Event data are often dirty owing to various recording conventions or simply system errors. These errors may cause many serious damages to real applications, such as inaccurate provenance answers, poor profiling results or concealing interesting patterns from event data. Cleaning dirty event data is strongly demanded. While existing event data cleaning techniques view event logs as sequences, structural information do exist among events. We argue that such structural information enhances not only the accuracy of repairing inconsistent events but also the computation efficiency. It is notable that both the structure and the names (labeling) of events could be inconsistent. In real applications, while unsound structure is not repaired automatically (which needs manual effort from business actors to handle the structure error), it is highly desirable to repair the inconsistent event names introduced by recording mistakes. In this paper, we propose a graph repair approach for 1) detecting unsound structure, and 2) repairing inconsistent event name.