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
Contributeur : Florent Breuil <>
Soumis le : mardi 7 avril 2015 - 10:04:25
Dernière modification le : jeudi 11 janvier 2018 - 06:20:35

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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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