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.

中文翻译：

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本体支持的混合方法在紧急情况下推荐

大规模灾难对所有政府组织和公众构成了重大的应对挑战。在应对工作中通常会遇到一些困难，这使得当局必须及时做出关键决策，以减轻灾难性或紧急情况并从中恢复过来。本文中，我们将基于案例的推理和基于规则的推理与决策支持的实施支持相集成，从而提供了一个本体支持的混合推理模型，以使决策者能够在紧急情况下有效地做出响应。我们还介绍了一种新的分层组织的语义知识表示模型，以表示基于案例的结构，该结构将基于案例的推理增强为知识密集型基于案例的推理。此外，我们从决策支持系统的效率方面对所提出的方法进行分析获得了实验结果。因此，使用知识表示的混合模型来合并两种方法的优点似乎是合理的。模型输出提供了在发生紧急情况时要部署的资源数量的估计。事实证明，针对知识表示结构和推理算法的建议方法可以提高紧急情况下推荐的准确性。结果表明，我们的混合系统方法在决策支持方面是有效的。使用主观评估还可以进一步验证支持本体的混合推理方法。模型输出提供了在发生紧急情况时要部署的资源数量的估计。事实证明，针对知识表示结构和推理算法的建议方法可以提高紧急情况下推荐的准确性。结果表明，我们的混合系统方法在决策支持方面是有效的。使用主观评估还可以进一步验证支持本体的混合推理方法。模型输出提供了在发生紧急情况时要部署的资源数量的估计。事实证明，针对知识表示结构和推理算法的建议方法可以提高紧急情况下推荐的准确性。结果表明，我们的混合系统方法在决策支持方面是有效的。使用主观评估还可以进一步验证支持本体的混合推理方法。