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Knowledge discovery in databases KDD techniques have been extensively employed to extract knowledge from massive data stores to support decision making in a wide range of critical applications. Maintaining the currency of discovered knowledge over evolving data sources is a fundamental challenge faced by all KDD applications. This paper addresses the challenge from the perspective of deciding the right times to refresh knowledge. We define the knowledge-refreshing problem and model it as a Markov decision process. Based on the identified properties of the Markov decision process model, we establish that the optimal knowledge-refreshing policy is monotonically increasing in the system state within every appropriate partition of the state space. We further show that the problem of searching for the optimal knowledge-refreshing policy can be reduced to the problem of finding the optimal thresholds and propose a method for computing the optimal knowledge-refreshing policy. The effectiveness and the robustness of the computed optimal knowledge-refreshing policy are examined through extensive empirical studies addressing a real-world knowledge-refreshing problem. Our method can be applied to refresh knowledge for KDD applications that employ major data-mining models.