Affiliation:
1. Department of Real Estate and Construction The University of Hong Kong Hong Kong China
2. Faculty of Architecture The University of Hong Kong Hong Kong China
3. Faculty of Civil and Environmental Engineering Technion‐Israel Institute of Technology Haifa Israel
Abstract
AbstractElement property is a crucial aspect of building information modeling (BIM) for almost all BIM‐based engineering tasks. Since there are limited properties predefined in Industry Foundation Classes (IFC) specifications, a vast number of property concepts were customized and stored in BIM models, which lack labor‐intensive data modeling and alignment for effective information management and reuse. To tackle the challenge, this study presents a natural language understanding (NLU)‐based method for the automatic ontological knowledge modeling of project‐specific property concepts from BIM models. A soft pattern matching model was used to acquire contextual definitions of concepts from a domain corpus before applying deep NLU models to transform the concept names and definitions into dense vector representations. These outputs were then fed into two stacking ensemble learning models to carry out two tasks: (a) classifying whether an unseen concept overlaps with the IFC ontology, and (b) aligning the repetitive concepts with the most relevant concepts in the ontology. Finally, all fresh properties were appended to an IFC ontology, either as new objects or new synonyms. The performance was evaluated based on 327 property concepts from real‐life BIM models. The results show that the proposed approach incorporating reading comprehension of definitions outperforms the existing name similarity‐based methods. Finally, a case study on a renovation project demonstrates the effectiveness of this study in automatic ontology modeling of property concepts.
Subject
Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering,Building and Construction
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献