node set of a graph into power and non-power nodes and to apply a modified force-directed method that emphasizes the power nodes which results in establishing local neighborhood clusters among power nodes. We also made refinements in conventional force-directed method by tuning the temperature cooling mechanism in order to resolve ‘local-minima’ problem. To avoid cluttered view, we applied semantic filtration on nodes, ensuring zero loss of semantics. Results show that our technique handles very large scale semantic nets with a substantial performance improvement while producing aesthetically pleasant layouts. A visualization tool, NavigOWL, is developed by using this technique which has been ported as a plug-in for Protege, a famous ontology editor.

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