论文标题
build2vec：矢量空间中的建筑代表
Build2Vec: Building Representation in Vector Space
论文作者
论文摘要
在本文中，我们代表了图形嵌入算法的方法，该方法用于转换从建筑信息模型（BIM）获得的标记属性图。工业基金会类（IFC）是BIM的标准架构，该模式可将建筑物数据转换为图表。我们使用Node2Vec具有偏见的随机步道来提取不同建筑组件之间的语义相似性，并在多维矢量空间中表示它们。案例研究实施是在新加坡国立大学（SDE4）的零净能量建筑上进行的。这种方法显示了在捕获不同建筑物对象的语义关系和相似性时，更具体地说，是空间和时空数据的有希望的机器学习应用。
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.
