Inclusion dependencies together with functional dependencies form the most important data dependencies used in practice. Inclusion dependencies are important for various database applications such as database design and maintenance, semantic query optimization and efficient view maintenance of data warehouse. Existing approaches for discovering inclusion dependencies consist in producing the whole set of inclusion dependencies holding in a database, leaving the task of selecting the interesting ones to an expert user. In this paper we take another look at the problem of discovering inclusion dependencies. We exploit the logical navigation, inherently available in relational databases through workloads of SQL statements, as a guess to automatically find out only interesting inclusion dependencies. This assumption leads us to devise a tractable algorithm for discovering interesting inclusion dependencies. Within this framework, approximate dependencies, i.e. inclusion dependencies which almost hold, are also considered. As an example, we present a novel application, namely self-tuning the logical database design, where the discovered inclusion dependencies can be used effectively.