Mining temporal knowledge has many applications. Such knowledge can be all the more interesting as some time constraints between events can be integrated during the mining task. Both in data mining and machine learning, some methods have been proposed to extract and manage such knowledge using temporal constraints. In particular, some work has been done to mine generalized sequential patterns. However, such constraints are often too crisp or need a very precise assessment to avoid erroneous information. Within this context, we propose an approach based on sequence graphs derived from soft temporal constraints. These relaxed constraints enable us to find more generalized sequential patterns. We also propose a temporal accuracy measure to provide the user with a tool for analysing the numerous extracted patterns.