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

Tracking #: 2483-3697

This paper is currently under review
Authors: 
Simona Colucci
Francesco Maria Donini
Eugenio Di Sciascio

Responsible editor: 
Guilin Qi

Submission type: 
Survey Article
Abstract: 
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 RFDS rules, provide or not explanation facilities. For classification purpose, we propose a maturity model made up by 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. We did not consider efficiency from our evaluation 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 implementers of RDFS-based Semantic Web applications, for choosing a suitable reasoning engine, or to decide at what level an in-sourced reasoning service could be implemented and documented.
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Under Review