CONSERT: Applying semantic web technologies to context modeling in ambient intelligence

Abstract : Representation and reasoning about context information is a main research area in Ambient Intelligence (AmI). Context modeling in such applications is facing openness and heterogeneity. To tackle such problems, we argue that usage of semantic web technologies is a promising direction. We introduce CONSERT, an approach for context meta-modeling offering a consistent and uniform means for working with domain knowledge, as well as constraints and meta-properties thereof. We provide a formalization of the model and detail its innovative implementation using techniques from the semantic web community such as ontology modeling and SPARQL. A stepwise example of modeling a commonly encountered AmI scenario showcases the expressiveness of our approach. Finally, the architecture of the representation and reasoning engine for CONSERT is presented and evaluated in terms of performance.
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https://hal-emse.ccsd.cnrs.fr/emse-01139804
Contributor : Florent Breuil <>
Submitted on : Tuesday, April 7, 2015 - 10:04:25 AM
Last modification on : Tuesday, October 23, 2018 - 2:36:09 PM

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Alexandru Sorici, Gauthier Picard, Olivier Boissier, Antoine Zimmermann, Adina Florea. CONSERT: Applying semantic web technologies to context modeling in ambient intelligence. Computers and Electrical Engineering, Elsevier, 2015, 44, pp.280-306. ⟨http://www.sciencedirect.com/science/article/pii/S0045790615000993⟩. ⟨10.1016/j.compeleceng.2015.03.012⟩. ⟨emse-01139804⟩

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