Semantic Enrichment of Association Rules Discovered in Operational Building Data

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.