Knowledge Propagation in Contextualized Knowledge Repositories: an Experimental Evaluation

TL;DR

This paper evaluates the performance and scalability of a logic-based framework for managing context-dependent knowledge in the Semantic Web, demonstrating its effectiveness and impact on inference computation.

Contribution

It provides the first experimental evaluation of a CKR framework implemented with SPARQL rules, analyzing scalability and knowledge propagation effects.

Findings

Scalability varies with reasoning regimes.

Knowledge propagation influences inference computation.

Framework effectively manages context-dependent knowledge.

Abstract

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.