Development and validation of a disaster management metamodel (DMM)

Disaster Management (DM) is a diffused area of knowledge. It has many complex features interconnecting the physical and the social views of the world. Many international and national bodies create knowledge models to allow knowledge sharing and effective DM activities. But these are often narrow in focus and deal with specified disaster types. We analyze thirty such models to uncover that many DM activities are actually common even when the events vary. We then create a unified view of DM in the form of a metamodel. We apply a metamodelling process to ensure that this metamodel is complete and consistent. We validate it and present a representational layer to unify and share knowledge as well as combine and match different DM activities according to different disaster situations

An approach to the development of commonsense knowledge modeling systems for disaster management

Knowledge is the fundamental resource that allows us to function intelligently. Similarly, organizations typically use different types of knowledge to enhance their performance. Commonsense knowledge that is not well formalized modeling is the key to disaster management in the process of information gathering into a formalized way. Modeling commonsense knowledge is crucial for classifying and presenting of unstructured knowledge. This paper suggests an approach to achieving this objective, by proposing a three-phase knowledge modeling approach. At the initial stage commonsense knowledge is converted into a questionnaire. Removing dependencies among the questions are modeled using principal component analysis. Classification of the knowledge is processed through fuzzy logic module, which is constructed on the basis of principal components. Further explanations for classified knowledge are derived by expert system technology. We have implemented the system using FLEX expert system shell, SPSS, XML, and VB. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy