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.

中文翻译：

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Build2Vec：在向量空间中构建表示

在本文中，我们展示了一种图嵌入算法的方法，该算法用于转换从建筑信息模型 (BIM) 获得的标记属性图。工业基础类 (IFC) 是 BIM 的标准模式，用于将建筑数据转换为图形表示。我们使用带有偏置随机游走的 node2Vec 来提取不同建筑组件之间的语义相似性，并将它们表示在多维向量空间中。在位于新加坡国立大学 (SDE4) 的净零能耗建筑上进行了案例研究实施。这种方法在捕捉不同建筑对象的语义关系和相似性方面显示出有前景的机器学习应用，更具体地说，是空间和时空数据。