A natural-language-based approach to intelligent data retrieval and representation for cloud BIM
Jia-Rui Lin, Zhen-Zhong Hu, Jian-Ping Zhang, Fang-Qiang Yu

TL;DR
This paper presents a natural language processing-based method for intelligent data retrieval and representation in cloud BIM systems, improving accessibility and usability of large BIM datasets during construction projects.
Contribution
It introduces a novel approach combining natural language processing, data retrieval strategies, and IFC schema mapping for efficient BIM data management and user interaction.
Findings
Prototype validated on Kunming Airport project data
Enhanced data retrieval efficiency and user interaction
Improved BIM data understanding and summarization
Abstract
As the information from diverse disciplines continues to integrate during the whole life cycle of an Architecture, Engineering, and Construction (AEC) project, the BIM (Building Information Model/Modeling) becomes increasingly large. This condition will cause users difficulty in acquiring the information they truly desire on a mobile device with limited space for interaction. To improve the value of the big data of BIM, an approach to intelligent data retrieval and representation for cloud BIM applications based on natural language processing was proposed. First, strategies for data storage and query acceleration based on the popular cloud-based database were explored to handle the large amount of BIM data. Then, the concepts keyword and constraint were proposed to capture the key objects and their specifications in a natural-language-based sentence that expresses the requirements of…
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