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

•We formally define an ontology-based context meta-model.•Approach offers uniform modeling of context content, annotation and dependencies.•Reasoning cycle performs automatic computation of temporal continuity of situations.•OSGi-compatible reasoning engine exploits the context meta-model in customizable way.

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.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slide