Degrees of Information Relevance in Situation Assessment

Information overload is a big challenge that the decision makers need to overcome while assessing and sharing information about the situations they are dealing with. In our previous papers, we developed an information relevance reasoning method to support the human decision makers to identify the relevant information that needs to be processed. However, the amount of the relevant information identified by this method still may grow fast when large volumes of information are continuously added to the knowledge base. The collected information selected by this method would include redundant facts, which are relevant to a situation, but not necessary for understanding the situation. In order to identify only the information that is necessary for characterizing a specific situation, we need to refine the definition of relevant information. In this paper, we first extend the conceptual framework for the identification of information relevance developed in our previous papers by considering degrees of relevance. Based on the definition of weak relevance and strong relevance, we develop relevant information simplification algorithms to remove the redundant facts from the weakly relevant facts. We evaluate our method in a medical cyber security scenario. The results show that our relevant information simplification method reduces the size of the information for characterizing a specific situation and the amount of time that is needed to infer answers to queries related to the situation. We also verify that by removing the redundant relevant information we can get the same answers to the queries as if using the whole knowledge base.