@inproceedings{
wang2024decoding,
title={Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScope{GPT}},
author={Siqi Wang and Chao Liang and Yunfan Gao and Liu Yang and Jing Li and Haofen Wang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=WO65vvadrY}
}
Industrial parks are critical to urban economic growth, blending technology and urban life to foster innovation. Yet, their development often faces challenges due to imbalances between industrial needs and urban services, necessitating strategic planning and operation. This paper presents IndustryScopeKG, a pioneering multi-modal, multi-level large-scale industrial park knowledge graph, and the IndustryScopeGPT framework. By leveraging vast datasets, including corporate, socio-economic, and geospatial information, IndustryScopeKG captures the intricate relationships and semantics of industrial parks, facilitating comprehensive analysis and planning. The IndustryScopeGPT framework, integrating LLMs with Monte Carlo Tree Search, enhances decision-making capabilities, enabling dynamic and adaptable responses to the diverse needs of industrial park planning and operation (IPPO) tasks. Our contributions include the release of the first open-source industrial park knowledge graph, IndustryScopeKG, and the demonstration of the IndustryScopeGPT framework's efficacy in site selection and planning tasks through the IndustryScopeQA benchmark. Our findings highlight the potential of combining LLMs with extensive datasets and innovative frameworks, setting a new standard for research and practice in the field.
Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT
Siqi Wang Chao Liang Yunfan Gao Liu Yang Jing Li Haofen Wang