MODELS OF KNOWLEDGE BASE DESIGN

Abstract

This article explores different models of knowledge base design, which are crucial for the development of intelligent systems. The models discussed include rule-based systems, semantic networks, frame-based systems, Bayesian networks, and case-based reasoning. Each model has its own strengths and limitations, and the choice of model depends on the specific requirements of the intelligent system being developed. Rule-based systems are easy to understand and implement but can be limited in their ability to handle complex or uncertain situations. Semantic networks and frame-based systems are useful for representing complex or structured knowledge domains but can be time-consuming to create and maintain. Bayesian networks are useful for handling uncertain or incomplete information but require expertise in probability theory and statistics. Case-based reasoning is useful for handling complex or uncertain situations but requires a large and diverse knowledge base. Overall, knowledge base design is a crucial process that directly influences the system's ability to make accurate decisions and recommendations.