Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services

There is a critical need to develop a mobile-based emergency response system (MERS) to help reduce risks in emergency situations. Existing systems only provide short message service (SMS) notifications, and the decision support is weak, especially in man-made disaster situations. This paper presents a MERS ontology-supported case-based reasoning (OS-CBR) method, with implementation, to support emergency decision makers to effectively respond to emergencies. The advantages of the OS-CBR approach is that it builds a case retrieving process, which provides a more convenient system for decision support based on knowledge from, and solutions provided for past disaster events. The OS-CBR approach includes a set of algorithms that have been successfully implemented in four components: data acquisition; ontology; knowledge base; and reasoning; as a sub-system of the MERS framework. A set of experiments and case studies validated the OS-CBR approach and application, and demonstrate its efficiency.

► Developed a mobile-based emergency response system to handle disaster situations
► Developed an extraction intelligent aggregation algorithm to fuse emergency information
► Developed a case retrieval algorithm and a case adaptation algorithm using fuzzy sets
► Developed an OS-CBR approach to support emergency decision making
► Implemented the OS-CBR as a subsystem of the MERS framework