The use of unconventional methods of artificial intelligence is the modern trend in the computer support of the solution of the decision-making methods. These methods are based on the use of knowledge of skilled professionals – experts, where this knowledge forms the basis for their high-quality knowledge mental models. Chapter One introduces the expert systems used for the simulation of the decision-making activity of experts when dealing with complex tasks. In terms of theory, the expert knowledge method is used. The introduced expert systems are able to effectively use uncertainties which take their source from inaccurate, incomplete, inconsistent input data, vague concepts of linguistic formulations of the rules, and uncertain knowledge. Chapter Two proposes a solution for heterogeneous data source integration in the information standard formats, based on Rule Based Expert System (RBES) to implement a metadata mining process. Later, it describes the process of automatic modelling in which the proposed RBES support in the data mining technique applications, based on the results of metadata mining process. Finally, it describes the application issues of the proposed solution in real cases. Chapter Three presents ARISTON, which is an integrated mathematical framework with all relevant parameters that constitute a fully automated, structured expert psychometric system for occupational guidance, aiming to identify and retrieve the professions which are nearest to the personality of an individual, while at the same time quantify all nearest neighbouring professions.