In this paper, we represent a methodology of a graph embeddings algorithm that is used to transform labeled property graphs obtained from a Building Information Model (BIM). Industrial Foundation Classes (IFC) is a standard schema for BIM, which is utilized to convert the building data into a graph representation. We used node2Vec with biased random walks to extract semantic similarities between different building components and represent them in a multi-dimensional vector space. A case study implementation is conducted on a net-zero-energy building located at the National University of Singapore (SDE4). This approach shows promising machine learning applications in capturing the semantic relations and similarities of different building objects, more specifically, spatial and spatio-temporal data.
翻译:在本文中,我们代表了一种图形嵌入算法的方法,用于转换从建筑信息模型(BIM)中获得的贴标签的财产图。工业基础类(IFC)是BIM的一个标准模型,用来将建筑数据转换成图形表示。我们使用偏差随机行走的节点2Vec来提取不同建筑组成部分之间的语义相似之处,并在一个多维矢量空间中代表它们。在新加坡国立大学的一座净零能建筑(SDE4)上开展了案例研究。该方法显示,在捕捉不同建筑物体的语义关系和相似性(更具体地说,空间和时空数据)时空数据方面,机器学习应用很有希望。