Generic conversational agents often use hard-coded stimulus-response data to generate responses, for which little to no effort is attributed to effectively understand and comprehend the input. The limitation of these types of systems is obvious: the general and linguistic knowledge of the system is limited to what the developer of the system explicitly defined. Therefore, a system which analyses user input at a deeper level of abstraction which backs its knowledge with common sense information will essentially result in a system that is capable of providing more adequate responses which in turn result in a better overall user experience.