  Interoperability remains a challenge in the construction industry. In this
study, we propose a semantic enrichment approach to construct BIM knowledge
graphs from pure building object geometries and demonstrate its potential to
support BIM interoperability. Our approach involves machine learning and
rule-based methods for object classification, relationship determination (e.g.,
hosting and adjacent) and attribute computation. The enriched results are
compiled into a BIM graph. A case study was conducted to illustrate the
approach for facilitating interoperability between different versions of the
BIM authoring software Autodesk Revit. First, pure object geometries of an
architectural apartment model were exported from Revit 2023 and fed into the
developed tools in sequence to generate a BIM graph. Then, essential
information was extracted from the graph and used to reconstruct an
architectural model in the version 2022 of Revit. Upon examination, the
reconstructed model was consistent with the original one. The success of this
experiment demonstrates the feasibility of generating a BIM graph from object
geometries and utilizing it to support interoperability.
