Knowledge Propagation in Contextualized Knowledge Repositories: an
  Experimental Evaluation
  As the interest in the representation of context dependent knowledge in the
Semantic Web has been recognized, a number of logic based solutions have been
proposed in this regard. In our recent works, in response to this need, we
presented the description logic-based Contextualized Knowledge Repository (CKR)
framework. CKR is not only a theoretical framework, but it has been effectively
implemented over state-of-the-art tools for the management of Semantic Web
data: inference inside and across contexts has been realized in the form of
forward SPARQL-based rules over different RDF named graphs. In this paper we
present the first evaluation results for such CKR implementation. In
particular, in first experiment we study its scalability with respect to
different reasoning regimes. In a second experiment we analyze the effects of
knowledge propagation on the computation of inferences.
