Build2Vec: Building Representation in Vector Space

• URL: http://arxiv.org/abs/2007.00740v1
• Date: Wed, 1 Jul 2020 20:39:39 GMT
• Title: Build2Vec: Building Representation in Vector Space
• Authors: Mahmoud Abdelrahman, Adrian Chong, and Clayton Miller
• Abstract summary: We represent a methodology of a graph embeddings algorithm that is used to transform labeled property graphs. We used node2Vec with biased random walks to extract semantic similarities between different building components. A case study implementation is conducted on a net-zero-energy building located at the National University of Singapore.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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