Correcting errors in a data set is a critical issue. This task can be either handmade by experts, or by crowdsourcing methods or automatically done using algorithms. Although even if the rate of errors present in a given lexical network is rather low, it is important to reduce it. We present here automatic methods for detecting potential secondary errors that would result from automatic inference mechanisms when they rely on an initial error manually detected. Encouraging results also invite us to consider strategies that would automatically detect "erroneous" initial relations, which could lead to the automatic detection of the majority of errors in a lexical-semantic network.