Two-stage Text-to-BIMQL semantic parsing for building information model extraction using graph neural networks

Abstract

With the increasing complexity of the building process, it is difficult for project stakeholders to retrieve large and multi-disciplinary building information models (BIMs). A natural language interface (NLI) is beneficial for users to query BIM models using natural language. However, parsing natural language queries (NLQs) is challenging due to ambiguous name descriptions and intricate relationships between entities. To address these issues, this study proposes a graph neural network (GNN)-based semantic parsing method that automatically maps NLQs into executable queries. Firstly, ambiguous mentions are collectively linked to referent ontological entities via a GNN-based entity linking model. Secondly, the logical forms of NLQs are interpreted through a GNN-based relation extraction model, which predicts links between mentioned entities in a heterogeneous graph fusing ontology and NLQ texts. The experiment based on 786 queries shows its outstanding performance. Moreover, a real-world case verifies the practicability of the proposed method for BIM model retrieval.