Abstract:
Semantic Web Visualization is a very important area of research because of
its application in multiple domains such as analyzing topology of large and
complex networks, semantic nets and more recently for ontologies. Ontology,
as a formal explicit description of concepts in a domain of discourse, is one
of the pillars in the Semantic Web. It is very important to visualize the
semantic nets in order to understand their structure in a more understandable
approach by the user. There is a growing need of e ective visualizations
required during the design as well as maintenance phase of ontology. Several
two-and three-dimensional visualization tools have been implemented and
discussed in research literature. Di erent layouts and algorithmic approaches
are followed in these tools. A tool may generate a drawing that makes it
hard for user to understand the structure of a large semantic net. On the
contrary, the representation may overlook the holistic view and thus losses
the semantics in the ne grained associations between important concepts
if the visualization focuses on the overview. A scalable solution is required
to facilitate e ective visualization of very large semantic nets that could
pave the way for a clearer understanding of the semantics and structure
of the semantic nets. This thesis focus on developing a scalable solution
to very large semantic net visualization problem and also to optimize the
performance by applying ltering and clustering approaches on large scale
graphs in order to get aesthetic visualization layout of semantic nets.
