Identifying Research Hotspots and Research Gaps in Specific Research Area Based on Fine-Grained Information Extraction via Large Language Models

Yuling Sun, Yuling Sun, Xuening Cui, Aning Qin, Jiatang Luo

2025

Abstract

This paper constructs a fine-grained scientific data indicator framework using LLMs to conduct knowledge mining in a specific field of natural science and technology, with empirical analysis carried out in the domain of carbon dioxide conversion and utilization technology. Firstly, based on the characteristics of the technical field, we systematically established four key scientific data dimensions: products, technologies, materials, and performance. Subsequently, six key scientific data indicators were selected to characterize these dimensions. Finally, the extracted scientific data were employed to analyse research hotspots and gaps in the field. This approach effectively addresses the inherent limitations of traditional technology topic analysis, such as overly coarse metric granularity and the lack of quantitative features. Moreover, since these scientific data dimensions and indicators are generalizable to natural science and technology fields aimed at product development, the proposed methodology demonstrates broad applicability.

Paper Citation

in Harvard Style

Sun Y., Cui X., Qin A. and Luo J. (2025). Identifying Research Hotspots and Research Gaps in Specific Research Area Based on Fine-Grained Information Extraction via Large Language Models. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 473-480. DOI: 10.5220/0013824900004000

in Bibtex Style

@conference{kdir25,
author={Yuling Sun and Xuening Cui and Aning Qin and Jiatang Luo},
title={Identifying Research Hotspots and Research Gaps in Specific Research Area Based on Fine-Grained Information Extraction via Large Language Models},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={473-480},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013824900004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Identifying Research Hotspots and Research Gaps in Specific Research Area Based on Fine-Grained Information Extraction via Large Language Models
SN -
AU - Sun Y.
AU - Cui X.
AU - Qin A.
AU - Luo J.
PY - 2025
SP - 473
EP - 480
DO - 10.5220/0013824900004000
PB - SciTePress