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The discovery of suitable web services for a given task is one of the major operations in SOA architecture, and researches are being done to automate this step. For the large amount of available Web services that can be expected in real-world settings, the computational costs of automated discovery based on semantic matchmaking become important. To make a discovery engine a reliable software component, we must aim at minimizing both the mean and the variance of the duration of the discovery task. For this, we present an extension for discovery engines in SWS environments that exploit structural knowledge and previous discovery results for reducing the search space of consequent discovery operations. Our prototype implementation shows significant improvements when applied to the Stanford SWS Challenge scenario and dataset.