Knowledge representation in expert systems is a fundamental aspect that enables the system to mimic human decision-making and problem-solving capabilities. The main goal of knowledge representation is to store knowledge in a form that the system can process efficiently and reason about effectively. Various methods are employed to achieve this, each with its strengths and limitations depending on the type of problem and domain of application. One of the most widely used methods is rule-based representation. In this approach, knowledge is encoded as a set of “if-then” rules, also known as production rules. Each rule consists of a condition (the “if” part) and an action or conclusion (the “then” part). For example, in a medical expert system, a rule could be: “If the patient has a high fever and sore throat, then suggest testing for strep throat.” Rule-based systems are intuitive and easy to understand, allowing experts to directly translate their knowledge into rules. The reasoning process typically involves forward chaining (data-driven) or backward chaining (goal-driven) to reach conclusions. Another method is semantic networks, where knowledge is represented using a graph structure. Nodes in the network represent concepts, objects, or entities, while edges represent