Building up a knowledge base is a complex task in which theoretical knowledge needs to be integrated with practical experience. This integration can be supported by a system that can manage linking between rules, representing experts or textbook or theoretical knowledge and facts (or data), representing cases from practice. Conflicts between rules and real-world cases can have diverse causes. Case data can be noisy or inconsistent or both. We use rules to filter cases and confine noise or inconsistency. However, rules can be overly general and can classify more cases than intended. Then machine learning can be used to find additional restrictions for the rules. If, however, no significant similarities can be determined between the misclassified cases, we seek additional expert input to support effective implementation of learning tools. Using a case subset, we capture expert input and formulate a set of #x201C;enriched#x201D; cases. We then use the enriched cases to learn additional rules and to introduce practical features to validate and refine the original rule base. In this paper, we discuss the role of machine learning as a vehicle for supporting
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