Knowledge discovery in databases (KDD) is a process to find previously unknown or unrecognized and potentially useful knowledge from structured data stored in databases. The relevancy problem in KDD is how to select the knowledge that is relevant to a given KDD task from a large body of domain knowledge that may contain knowledge irrelevant to the task. Relevant reasoning based on strong relevant logic can be used to solve this relevancy problem. We propose a general method to integrate domain knowledge bases into the KDD process. It is based on the relevant reasoning and simulates the human way of thinking when one faces a new or old situation. We give an algorithm to create new relevant features from the domain knowledge bases where knowledge is represented in the form of production rules