Contextual Fuzzy-Based Decision Support System Through Opinion Analysis: A Case Study at University of the Salerno

Abstract

According to the literature about customer satisfaction and loyalty, it is possible to define knowledge-based system to support management decision-making in the organizations. Nevertheless, the problem as to how much the context impacts on correlation has not been investigated in the literature. This paper focuses on developing of Decision Support System (DSS) taking into account correlations among statistical factors, i.e., expert knowledge, and customers’ opinions depending on several contextual features, e.g., culture, location, in order to build context-sensitive simulation environment. The proposed work defines a general system design workflow to tailor knowledge-based DSS by using a fuzzy model to quantify correlations among variables in a given context. We explore ontologies to represent correlations among statistical factors, e.g., Calculative Commitment, Quality of Service. We apply fuzzy data analysis techniques to train fuzzy classifier on the customer’s opinions collected by survey. Finally, synergistic usage of Description Logic and Fuzzy Theory allows the implementation of a simulation environment that supports the management team to tune business strategies. The framework has been instantiated for a case study to support public administration at the University of the Salerno.