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01-Applied Mathematics & Information Sciences
An International Journal


Volumes > Volume 08 > No. 1


Scalable Visualization of Semantic Nets using Power-Law Graphs

PP: 355-367
Ajaz Hussain, Khalid Latif, Aimal Tariq Rextin, Amir Hayat, Masoon Alam,
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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