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.
Digital Object Identifier (DOI)
Hussain, Ajaz; Latif, Khalid; Tariq Rextin, Aimal; Hayat, Amir; and Alam, Masoon
"Scalable Visualization of Semantic Nets using Power-Law Graphs,"
Applied Mathematics & Information Sciences: Vol. 08:
1, Article 45.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol08/iss1/45