An ontology-aided, natural language-based approach for multi-constraint BIM model querying

• URL: http://arxiv.org/abs/2303.15116v1
• Date: Mon, 27 Mar 2023 11:35:40 GMT
• Title: An ontology-aided, natural language-based approach for multi-constraint BIM model querying
• Authors: Mengtian Yin, Llewellyn Tang, Chris Webster, Shen Xu, Xiongyi Li, Huaquan Ying
• Abstract summary: This paper presents a novel ontology-aided semantic to automatically map natural language queries (NLQs) that contain different constraints into computer-readable codes for querying complex BIM models. A case study about the design-checking of a real-world residential building demonstrates the practical value of the proposed approach in the construction industry.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Being able to efficiently retrieve the required building information is critical for construction project stakeholders to carry out their engineering and management activities. Natural language interface (NLI) systems are emerging as a time and cost-effective way to query Building Information Models (BIMs). However, the existing methods cannot logically combine different constraints to perform fine-grained queries, dampening the usability of natural language (NL)-based BIM queries. This paper presents a novel ontology-aided semantic parser to automatically map natural language queries (NLQs) that contain different attribute and relational constraints into computer-readable codes for querying complex BIM models. First, a modular ontology was developed to represent NL expressions of Industry Foundation Classes (IFC) concepts and relationships, and was then populated with entities from target BIM models to assimilate project-specific information. Hereafter, the ontology-aided semantic parser progressively extracts concepts, relationships, and value restrictions from NLQs to fully identify constraint conditions, resulting in standard SPARQL queries with reasoning rules to successfully retrieve IFC-based BIM models. The approach was evaluated based on 225 NLQs collected from BIM users, with a 91% accuracy rate. Finally, a case study about the design-checking of a real-world residential building demonstrates the practical value of the proposed approach in the construction industry.

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