Advertisement

The RealEstateCore Ontology

  • Karl HammarEmail author
  • Erik Oskar Wallin
  • Per Karlberg
  • David Hälleberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11779)

Abstract

Recent developments in data analysis and machine learning support novel data-driven operations optimizations in the real estate industry, enabling new services, improved well-being for tenants, and reduced environmental footprints. The real estate industry is, however, fragmented in terms of systems and data formats. This paper introduces RealEstateCore (REC), an OWL 2 ontology which enables data integration for smart buildings. REC is developed by a consortium including some of the largest real estate companies in northern Europe. It is available under the permissive MIT license, is developed and hosted at GitHub, and is seeing adoption among both its creator companies and other product and service companies in the Nordic real estate market. We present and discuss the ontology’s development drivers and process, its structure, deployments within several companies, and the organization and plan for maintaining and evolving REC in the future.

Resource Type: Ontology

IRI: https://w3id.org/rec/full/3.0/

DOI:  https://doi.org/10.5281/zenodo.2628367

Keywords

Ontology Smart Buildings Building automation IoT Energy optimization Space analytics 

References

  1. 1.
    Balaji, B., et al.: Brick: towards a unified metadata schema for buildings. In: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, pp. 41–50. ACM (2016)Google Scholar
  2. 2.
    Bazjanac, V., Crawley, D.: Industry foundation classes and interoperable commercial software in support of design of energy-efficient buildings. In: Proceedings of Building Simulation 1999, vol. 2, pp. 661–667 (1999)Google Scholar
  3. 3.
    Bazjanac, V., Crawley, D.B.: The implementation of industry foundation classes in simulation tools for the building industry. Technical report, Lawrence Berkeley National Laboratory (1997)Google Scholar
  4. 4.
    Beetz, J., Van Leeuwen, J., De Vries, B.: Ifcowl: a case of transforming express schemas into ontologies. Ai Edam 23(1), 89–101 (2009)Google Scholar
  5. 5.
    Blomqvist, E., Hammar, K., Presutti, V.: Engineering ontologies with patterns - the eXtreme design methodology. In: Hitzler, P., Gangemi, A., Janowicz, K., Krisnadhi, A., Presutti, V. (eds.) Ontology Engineering with Ontology Design Patterns: Foundations and Applications, pp. 23–50. IOS Press, Amsterdam (2016)Google Scholar
  6. 6.
    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_9CrossRefGoogle Scholar
  7. 7.
    Evans, R., Gao, J.: DeepMind AI reduces energy used for cooling Google data centers by 40%, July 2016. https://web.archive.org/web/20190322212318/blog.google/outreach-initiatives/environment/deepmind-ai-reduces-energy-used-for/. Accessed 26 Mar 2019
  8. 8.
    Froese, T., et al.: Industry foundation classes for project management-a trial implementation. Electron. J. Inf. Technol. Constr. 4, 17–36 (1999)Google Scholar
  9. 9.
    Garijo, D.: WIDOCO: a wizard for documenting ontologies. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 94–102. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68204-4_9CrossRefGoogle Scholar
  10. 10.
    Haller, A., et al.: The modular SSN ontology: a joint W3C and OGC standard specifying the semantics of sensors, observations, sampling, and actuation. Semantic Web 10(1), 9–32 (2019)CrossRefGoogle Scholar
  11. 11.
    Hammar, K.: Content Ontology Design Patterns: Qualities, Methods, and Tools, vol. 1879. Linköping University Electronic Press, Oslo (2017)Google Scholar
  12. 12.
    Janowicz, K., Haller, A., Cox, S.J., Le Phuoc, D., Lefrançois, M.: SOSA: a lightweight ontology for sensors, observations, samples, and actuators. J. Web Semantics 56, 1–10 (2019) CrossRefGoogle Scholar
  13. 13.
    Klein, M.C., Fensel, D.: Ontology versioning on the semantic web. In: SWWS, pp. 75–91 (2001)Google Scholar
  14. 14.
    Lohmann, S., Negru, S., Haag, F., Ertl, T.: Visualizing ontologies with VOWL. Semantic Web 7(4), 399–419 (2016)CrossRefGoogle Scholar
  15. 15.
    Preston-Werner, T.: Semantic versioning 2.0.0. https://web.archive.org/web/20190321081743/semver.org/spec/v2.0.0.html. Accessed 26 Mar 2019
  16. 16.
    Wiens, V., Lohmann, S., Auer, S.: WebVOWL editor: device-independent visual ontology modeling. In: Proceedings of the ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks. CEUR Workshop Proceedings, vol. 2180. CEUR-WS.org (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and InformaticsJönköping UniversityJönköpingSweden
  2. 2.Idun Real Estate Solutions ABStockholmSweden
  3. 3.Akademiska Hus ABStockholmSweden

Personalised recommendations