Data inconsistency and data mismatch are critical problems that limit data interoperability and hinder smooth operation of a distributed business. An ontology represents a semantic model that explicitly describes various entities and their properties of a domain of discourse and acts as a vehicle for seamless data integration and exchange. The existing methodologies for ontology development fail to provide a comprehensive coverage for different steps, e.g. pre-development, development and post-development, which are necessary for successfully developing ontologies. We propose a generic and comprehensive methodology that puts ontology engineering on a firm scientific foundation and at the same time provides a collaborative environment for effective knowledge sharing and reuse. Furthermore, our approach also provides a way for automatically extracting frequent terms from the data to construct an ontology in a bottom-up fashion. The performance of our methodology has been evaluated by developing different ontologies to solve the real life applications, e.g. fault diagnosis and root cause investigation and spare parts maintenance.
