A multiplatform energy-aware OWL reasoner benchmarking framework

Performance evaluation is increasingly relevant for Web Ontology Language (OWL) reasoners, due to the expanding availability of knowledge corpuses on the Web, the growing variety of applications, and the rise to prominence of mobile and pervasive computing. Motivated mainly by the difficulty of comparing reasoning engines in the Semantic Web of Things (SWoT), this paper introduces evOWLuator, a novel approach and a multiplatform framework devised to be both flexible and expandable. It features integration of traditional and mobile/embedded engines as well as ontology dataset management, reasoning test execution, and report generation. A case study consisting of an experimental setting for time, memory peak and energy footprint evaluation with eight reasoners and four different platforms allows showcasing usage and validating features and usability of the tool.