Grégoire Burel, Lara S. G. Piccolo, Kenny Meesters, & Harith Alani. (2017). DoRES -- A Three-tier Ontology for Modelling Crises in the Digital Age. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 834–845). Albi, France: Iscram.
Abstract: During emergency crises it is imperative to collect, organise, analyse and share critical information between individuals and humanitarian organisations. Although dierent models and platforms have been created for helping these particular issues, existing work tend to focus on only one or two of the previous matters. We propose the DoRES ontology for representing information sources, consolidating it into reports and then, representing event situation based on reports. Our approach is guided by the analysis of 1) the structure of a widely used situation awareness platform; 2) stakeholder interviews, and; 3) the structure of existing crisis datasets. Based on this, we extract 102 dierent competency questions that are then used for specifying and implementing the new three-tiers crisis model. We show that the model can successfully be used for mapping the 102 dierent competency questions to the classes, properties and relations of the implemented ontology.
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Leon J. M. Rothkrantz, & Siska Fitrianie. (2015). Bayesian Classification of Disaster Events on the Basis of Icon Messages of Observers. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: During major disaster events, human operators in a crisis center will be overloaded with under-stress a flood of phone calls. As an increasing number of people in and around big cities do not master the native language, the need for automated systems that automatically process the context and content of information about disaster situations from the communicated messages becomes apparent. To support language-independent communication and to reduce the ambiguity and multitude semantics, we developed an icon-based reporting observation system. Contrast to previous approaches of such a system, we link icon messages to disaster events without using Natural Language Processing. We developed a dedicated set of icons related to the context and characteristic features of disaster events. The developed system is able to compute the probability of the appearance of possible disaster events using Bayesian reasoning. In this paper, we present the reporting system, the developed icons, the Bayesian model, and the results of two experiments.
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