Associative context mining for ontology-driven hidden knowledge discovery

The modern society has been developing new paradigms in diverse fields through IT convergence based on information technique development. In the field of construction/transportation, such IT convergence has been attracting attention as a new generation technology for disaster prevention and management. Researches on disaster prevention and management are continuously being performed. However, the development of safety technology and simulation for prediction and prevention is comparatively slow. For the new generation IT convergence to efficiently secure safety and manage disaster prevention, it is more important than anything else to construct systematic disaster prevention system and information technology. In this study, we suggested the associative context mining for ontology-driven hidden knowledge discovery. Such method reasons potential new knowledge information through the association rule mining in the ontology-driven context modeling, a preexisting research, and uses the semantic reasoning engine to create and apply rules to the context simulation. The ontology knowledge base consists of internal, external, and service context information such as user profile, weather index, industry index, location information, environment information, and comprehensive disaster situation. Apriori mining algorithm of the association rule is applied to reason the potential relationship among internal, external, and service context information and discovers and applies hidden knowledge to the semantic reasoning engine. The accuracy and validity are verified through evaluating the performance of the developed ontology-driven associative context simulation. Such developed simulation is expected contribute to enhancing public safety and quality of life through determining potential risk involved in disaster prevention and quick response.