An ontology supported hybrid approach for recommendation in emergency situations
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An ontology supported hybrid approach for recommendation in emergency situations Sonia Mehla 1 & Sarika Jain 1 Received: 17 June 2019 / Accepted: 22 June 2020 # Institut Mines-Télécom and Springer Nature Switzerland AG 2020
Abstract Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. The proposed approaches for both the knowledge representation structure and the inference algorithm have proved to improve the accuracy of recommendation in emergencies. The results show that our hybrid system approach is efficient in decision support. The ontology-supported hybrid reasoning approach is also further validated using subjective evaluation. Keywords Decision support system . Ontology . Rule based reasoning . Case based reasoning . Emergency situations . Hybrid reasoning
1 Introduction Recently, an increasing number of natural disasters, such as tsunamis, earthquakes, floods, aviation accidents, and epidemics, have posed very significant challenges not only to the population at large but also to agencies and public services responsible for managing disaster responses. Disasters are unexpected crises that cause destruction and damage to human life and infrastructures. Some disasters can be tracked and predicted weeks in advance, such as hurricanes, and some of these disasters even occur in specific seasons, such as monsoons, tornados, and blizzards, although some of them sometimes occur without warning [1]. These crises affect a major percentage
* Sonia Mehla [email protected] Sarika Jain [email protected] 1
Department of Computer Applications, National Institute of Technology, Kurukshetra, Haryana, India
of cities by disrupting all daily activities of society, such as city services, economic activities, community services, communication systems, and public health facilities. Machine learning techniques can be used in the early prediction of a disaster, which might help disaster management teams to make rapid decisions. However, because of the increasing threat o
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