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Jiayao Li, Juanqiong Gou, Wenxin Mu, & Liyu Peng. (2017). Modeling of Railway Risk Inter-Relation based on the study of Accident Context. 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. 328–340). Albi, France: Iscram.
Abstract: In order to detect and control the critical potential risk source of railway more scientifically, more reasonably and more accurately in complex accident context, a knowledge modeling method of risk inter-relation is proposed based on ontology modeling of accident context. First, the mechanism of accident causation is summarized based on the accident case analysis. Then, the knowledge model of accident cause is built based on ontology theory, including the ontology model of two context instances. Last but not least, the risk inter-relation rules with different dimensions of inter-relation patterns are inferred based on the instantiation of ontology model. The two context instances are used to illustrate the identification process of risk inter-relation. The results prove the rationality of the method, which can provide a reference for the precise railway risk prevention.
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Min Zhu, Ruxue Chen, Tianye Lin, Quanyi Huang, & Guang Tian. (2019). Describing and Forecasting the Medical Resources assignments for International Disaster Medical relief Forces Using an Injury-Driven Ontology Model. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Available medical resources are the basis of efficient disaster medical relief. The medical resources assignment for disaster medical relief forces is usually fixed. However, the injury condition distribution changes in different disaster and so does the demand for the medical resources. So the assignment of medical relief forces should be more flexible and based on the injury. We analyzed the component parts and rules of disaster medical relief, defining the related concepts and rules. Then, we constructed the describing rules of injury-treatment-medical-technique-resource-assignment process. Based on these, we established the ontology of disaster medical relief system and the injury-driven medical resources assignment ontology model (MRAOM). We used the model to describe the medical relief situation after earthquake to demonstrate the model could describe complicated situations. We also used the model to describe and forecast the medical resource assignment of treating batch wounded to demonstrate the model's validity.
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Aviv Segev. (2008). Adaptive ontology use for crisis knowledge representation. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 285–293). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: While a crisis requires quick response of emergency management factors, ontology is generally represented in a static manner. Therefore, an adaptive ontology for crisis knowledge representation is needed to assist in coordinating relief efforts in different crisis situations. The paper describes a method of ontology modeling that modifies the ontology in real time during a crisis according to the crisis surroundings. An example of ontology use based on a sample Katrina crisis blog is presented.
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