We argue that, with the growth of process mining in breadth (variety of covered tasks) and depth (sophistication of the considered pro- cess models), event logs need to be augmented by commonsense knowl- edge to provide a better input for process mining algorithms. This is crucial to infer key facts that are not explicitly recorded in the logs, but are necessary in a variety of tasks, such as understanding the event data, assessing their compliance and quality, identifying outliers and clusters, computing statistics, and discovering decisions, ultimately empowering process mining as a whole.