Emergency Cases Ontology-Based Uncertain Similarity Matching

In decision-making process for emergency response, there are a lot of uncertain information. It is very difficult to make an effective decision based merely on existing experiences. This becomes a key issue to the development of emergency response strategy. This paper presents an ontology-based context matching algorithm (OCMA) for emergency decision-making based on historical cases. We use rough set upper and lower approximation and principle of similarity relation to cope with uncertain information. Combining with case similarity and calculation of weight, both matching of single-value and multi-value context variables are taken into consideration. With this approach, we solve the problem of uncertain similarity matching which the traditional case matching can't deal with very effectively.