Knowledge Graphs for Building Automation Systems

Ontotext attempts to respond to the growing need of the building automation industry, which is facing a demand for intelligent data integration to manage and use data from controllers, sensors, and devices. To do this, it uses “knowledge graph” (KG) which has a proven track record of offering a sustainable solution for harnessing and making sense of heterogeneous data. 

The knowledge graph represents a collection of interlinked descriptions of entities – real-world objects, events, situations or abstract concepts – where: 

    • Descriptions have a formal structure that allows both people and computers to process them in an efficient and unambiguous manner; 
    • Entity descriptions contribute to one another, forming a network where each entity represents part of the description of the entities related to it. 

Knowledge graph technologies that have been on the rise and maturing as an enterprise solution for the last few years, allow seamless data integration and easy data management. They also offer the means for meeting the current challenges while tapping into the potential of future opportunities for even smarter building automation systems.  

The modeling and interlinking of the data involved in the process of building a knowledge graph helps to ingest heterogeneous data and reap the benefits of a data-centric approach to building automation.   

In other words, the better the linking of the physical and digital layers of the devices and their data, the smarter the thinking of the building is.  

Putting standardized semantic data in context, knowledge graphs enhance building automation processes by enabling: 

    • an integrated and automated approach to analysis of assets and facility equipment maintenance management;  
    • rich dashboards with multiple views of the data;  
    • analytical tools fed with data from different systems;  
    • monitoring and management of physical objects through their digital twins;  
    • integration with other business systems;  
    • scalable architecture;  
    • predictive maintenance on the installed equipment. 

Modeling, normalization and data integration is the way forward to harvesting the data streams building automation systems produce. By using knowledge graphs, these systems are enriched with an abstracted level, a sort of a digital twin, of each and every controller, space and event in a building. This enables one to have multiple views on the data, navigate data sensors and devices, query them effortlessly, perform diagnostics from a single access point, practice preventive maintenance and more. 

All in all, reaping the benefits of informed, sustainable strategies for the future of the building automation industry and the smarter day-to-day functioning of our buildings looks easier and more efficient with a knowledge graph.