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| Data Intelligence Empowered Innovation: An Exploration of the Innovation Assistance Framework Based on Data Intelligence Technology |
| Lu Wei1,2, Ma Yongqiang1,2, Liu Jiawei1,2, Yang Jinqing3, Cheng Qikai1,2 |
1.School of Information Management, Wuhan University, Wuhan 430072
2.Information Retrieval and Knowledge Mining Laboratory, Wuhan University, Wuhan 430072
3.School of Information Management, Central China Normal University, Wuhan 430079 |
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Abstract Large language models (LLMs), such as ChatGPT (Chat Generative Pre-trained Transformer), have shown excellent performance in text generation and human-machine dialog. Under the background of LLMs, such technologies as big data and artificial intelligence have demonstrated important practical value in empowering scientific research and innovation. Although current science and technology (S&T) information resource management and knowledge services can provide relatively accurate information and routine knowledge aggregation services for scientific research and innovation, they have not yet been deeply integrated with scientific research and innovation activities. Researchers also face challenges such as insufficient information processing capabilities and limited cognitive abilities during the scientific research and innovation process. This article analyzes the new characteristics of scientific research activities in the data intelligence era. Furthermore, an innovation assistance framework based on data intelligence technologies is proposed, and its functional positioning, service mode, and key empowerment path in the entire innovation process are analyzed and discussed in depth. In the future, with the continuous maturity and progress of big data and artificial intelligence technologies, data-intelligence-technology-enabled S&T information resource management will be further embedded in the entire process of scientific research and innovation activities. Innovative assistance services based on data intelligence technologies, such as innovative auxiliary services for literature reading, experiment design, and article-writing scenarios, can provide researchers with personalized, fine-grained knowledge and scenario-based solutions to better serve scientific research and innovation activities.
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Received: 05 April 2023
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