A Generic Model of Self-Incrementing Knowledge-Based Decision Support System Using the Bolzmann Machine

Knowledge-driven decision support systems (DSS) rely significantly on the currency of it’s back-end knowledge-base for improved quality of support that it can provide to the decision making process. In this paper, a model has been developed and presented to support this purpose i.e. making the knowledge base of a knowledge-driven DSS as self-incrementing. First, the necessity of such a model for maintaining knowledge currency and the importance of knowledge currency in the context of DSS supporting operational decision processes, are discussed. Then, a generic model is presented using customer-email as input knowledge sources, frames as a knowledge representation scheme, and Bolzmann machine as the seif-incrementing mechanism. The model can be further extended or fine-tuned both in terms of the input options and process options. The input options can include other types of knowledge inputs with varying degrees of structuredness. The process options may include other algorithms and machine learning processes.