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M.Tech / M.E / PhD Thesis | Computer Science & Engineering | India | Volume 3 Issue 5, May 2014

Weight-based Ontology Pruning using Analysis of Inference Engines for Semantic Web

Kavita D. Pandya | Chirag Pandya

Abstract: Semantic Web relies heavily on the conventional Ontologies that represent underlying concepts and data for the purpose of comprehensive machine understanding using structural representation. Thus the success of Semantic web strongly depends on the quality of ontologies. The Proliferation of ontologies for semantic web demands easy and fast access of it to the users. Thus quick access to quality ontologies becomes prominent. In order to provide such ontologies this paper describes a new and efficient way of pruning down the ontologies. Here pruning deals with removing less desirable data from different ontologies. This paper tends to focus on two related areas namely analyzing ontologies using different Reasoners and then reducing the complexity of ontologies based on analysis result. The complexity reduction is carried out using weight assignment to different relations using which system can itself decide whether to eliminate the particular relation or not. Our goal is to provide semantic web with quality ontologies by removing multiple less sensible relationships in the ontology.

Keywords: Semantic web, Ontology, Reasoners, Ontology pruning, relationships

Edition: Volume 3 Issue 5, May 2014,

Pages: 1623 - 1627