Krispel, UlrichUlrichKrispelEvers, Henrik LeanderHenrik LeanderEversTamke, MartinMartinTamkeUllrich, TorstenTorstenUllrich2022-03-052022-03-052017https://publica.fraunhofer.de/handle/publica/25312010.1186/s40327-017-0042-5Background: The concept of building information management (BIM) is based on its holistic nature. This idea pays off, if all relevant information is fused into one consistent data set. As a consequence, the completeness of data is vital and the research question on how to complete data automatically remains open. Methods: In this article we present a data completion technique based on knowledge management. We encode expert and domain knowledge in a generative system that represents norms and standards in a machine-readable manner. The implementation of this approach be used to automatically determine a hypothesis on the location of electrical lines within indoor range scans. Results: The generative paradigm can encode domain expert knowledge in a machine-readable way. In this article we demonstrate its usage to represent norms and standards. Conclusions: The benefit of our method is the further completion of digital building information models -- a necessary step to take full advantage of building information modeling.enshape grammarsknowledge management (KM)building information models (BIM)Guiding Theme: Smart CityGuiding Theme: Digitized WorkResearch Area: Modeling (MOD)journal article