| Autor: |
Kurteva A; Semantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria.; Industrial Design Engineering, Delft University of Technology, 2628 CE Delft, The Netherlands., Chhetri TR; Semantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria., Tauqeer A; Semantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria.; Consumption and Healthy Lifestyles Chair Group, Wageningen University & Research, 6706 KN Wageningen, The Netherlands., Hilscher R; Semantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria.; RTI International, Research Triangle Park, NC 27709, USA., Fensel A; Semantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria.; Consumption and Healthy Lifestyles Chair Group, Wageningen University & Research, 6706 KN Wageningen, The Netherlands.; Wageningen Data Competence Center, Wageningen University & Research, 6708 PB Wageningen, The Netherlands., Nagorny K; Institut für Angewandte Systemtechnik Bremen GmbH (ATB), 28359 Bremen, Germany., Correia A; Institut für Angewandte Systemtechnik Bremen GmbH (ATB), 28359 Bremen, Germany., Zilverberg A; Institut für Angewandte Systemtechnik Bremen GmbH (ATB), 28359 Bremen, Germany., Schestakov S; L3S Research Center, Leibniz University Hannover, 30167 Hannover, Germany., Funke T; L3S Research Center, Leibniz University Hannover, 30167 Hannover, Germany., Demidova E; Data Science and Intelligent Systems Group (DSIS), University of Bonn, 53115 Bonn, Germany. |
| Abstrakt: |
The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case. |
