Enhanced Search Method for Ontology Classification

Abstract

The Web ontology language (OWL) has become a W3C recommendation to publish and share ontologies on the semantic web. In order to derive hidden information (classification, satisfiability and realization) of OWL ontology, a number of OWL reasoners have been introduced. Most of reasoners use both top-down and bottom-up search for ontology classification. In this paper, we propose an enhanced method of optimizing the ontology classification process of ontology reasoning. One goal of this paper is to provide such a available algorithm for future implementers of ontology reasoning system. Building the optimization method that came off best into ontology reasoning system greatly enhanced its efficiency. Our work focuses on two key aspects: The first and foremost, we describe classical methods for ontology classification. As subsumption testing to classify ontology is costly, it is important to ensure that the classification process uses the smallest number of tests. Therefore, we consider enhanced method and evaluate their effect on four different types of test ontology. The result of the experiment was that the enhanced search method increases performance improvement 30% something like that compare with the classical method.