  Semantic Web knowledge representation standards, and in particular RDF and
OWL, often come endowed with a formal semantics which is considered to be of
fundamental importance for the field. Reasoning, i.e., the drawing of logical
inferences from knowledge expressed in such standards, is traditionally based
on logical deductive methods and algorithms which can be proven to be sound and
complete and terminating, i.e. correct in a very strong sense. For various
reasons, though, in particular, the scalability issues arising from the
ever-increasing amounts of Semantic Web data available and the inability of
deductive algorithms to deal with noise in the data, it has been argued that
alternative means of reasoning should be investigated which bear high promise
for high scalability and better robustness. From this perspective, deductive
algorithms can be considered the gold standard regarding correctness against
which alternative methods need to be tested. In this paper, we show that it is
possible to train a Deep Learning system on RDF knowledge graphs, such that it
is able to perform reasoning over new RDF knowledge graphs, with high precision
and recall compared to the deductive gold standard.
