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Knowledge representation and granular computing is an active area of current research for their potential application to many real life problems. Thus, it is challenging for human being in converting huge data into knowledge, and to use this knowledge to make informed decisions properly. It is very difficult to extract expert knowledge from the universe and is an active area of research in artificial intelligence. This involves analysis of how to accurately and effectively use a set of symbols to represent a set of facts within a knowledge domain. The focus of the work in this book is a combination of theoretical advancements of some of the extended models and their applications in knowledge bases. The theoretical advancements have been supported with formal proof to establish soundness whereas the applications are mostly undertaken from real life situations. The book discusses some aspects of rough sets approach in the study of knowledge discovery in databases and granular computing. An elaborate bibliography is provided at the end of the book. The material in this book includes concepts, figures, graphs, and tables to guide researchers in the area of knowledge representation.
