  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.
