OfficeGraph: A Knowledge Graph of Office Building IoT Measurements

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.