Extraction of Margin Results Using Feedback Learning Techniques & Ontologies
As the sheer volume of new knowledge increases, there is a need to find effective ways to convey and correlate emerging knowledge in machine-readable form. The success of the semantic web hinges on the ability to formalize distributed knowledge in terms of a varied set of ontologies. The authors present Pan-Onto-Eval, a comprehensive approach to evaluating an ontology by considering its structure, semantics, and domain. They provide formal definitions of the individual metrics that constitute Pan-Onto-Eval and synthesize them into an integrated metric. They illustrate its effectiveness by presenting an example based on multiple ontologies for a University.