Beyond efficiency: A systematic classification of 48 RDFS-based Semantic Web reasoners and applications

In this paper, we present a systematic classification of 48 RDFS-based Semantic Web reasoners and applications, with the aim of evaluating their deductive capabilities. In fact, not all such applications show the same reasoning behavior w.r.t. the RDF data they use as information source and the ability of reasoning is not a binary quality: it can, e.g., consider or not blank nodes denotation, include different subsets of RDFS rules, provide or not explanation facilities. For classification purpose, we propose a maturity model made up of three orthogonal dimensions for the evaluation of reasoners and applications: blank nodes, deductive capabilities, and explanation of the results. For each dimension, we set up a progression from absence to full compliance. Each RDFS-based Semantic Web reasoner/application is then classified in each dimension, based on both its documentation and published articles. In our evaluation, we did not consider efficiency on purpose, since efficiency could be compared only for systems providing an equal service in every of the above dimensions. Our classification can be used by Semantic Web developers, for choosing a suitable SW system, or to decide at what level an in-sourced application could be implemented and documented, in scenarios in which the three dimensions above are crucial.