Knowledge Representation and Validation in a Decision Support System: Introducing a Variability Modelling Technique

Abstract

Knowledge has become the main value driver for modern organizations and has been described as a critical competitive asset for organizations. An important feature in the development and application of knowledge-based systems is the knowledge representation techniques used. A successful knowledge representation technique provides a means for expressing knowledge as well as facilitating the inference processes in both human and machines [19]. The limitation of symbolic knowledge representation has led to the study of more effective models for knowledge representation [17]. Malhotra [14] defines the challenges of the information-sharing culture of the future knowledge management systems as the integration of decision-making and actions across inter-enterprise boundaries. This means a decision making process will undergo different constraints. Therefore, existence of a method to validate a Decision Support System (DSS) system is highly recommended. In the third generation of knowledge management, the knowledge representation acts as boundary objects around which knowledge processes can be organized [26]. Knowledge is viewed in a constructionist and pragmatic perspective and a good knowledge is something that allows flexible and effective thinking and construction of knowledge-based artifacts [26]. This paper answers the two questions of [26] and [14] in the context of a DSS: 1) how to define and represent knowledge objects and 2) how to validate a DSS. For any decision, there are many choices that the decision maker can select from [7]. The process of selection takes place at a decision point and the selected decision is a choice. For example, if someone wants to pay for something, and the payment mode is either by cash or by credit card, the payment mode is the decision point; cash and credit card are choices. Now, we can conclude that the choices and decision points represent the knowledge objects in DSS. Choices, decision points and the constraint dependency rules between these two are collectively named as variability. Task variability is defined in [5] as the number of exceptions encountered in the characteristics of the work. The study in [5] tested the importance of variability in the system satisfaction. Although there are many existing approaches for representing knowledge DSS, the design and implementation of a good and useful method that considers variability in DSS is much desired.