Event logs are helpful to figure out what is happening in a system or to diagnose the causes that led to an unexpected crash or security issue. Unfortunately, their growing sizes and lacks of abstraction make them difficult to interpret, especially when a system integrates several communicating components. This paper proposes to learn models of communicating systems, e.g., Web service compositions, distributed applications, or IoT systems, from their event logs in order to help engineers understand how they are functioning and diagnose them. Our approach, called CkTail, generates one Input Output Labelled Transition System (IOLTS) for every component participating in the communications and dependency graphs illustrating another viewpoint of the system architecture. Compared to other model learning approaches, CkTail improves the precision of the generated models by better recognising sessions in event logs. Experimental results obtained from 9 case studies show the effectiveness of CkTail to recover accurate and general models along with component dependency graphs.