@article{Zhang2025, 
author = {Mengxia Zhang},
title = {Building an Industry-Level Underground Space Knowledge Database Using Large Scale Language Model},
year = {2025},
journal = {Chinese Journal of Underground Space and Engineering},
volume = {21},
number = {1},
pages = {16-22},
keywords = {underground space, knowledge database, large scale language model},
url = {https://www.sciopen.com/article/10.20174/j.JUSE.2025.01.02},
doi = {10.20174/j.JUSE.2025.01.02},
abstract = {The intelligent construction of an underground space knowledge base will become an important foundation for promoting scientific planning of urban underground space. This paper proposes building an industry-level underground space knowledge base based on large language models to enable intelligent management and innovative applications of underground space expertise. Through text data collection, manual pre-annotation, and ChatGLM model fine-tuning, a knowledge base system oriented towards industry applications are constructed. Test results show that building an industry knowledge base can customize underground space corpora, enhance ChatGLM models, and achieve more accurate professional question answering. This research lays the foundation for building a high-quality knowledge innovation platform for the underground space industry, which will help improve the scientificity and work efficiency of underground space planning and design, and promote the rational utilization and sustainable development of urban underground space.}
}