|
|
|
| Intelligent Information Technology: Connotations, Boundaries, and Frameworks |
| Yao Changqing1, Cheng Qikai2,3, Wang Lijun1, Liu Jiawei2,3 |
1.Institute of Scientific and Technical Information of China, Beijing 100038
2.School of Information Management, Wuhan University, Wuhan 430072
3.Institute of Intelligence and Innovation Governance, Wuhan University, Wuhan 430072 |
|
|
|
|
Abstract Information has undergone a profound transformation from traditional to intelligent models. From early intelligent retrieval to the current large-model-driven intelligent information understanding, information technologies have gradually evolved from ‘Intelligence + Information’ and ‘Intelligence for Information’ into new paradigms of ‘Intelligence as Information’ and ‘Information is Intelligence.’ This study thoroughly explores the concept, technical boundaries, and systematic construction of intelligent information technology, proposing a system architecture tailored to the digital intelligence era. This framework encompasses collaborative sensing and integration technologies for all-source scientific and technological information, cognitive understanding technologies for intelligent information, monitoring and early warning technologies for intelligent information, intelligent analysis technologies for competitive intelligence, and scientific-information-driven intelligent evidence-based decision-making technologies. This system comprehensively addresses all processes of information work, aiming to enhance information and research capabilities through system construction, support the intelligent transformation of scientific information endeavors, and contribute to China’s high-level self-reliance and strength in science and technology. The research presented in this paper not only holds significant theoretical value for the advancement of intelligent information technology but also provides a clear technical roadmap and implementation framework for practical applications.
|
|
Received: 25 November 2024
|
|
|
|
1 陆伟, 马永强, 刘家伟, 等. 数智赋能的科研创新——基于数智技术的创新辅助框架探析[J]. 情报学报, 2023, 42(9): 1009-1017.
2 陆伟, 刘家伟, 马永强, 等. ChatGPT为代表的大模型对信息资源管理的影响[J]. 图书情报知识, 2023, 40(2): 6-9, 70.
3 钱学敏. 钱学森关于复杂系统与大成智慧的理论[J]. 西安交通大学学报(社会科学版), 2004, 24(4): 51-57.
4 曾民族. 中国九十年代情报技术的展望[J]. 现代图书情报技术, 1990(1): 2-4, 11.
5 严怡民. 情报学研究现状与展望[J]. 情报学报, 1994, 13(1): 6-12.
6 陆伟, 刘寅鹏, 石湘, 等. 大模型驱动的学术文本挖掘——推理端指令策略构建及能力评测[J]. 情报学报, 2024, 43(8): 946-959.
7 赵纯厚. 情报科学与情报技术的发展[J]. 情报学报, 1989, 8(5): 393-400, 392.
8 CroftW·Bruce. 智能情报检索的探讨[J]. 贾同兴, 译. 现代图书情报技术,1991(4): 53-56.
9 邓珞华. 我国图书情报界的人工智能研究[J]. 情报学报, 1989, 8(3): 226-231.
10 李贺, 王平, 毕强. 基于数据仓库的决策支持系统结构分析[J]. 情报学报, 2000, 19(5): 442-450.
11 Tang J, Zhang J, Yao L M, et al. ArnetMiner: extraction and mining of academic social networks[C]// Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2008: 990-998.
12 李国杰. 智能化科研(AI4R): 第五科研范式[J]. 中国科学院院刊, 2024, 39(1): 1-9.
13 Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold[J]. Nature, 2021, 596(7873): 583-589.
14 Simon H A. Designing organizations for an information-rich world[M]// Computers, Communications, and the Public Interest. Baltimore: The Johns Hopkins Press, 1971: 37-72.
15 马费成, 宋恩梅, 张勤. IRM-KM范式与情报学发展研究[M]. 武汉: 武汉大学出版社, 2008: 319-364.
16 王飞跃. 情报5.0: 平行时代的平行情报体系[J]. 情报学报, 2015, 34(6): 563-574.
17 陈云霁, 郭崎. AI for Technology: 技术智能在高技术领域的应用实践与未来展望[J]. 中国科学院院刊, 2024, 39(1): 34-40.
18 赵志耘. 论复杂信息环境下的科技情报卓智赋能[J]. 情报学报, 2022, 41(12): 1229-1237.
19 赵志耘, 曾文. 复杂信息环境下科技情报理论体系构建问题研究[J]. 情报学报, 2022, 41(6): 549-557.
20 唐星龙, 张昱, 曾文. 复杂信息环境下科技情报技术基础的体系建设研究[J]. 情报学报, 2024, 43(7): 761-772. 责任编辑 王海燕） |
|
|
|
