  Markov Logic Networks join probabilistic modeling with first-order logic and
have been shown to integrate well with the Semantic Web foundations. While
several approaches have been devised to tackle the subproblems of rule mining,
grounding, and inference, no comprehensive workflow has been proposed so far.
In this paper, we fill this gap by introducing a framework called Mandolin,
which implements a workflow for knowledge discovery specifically on RDF
datasets. Our framework imports knowledge from referenced graphs, creates
similarity relationships among similar literals, and relies on state-of-the-art
techniques for rule mining, grounding, and inference computation. We show that
our best configuration scales well and achieves at least comparable results
with respect to other statistical-relational-learning algorithms on link
prediction.
