Knowledge Graphs’ Ontologies and Applications for Energy Efficiency in Buildings: A Review

Abstract

The Architecture, Engineering and Construction (AEC) industry has been utilizing Decision Support Systems (DSSs) for a long time to support energy efficiency improvements in the different phases of a building’s life cycle. In this context, there has been a need for a proper means of exchanging and managing of different kinds of data (e.g., geospatial data, sensor data, 2D/3D models data, material data, schedules, regulatory, financial data) by different kinds of stakeholders and end users, i.e., planners, architects, engineers, property owners and managers. DSSs are used to support various processes inherent in the various building life cycle phases including planning, design, construction, operation and maintenance, retrofitting and demolishing. Such tools are in some cases based on established technologies such Building Information Models, Big Data analysis and other more advanced approaches, including Internet of Things applications and semantic web technologies. In this framework, semantic web technologies form the basis of a new technological paradigm, known as the knowledge graphs (KG), which is a powerful technique concerning the structured semantic representation of the elements of a building and their relationships, offering significant benefits for data exploitation in creating new knowledge. In this paper, a review of the main ontologies and applications that support the development of DSSs and decision making in the different phases of a building’s life cycle is conducted. Our aim is to present a thorough analysis of the state of the art and advancements in the field, to explore key constituents and methodologies, to highlight critical aspects and characteristics, to elaborate on critical thinking and considerations, and to evaluate potential impact of KG applications towards the decision-making processes associated with the energy transition in the built environment.