@article{scholars7770,
            year = {2016},
           pages = {11988--11993},
       publisher = {Asian Research Publishing Network},
         journal = {ARPN Journal of Engineering and Applied Sciences},
          volume = {11},
            note = {cited By 0},
          number = {20},
           title = {Ontology based decision support knowledge acquisition module},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994238863&partnerID=40&md5=f66102e17bf533995c424bf9739eef6a},
        abstract = {Data integration issues have been part of the challenges faced by the oil and gas industries. This is also compounded by the fact that different kind of modeling tools has been used that are complex for decision makers thereby further making it difficult to adopt other tools. The consequence of this is sufficient previous records are not efficiently inculcated into the system for a better decision analysis during the decision making processes. Also, failures due to this insufficient analysis usually lead to huge capital costs that far surpass the equipment procurement cost. Ontology based knowledge acquisition system tool can help mitigate these issues. Expert input is used for part of data collection. Ontology knowledge model is developed which helps to build taxonomy of objects of interest in the domain to serve as a knowledge base. One of the advantage of the ontology knowledge model is its linguistic properties which is close to human semantics to help bridge the issue of technicality barrier to help decision makers understand the underlying information in the system. Web ontology language application programming interface (OWL-API) together with java codes are used to serialize between the ontology knowledge base and the graphical user interface developed in java to automatically add and update the knowledge in the database to make assessment based on the newly added data and the existing data. The tool is capable of adding new data and making appropriate inference. The tool will serve as a basis for decision makers who are usually not technical inclined to make effective decisions that will yield improved and profitable productions. {\^A}{\copyright} 2006-2016 Asian Research Publishing Network (ARPN).},
          author = {Shobowale, K. O. and Hashim, F. M. and Hussin, H.},
            issn = {18196608}
}