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.

翻译：高效检索所需建筑信息对于建设项目相关方开展工程与管理活动至关重要。自然语言界面系统正成为一种经济高效地查询建筑信息模型的方法。然而，现有方法无法逻辑组合不同约束条件进行精细查询，削弱了基于自然语言的BIM查询的实用性。本文提出一种新颖的本体辅助语义解析器，可将包含不同属性与关系约束的自然语言查询自动映射为计算机可读代码，用于查询复杂BIM模型。首先，开发模块化本体以表达工业基础类概念与关系的自然语言表述，并通过目标BIM模型中的实体对其进行填充，以整合项目特定信息。随后，本体辅助语义解析器逐步从自然语言查询中提取概念、关系与值约束，完整识别约束条件，最终生成包含推理规则的标准SPARQL查询，成功检索基于IFC的BIM模型。基于从BIM用户收集的225个自然语言查询进行评估，该方法达到91%的准确率。最后，通过真实住宅建筑的设计校核案例，证明了该方法在建筑行业的实际应用价值。