  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.
