# A natural-language-based approach to intelligent data retrieval and representation for cloud BIM

arXiv: https://arxiv.org/abs/2411.09951 · HTML: https://mlsanity.com/paper/2411.09951/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2024.

## Built on by (most cited first, 4 of 4)

- [LLM-assisted Graph-RAG Information Extraction from IFC Data](https://mlsanity.com/paper/2504.16813/) (2025)
- [BIGNet: Pretrained Graph Neural Network for Embedding Semantic, Spatial, and Topological Data in BIM Models](https://mlsanity.com/paper/2509.11104/) (2025)
- [Domain-Specific Fine-Tuning and Prompt-Based Learning: A Comparative Study for developing Natural Language-Based BIM Information Retrieval Systems](https://mlsanity.com/paper/2508.05676/) (2025)
- [BIM Information Extraction Through LLM-based Adaptive Exploration](https://mlsanity.com/paper/2605.01698/) (2026)

## Abstract (arXiv)

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 the user. Keywords and constraints can be mapped to IFC entities or properties through the International Framework for Dictionaries (IFD). The relationship between the user's requirement and the IFC-based data model was established by path finding in a graph generated from the IFC schema, enabling data retrieval and analysis. Finally, the analyzed and summarized results of BIM data were represented based on the structure of the retrieved data. A prototype application was developed to validate the proposed approach on the data collected during the construction of the terminal of Kunming Airport, the largest single building in China. With this approach, users can significantly benefit from requesting for information and the value of BIM will be enhanced.