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Reports (Research Report) Year : 2015

Dynamic knowledge model evolution in SWoT: a way to improve services selection relevancy over time

Abstract

Semantic web technologies are gaining momentum in the WoT (Web of Things) community for its promising 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 of the approaches is validated on two real use-cases.
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hal-01168411 , version 1 (25-06-2015)

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  • HAL Id : hal-01168411 , version 1

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Gérald Rocher, Jean-Yves Tigli, Stéphane Lavirotte, Rahma Daikhi. Dynamic knowledge model evolution in SWoT: a way to improve services selection relevancy over time. [Research Report] Universite de Nice Sophia-Antipolis (UNS); CNRS. 2015. ⟨hal-01168411⟩
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