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. In a third experiment we study the effects
of modularization of global knowledge with respect to local reasoning.