Run-time knowledge model enrichment in SWoT: A step toward ambient services selection relevancy

Gérald Rocher 1, * Jean-Yves Tigli 1 Stéphane Lavirotte 1 Rahma Daikhi 2
* Auteur correspondant
1 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe RAINBOW
SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : Semantic web technologies are gaining momentum in the WoT (Web of Things) community for its ability to manage the increasing semantic heterogeneity between devices (Semantic Web of Things, SWoT) in ambient environments. However, most of the approaches rely on ad-hoc and static knowledge models (ontologies) designed for specific domains and applications. While it is a solution for handling the semantic heterogeneity issue, it offers no perspective in term of ontology evolution over time. We study in this paper several approaches allowing: (1) to handle the semantic heterogeneity issue; (2) to capitalize the knowledge contributions throughout the life of the system allowing it to potentially better assist people in their environment over time. One approach is validated on two real use-cases.
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Communication dans un congrès
5th International Conference on the Internet of Things (IoT 2015), Oct 2015, Séoul, South Korea. Proceedings of the 2015 5th International Conference on the Internet of Things (IOT), pp.62-68, 2015, 〈10.1109/IOT.2015.7356549〉
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Gérald Rocher, Jean-Yves Tigli, Stéphane Lavirotte, Rahma Daikhi. Run-time knowledge model enrichment in SWoT: A step toward ambient services selection relevancy. 5th International Conference on the Internet of Things (IoT 2015), Oct 2015, Séoul, South Korea. Proceedings of the 2015 5th International Conference on the Internet of Things (IOT), pp.62-68, 2015, 〈10.1109/IOT.2015.7356549〉. 〈hal-01297403〉

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