Abstract
This position paper explores applying the Data, Information, Knowledge, Wisdom (DIKW) framework to Digital Twins of buildings and their energy systems. The DIKW framework provides a basis for applying hybrid artificial intelligence (AI) by illustrating where symbolic elements, such as knowledge graphs and sub-symbolic, ML-based approaches, are combined to represent the behavior of complex systems to support decision-making. Several established processes and technologies (BIM, BMS, EMS) are also used in the built environment that often operate in isolation and would benefit from modularization and interconnectivity. Adherence to the DIKW could provide a basis for evaluating interoperability by identifying the data requirements and outputs of each process and technology that work with the data.
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Acknowledgements
A special thanks to Dr Emanuele Laurenzi and Wolfram Willuhn for their productive collaboration in building the knowledge graph and developing applications.
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Allan, J. et al. (2024). A Hierarchical Knowledge Framework for Digital Twins of Buildings and Their Energy Systems (Position Paper). In: Almeida, J.P.A., Di Ciccio, C., Kalloniatis, C. (eds) Advanced Information Systems Engineering Workshops. CAiSE 2024. Lecture Notes in Business Information Processing, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-031-61003-5_5
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DOI: https://doi.org/10.1007/978-3-031-61003-5_5
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