Abstract: We present an infrastructure for probabilistic reasoning with ontologies based on our Markov logic engine RockIt. Markov logic is a template language that combines first-order logic with log-linear graphical models. We show how to translate OWL-EL as well as RDF schema to Markov logic and how to use RockIt for applying MAP inference on the given set of formulas. The resulting system is an infrastructure for log linear logics that can be used for probabilistic reasoning with both extended OWL-EL and RDF schema. We describe our system and illustrate its benefits by presenting experimental results for two types of applications. These are ontology matching and knowledge base verification, with a special focus on temporal reasoning. Moreover, we illustrate two further use cases which are Activity Recognition and Root Cause Analysis. Our infrastructure has been applied to these use cases in the context of a cooperation with industry partners. The experiments indicate that our system, which is based on a well-founded probabilistic semantics, is capable of solving relevant problems as good as or better than state of the art systems that have specifically been designed for the respective problem. Moreover, the heterogeneity of the presented uses cases illustrates the wide applicability of our infrastructure.