Exploring graph neural networks for semantic enrichment: Room type classification

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(1) pioneers the application of GNNs for semantic enrichment and opens the door to other possible applications.

Automated summary

This paper introduces a novel approach to semantic enrichment of BIM models by representing models as graphs and applying graph neural networks (GNNs). The experiments demonstrate the feasibility of this approach and show that the developed GNN algorithm achieved higher accuracy and more balanced prediction compared to other machine learning algorithms. The application of GNNs in semantic enrichment opens up possibilities for other potential applications.