Exploring graph neural networks for semantic enrichment: Room type classification

Zijian Wang, Rafael Sacks, Timson Yeung

Research output: Contribution to journalArticlepeer-review


Semantic enrichment of Building Information Modeling (BIM) models supplements models with the implicit semantics for further applications. In this paper, we use the room classification task to develop, test and illustrate a novel approach to semantic enrichment of BIM models - representation of models as graphs and application of graph neural networks (GNNs). A dedicated graph dataset consisting of 224 apartment layouts with nine room types and node/edge features was compiled. An improved GNN algorithm, SAGE-E, was developed for processing both node and edge features and a batch method was used to improve efficiency. The experiments showed that (1) The novel approach of adopting graphs and GNNs was feasible. (2) SAGE-E achieved higher accuracy (79%) and more balanced prediction (F1 = 0.79) when compared with other machine learning algorithms. (3) SAGE-E shortened the training and validation process. This work pioneers the application of GNNs for semantic enrichment and opens the door to other possible applications.

Original languageEnglish
Article number104039
JournalAutomation in Construction
StatePublished - Feb 2022


  • Building Information Modeling (BIM)
  • Classification
  • Deep learning
  • Graph neural networks (GNNs)
  • Machine learning
  • Semantic enrichment (SE)

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction


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