Abstract
In order to support the global energy transition, smart building management provides opportunities to increase efficiency and comfort. In practice, real-world smart buildings make use of combinations of heterogeneous IoT devices, and a need for (knowledge graph-enabled) interoperability solutions has been established. While ontologies and synthetic datasets are available, a real-world, large scale and diverse knowledge graph has so far not been available. In this paper, we present OfficeGraph, a knowledge graph expressed in the saref ontology containing over 14 million sensor measurements from 444 heterogeneous devices, collected over a period of 11 months, in a seven story office building. We describe the procedure of mapping original sensor measurements to rdf and how links to external linked data are established. We describe the resulting knowledge graph consisting of 90 Million rdf triples, and its structural and semantic features. Several use cases are shown of the knowledge graph: a) through various realistic data analysis use cases based on competencies identified by building managers and b) through an existing machine learning experiment where we replace the original dataset with OfficeGraph.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In order to avoid confusion between rdf properties and the property being measured, we will specifically refer to the former as rdf properties, and address the latter as properties.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
OPSD results for the value prediction task were not part of the experiments described in [17], but are presented here to compare results.
References
Compton, M., et al.: The SSN ontology of the W3C semantic sensor network incubator group. J. Web Semant. 17, 25–32 (2012)
Dadkhah, S., Mahdikhani, H., Danso, P.K., Zohourian, A., Truong, K.A., Ghorbani, A.A.: Towards the development of a realistic multidimensional IoT profiling dataset. In: 2022 19th Annual International Conference on Privacy, Security & Trust (PST), pp. 1–11 (2022)
Daniele, L., den Hartog, F., Roes, J.: Created in close interaction with the industry: the smart appliances REFerence (SAREF) ontology. In: Cuel, R., Young, R. (eds.) FOMI 2015. LNBIP, vol. 225, pp. 100–112. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21545-7_9
Duan, S., Kementsietsidis, A., Srinivas, K., Udrea, O.: Apples and oranges: a comparison of RDF benchmarks and real RDF datasets. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp. 145–156 (2011)
Heo, S., Song, S., Kim, B., Kim, H.: Sharing-aware data acquisition scheduling for multiple rules in the IoT. In: 2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 43–55. IEEE (2020)
Iglesias, E., Jozashoori, S., Vidal, M.E.: Scaling up knowledge graph creation to large and heterogeneous data sources. J. Web Semant. 75, 100755 (2023)
Jafarpur, P., Berardi, U.: Effects of climate changes on building energy demand and thermal comfort in Canadian office buildings adopting different temperature setpoints. J. Build. Eng. 42, 102725 (2021)
Moreira, J., et al.: Towards IoT platforms’ integration semantic translations between W3C SSN and ETSI SAREF. In: SEMANTICS Workshops (2017)
Open Power System Data: Data Package Household Data. Version 2020-04-15 (2020). https://data.open-power-system-data.org/household_data/2020-04-15/
Rafsanjani, H.N., Ghahramani, A.: Towards utilizing internet of things (IoT) devices for understanding individual occupants’ energy usage of personal and shared appliances in office buildings. J. Build. Eng. 27, 100948 (2020)
Ren, J., Dubois, D.J., Choffnes, D., Mandalari, A.M., Kolcun, R., Haddadi, H.: Information exposure from consumer IoT devices: a multidimensional, network-informed measurement approach. In: Proceedings of the Internet Measurement Conference, pp. 267–279 (2019)
Rijgersberg, H., Van Assem, M., Top, J.: Ontology of units of measure and related concepts. Semant. Web 4(1), 3–13 (2013)
Ristoski, P., Rosati, J., Di Noia, T., De Leone, R., Paulheim, H.: RDF2Vec: RDF graph embeddings and their applications. Semant. Web 10(4), 721–752 (2019)
Arz von Straussenburg, A.F., Blazevic, M., Riehle, D.M.: Measuring the actual office workspace utilization in a desk sharing environment based on IoT sensors. In: Gerber, A., Baskerville, R. (eds.) DESRIST 2023. LNCS, vol. 13873, pp. 69–83. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-32808-4_5
W3C: Web of Things (WoT) Thing Description (2020). https://www.w3.org/TR/2020/REC-wot-thing-description-20200409/
van der Weerdt, R., de Boer, V., Daniele, L., Nouwt, B., Siebes, R.: Making heterogeneous smart home data interoperable with the SAREF ontology. Int. J. Metadata Semant. Ontol. 15(4), 280–293 (2021)
van der Weerdt, R., de Boer, V., Daniele, L., Siebes, R., van Harmelen, F.: Evaluating the effect of semantic enrichment on entity embeddings of IoT knowledge graphs. In: Proceedings of the 1st International Workshop on Semantic Web on Constrained Things at ESWC 2023, vol. 3412 (2023)
Wielemaker, J., Beek, W., Hildebrand, M., Van Ossenbruggen, J.: ClioPatria: a SWI-prolog infrastructure for the semantic web. Semant. Web 7(5), 529–541 (2016)
Acknowledgements
This work is part of the InterConnect project (interconnectproject.eu/) which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 857237.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
van der Weerdt, R., de Boer, V., Siebes, R., Groenewold, R., van Harmelen, F. (2024). OfficeGraph: A Knowledge Graph of Office Building IoT Measurements. In: Meroño Peñuela, A., et al. The Semantic Web. ESWC 2024. Lecture Notes in Computer Science, vol 14665. Springer, Cham. https://doi.org/10.1007/978-3-031-60635-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-60635-9_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-60634-2
Online ISBN: 978-3-031-60635-9
eBook Packages: Computer ScienceComputer Science (R0)