Comprehensive real-time event information is critical to policymakers during emergency response and decision-making process. However, the development process of the emergent events has great uncertainty, and situational evolutions of emergencies are often difficult to use the fixed reasoning mode to attain. For this reason, this paper proposes a new method based on the ontology cluster for the evolution reasoning of emergency scenarios and extends the sematic web rule language to realize the scenario deduction, which can apply the Bayesian network to perform the conditional probability reasoning. A counterpart modeling and modifying of the Bayesian network optimization process is introduced. Besides, the probabilistic interpretation rules of atom components in context evolution are described with detailed query examples of emergency situation deducting and reasoning. The experimental results show that this approach is efficient in describing and capable of calculating the occurrence possibilities of the emergent events. INDEX TERMS Emergent events, event scenario deduction, ontology cluster, Bayesian network, SWRL.

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In order to deal with some real-world problems, the uncertainty reasoning for Semantic Web has been widely studied, though lots of researchers tend to combine fuzzy theory with Description Logic Programs (DLP) and Semantic Web Rule Language (SWRL). Since probability theory is more suitable than fuzzy logic to make prediction about event from a state of partial knowledge, a probabilistic extension of SWRL, named Bayes-SWRL, is introduced in this paper. Based on the syntax and model-theoretic semantic defined for Bayes-SWRL, we propose a probabilistic reasoning algorithm, which is employed to implement the prototype reasoner of Bayes-SWRL. In addition, we point out some constrains of Bayes-SWRL that users should pay attention to.

To acquire the basic information about Emergent events is essential for government agencies as well as individuals to get well informed or to participate in relevant disasters relief activities. This paper presents a software interface model for intelligently retrieving emergent events on the internet through a Meta search engine interface. Particularly, we propose an H-T-E (Hazard, Trigger and Event) query expansion approach to get high relevance of ever updating event series, which expands the original query keywords by flexibly combining the event trigger words and hazard words together with the event theme terms. We build our interface in java based on the open source project Carrot2.The experimental results show that the proposed method can bring about increased accuracy and extra features not supplied by commercial search engines.

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