Semantic Enrichment of Association Rules Discovered in Operational Building Data for Reuse of Building Performance Patterns

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The advancements in Building Information Modelling, Building Monitoring Systems and machine learning have made the discovery of hidden insights and performance patterns in operational building data possible and highly accurate. Semantic web technologies play a fundamental role in terms of knowledge representation and provide the necessary infrastructure for reuse of the discovered insights. Such knowledge can be of particular importance to decision-making for building performance improvement, however, this requires patterns discovered with traditional data mining techniques to be attributed with semantics, so that they can be machine-interpretable and reusable. Using linked data-based crowdsourcing techniques for interpretation of building performance patterns enables the creation of knowledge graphs of building data, enriched with contextualized building performance insights. This paper presents a crowdsourcing mechanism that allows the semantic enrichment of building performance patterns through semantic annotation and classification. We discuss the results and the potential of linked building data graphs enriched with building performance insights.
Original languageEnglish
Title of host publicationProceedings of the 37th International Conference of CIB W78
Place of PublicationSao Paulo
Publication statusPublished - 19 Aug 2020
EventICCCBE/W78 Virtual Joint Conference: 37th International CIB W78 Conference - Sao Paulo, Brazil
Duration: 18 Aug 202020 Oct 2020
Conference number: 37

Publication series

NameCIB w78 Conference Series
ISSN (Electronic)2706-6568


ConferenceICCCBE/W78 Virtual Joint Conference
Abbreviated titleCIB W78
CitySao Paulo
Internet address


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