Knowledge representation is the field of Artificial Intelligence concerned with how to formally capture, organize, and store knowledge to enable reasoning and decision-making by machines. It involves creating structures and methods to encode information in a format that computers can understand and manipulate. Common approaches include logic-based representations like semantic networks, frames, and ontologies, as well as probabilistic methods such as Bayesian networks and Markov models. The goal is to bridge the gap between human knowledge and machine processing capabilities, facilitating tasks like natural language understanding, problem-solving, and intelligent decision-making. Effective knowledge representation is crucial for building robust Artificial Intelligence systems capable of learning, adapting, and reasoning in complex environments. This book brings forth some of the most innovative concepts and elucidates the unexplored aspects of knowledge representation. It presents the complex subject of knowledge representation in the most comprehensible and easy to understand language. Scientists and students actively engaged in this field will find this book full of crucial and unexplored concepts.