Model Driven Ontology: A New Methodology for Ontology Development

Semantic technology is becoming a preferable alternative for enterprise-wide applications intertwined with interoperable information sharing, due to the distributed nature of this technology. Ontology is the cornerstone of semantic technology; therefore, a major challenge for the project team is to build a complete and consistent ontology data model that represents the correct business domain. Effective collaboration among customer and team members is essential for the creation of the correct ontology model. Equally necessary is a mechanism to automatically transform this model into ontology script. Within today’s leading organizations using semantic technology, a significant factor in business success rests solely in the hands of the ontologists. It is they alone who are responsible for building the correct ontology data model. Having no other members on the project team capable of verifying and validating the created ontologies may put the entire business at risk. This paper describes a new methodology, “Model Driven Ontology,” in which using a standard modeling activity as a key process for building ontology would effectively and efficiently enhance collaborations between different parties of the project team. This would lead to a consistent ontology model validated and approved by all members of the project team (business experts, intel analysts, DB admins, architects, ontologists, etc.). Model Driven Ontology uses a UML object model artifact as a starting point to build an ontology data model. This model as a common ground for all team members is then systematically transformed to a formal ontology, facilitating the development of enterprise-wide information exchange and sharing, which can be uniformly developed, centrally maintained, and efficiently reused [6]. This would lead to more efficient and inexpensive information sharing between different information systems, cost effective development and deployment of information systems, and better quality decision making as a result of more timely, accurate, and complete information.