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Time-Aware Knowledge Graphs for Decision Making in the Building Industry

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 348))

Abstract

The phenomenal progress in the development of the Internet of Things (IoT) has already had tremendous impact on almost all industrial sectors and, finally, on our everyday life. The ongoing total digital transformation leads to entirely new time-aware services and creates new and more pro-active business opportunities.

Knowledge graphs are becoming more and more popular in different domains, and also for the integration of sensor networks. In this project we make knowledge graphs time-aware through a set of general temporal properties relevant for the integration of sensing networks. Time-aware knowledge graphs enable us to do time series analysis, find temporal dependencies between events, and implement time-aware applications. The requirements for the temporal properties derive from a use case of residential real estate, with the aim to enable the occupants to interact with their houses.

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Acknowledgement

The K-Project Dependable, secure and time-aware sensor networks (DeSSnet) is funded within the context of COMET – Competence Centers for Excellent Technologies by the Austrian Ministry for Transport, Innovation and Technology (BMVIT), the Federal Ministry for Digital and Economic Affairs (BMDW), and the federal states of Styria and Carinthia. The program is conducted by the Austrian Research Promotion Agency (FFG). The authors are grateful to the institutions funding the DeSSnet project and wish to thank all project partners for their contributions.

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Correspondence to Herwig Zeiner .

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Zeiner, H., Weiss, W., Unterberger, R., Maurer, D., Jöbstl, R. (2019). Time-Aware Knowledge Graphs for Decision Making in the Building Industry. In: Freitas, P., Dargam, F., Moreno, J. (eds) Decision Support Systems IX: Main Developments and Future Trends. EmC-ICDSST 2019. Lecture Notes in Business Information Processing, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-030-18819-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-18819-1_5

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  • Online ISBN: 978-3-030-18819-1

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