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.

翻译：互操作性仍然是建筑行业面临的挑战。本研究提出一种语义增强方法，从纯建筑对象几何构建BIM知识图谱，并展示其在支持BIM互操作性方面的潜力。该方法融合机器学习与基于规则的分类技术，用于对象分类、关系判定（如承载关系和邻接关系）及属性计算。增强后的结果被整合为BIM图谱。通过案例研究验证了该方法在促进不同版本BIM建模软件Autodesk Revit间互操作性的效果：首先从Revit 2023导出某公寓建筑模型的纯对象几何数据，依次输入所开发工具生成BIM图谱；随后从该图谱提取关键信息，在Revit 2022中重建建筑模型。经检验，重建模型与原始模型完全一致。该实验结果证明从对象几何生成BIM图谱并用于支持互操作性的可行性。