Smart Ontology-Based Event Identification

Identifying an event and all its attributes help in timely response to emergencies or business decisions. Although accurate event identification has been studied in the last decade, fewer thoughts have been put into determining actions with context-dependent effects. This paper is motivated by the desire to develop a synergy between the different answers on the same query posed by users of differing priority. The proposed approach exploits semantic technologies to model the personalized behavior. We provide a control protocol that recognizes the pattern in the flow of precision as the priority of user changes. The control protocol has been utilized to define the priority of the user and is exploited in an efficient algorithm to yield good tradeoffs between various attributes of the decision. Both bottom-up and top-down parsing of the ontological knowledge base is depicted depending on whether the event object is available in the knowledge base or not. The algorithm is then tested on the real-world use case of events of terrorist attacks. The algorithm renders varying answer with varying precision based on a balance between the available resources, the required certainty, the required specificity level, and the acceptable threshold value. The proposed control protocol and the algorithm proved to be logically sound and seem to be a direct consequence of representing knowledge in a manner that is complete.