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Comparisons of different approaches to statistical relational learning are difficult due to the variety of the available concepts and due to the absense of a common interface. The main objective of the KReator toolbox introduced here is to provide a common methodology for modelling, learning, and inference in a relational probabilistic framework. As a second major contribution of this paper, we present the RME approach to relational probabilistic reasoning which applies the principle of maximum entropy to groundings of a relational knowledge base and which is also supported by KReator.