Mini-ME Matchmaker and Reasoner for the Semantic Web of Things

Abstract

The Semantic Web of Things (SWoT) aims to support smart semantics-enabled applications and services in pervasive contexts. Due to architectural and performance issues, most Semantic Web reasoners are often impractical to be ported: they are resource consuming and are basically designed for standard inference tasks on large ontologies. On the contrary, SWoT use cases generally require quick decision support through semantic matchmaking in resource-constrained environments. This paper describes Mini-ME (the Mini Matchmaking Engine), a mobile inference engine designed from the ground up for the SWoT. It supports Semantic Web technologies and implements both standard (subsumption, satisfiability, classification) and non-standard (abduction, contraction, covering, bonus, difference) inference services for moderately expressive knowledge bases. In addition to an architectural and functional description, usage scenarios and experimental performance evaluation are presented on PC (against other popular Semantic Web reasoners), smartphone and embedded single-board computer testbeds.

Osmotic Cloud-Edge Intelligence for IoT-Based Cyber-Physical Systems

“…Future work concerns: Exploiting knowledge representation and reasoning in the orchestrator to dynamically discover the best deployment configuration via context-aware semantic matchmaking [ 28 ] between ontology-based annotations of microservices and devices. Investigation of more advanced IoT-oriented AI algorithms, by enhancing machine learning with semantic technologies [ 29 ] and computational argumentation.…”

“…Exploiting knowledge representation and reasoning in the orchestrator to dynamically discover the best deployment configuration via context-aware semantic matchmaking [ 28 ] between ontology-based annotations of microservices and devices.…”