中文    English

Journal of Library and Information Science in Agriculture ›› 2021, Vol. 33 ›› Issue (1): 32-40.doi: 10.13998/j.cnki.issn1002-1248.20-0969

• Special manuscript • Previous Articles     Next Articles

A Database Construction of S&T Intelligence Cognition Models

LIU Xiwen1,2, GUO Shijie1,2   

  1. 1. Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100049;
    2. National Science Library, Chinese Academy of Sciences, Beijing 100190
  • Received:2020-10-28 Online:2021-01-05 Published:2021-02-05

Abstract: [Purpose/Significance] This paper aims to study the organization and construction methods of the intelligence cognition models database to help scientists and information analysts to have an accurate understanding of the research area within a short period of time, and assist them in identifying technological opportunities and threats. [Method/Process] The intelligence cognition models database contains various technical elements and literature information across different subjects, so it consists of literature library, algorithm library, scientific/technical knowledge library, application cases library, etc. It has functions including research hotspots identification, technical performance comparison, information analysis methods recommendation, algorithm-aided design and so on. The construction process of the database includes the collection, verification, storage, organization, and utilization of the "intelligence cognitive models", among which the verification of the models is a crucial step. [Results/Conclusions] The intelligence cognition models database is of great significance to the scientific and technological information study and it can play the role of data infrastructures and information analysis toolboxes. The construction of the database requires the cooperation of the information analysts, scientists, and information technology specialists. In the future, the maintenance, application and upgrading of the models library need to be further considered.

Key words: intelligence cognition model, models database, information research infrastructure, academic information research

CLC Number: 

  • G350
[1] TONY H, STEWART T, KRISTIN T, et al.第四范式: 数据密集型科学发现[M]. 北京: 科学出版社, 2012.
TONY H, STEWART T, KRISTIN T, et al.The Fourth Paradigm: Data-intensive Scientific Discovery[M]. Beijing: China science publishing & media ltd, 2012.
[2] 张霖. 关于数字孪生的冷思考及其背后的建模和仿真技术[J]. 系统仿真学报, 2020(4): 1-10.
ZHANG L.Cold thinking about digital twin and its modeling and simulation technology[J]. Journal of system simulation, 2020(4): 1-10.
[3] TSHITOYAN V, DAGDELEN J, WESTON L, et al.Unsupervised word embeddings capture latent knowledge from materials science literature[J]. Nature, 2019, 571(7763): 95.
[4] ROSS D K. Automating chemistry and biology using robot scienti-sts[EB/OL]. [2015-11-24]. http://ki2015.computational-logic.org/program/Keynote-Ross-King.pdf.
[5] SAID F, SAHAR V, CHRISTOPH L, et al.SEO: A scientific events data model[EB/OL]. [2019-03-24].https://www.researchgate.net/publication/334285784_SEO_A_Scientific_Events_Data_Model?channel=doi&linkId=5d224819a6fdcc2462ca8858&showFulltext=true.
[6] KOOHI-MOGHADAM M, WANG H, WANG Y, et al.Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach[J]. Nature machine intelligence, 2019, 1: 561-567.
[7] DATTILO A, VANDERBURG A, SHALLUE C J, et al.Identifying exoplanets with deep learning II: Two new Super-Earths uncovered by a neural network in k2 data[J]. The astronomical journal, 2019.
[8] WANG J, ZHANG Y, WEBBER S A H, et al. Solar flare predictive features derived from polarity inversion line masks in active regions using an unsupervised machine learning algorithm[J]. The astrophysical journal, 2020, 892(2): 140-149.
[9] PHAM B T, JAAFARI A, PRAKASH I, et al.Hybrid computational intelligence models for groundwater potential mapping[J]. Catena, 2019.
[10] 叶鹰, 武夷山. 情报学基础教程(第二版)[M]. 北京: 科学出版社, 2012.
YE Y, WU Y S.Basic course of information science (second edition)[M]. Beijing: China science publishing & media ltd, 2012.
[11] ESS-DIVE. ESS-DIVE Repository[EB/OL]. [2020-11-03]. http://ess-dive.lbl.gov/about/.
[12] MESRI. La feuille de route nationale des infrastructures de recherche[EB/OL]. [2018-05-06]. http://www.enseignementsup-recherche.gouv.fr/cid70554/la-feuille-de-route-nationale-des-infrastructures-de-recherche.html.
[13] DDBJ. Bioinformation and DDBJ center[EB/OL].[2020-11-03]. https://www.ddbj.nig.ac.jp/index-e.html.
[14] ESRFI. Roadmap 2018: Strategy report on research infrastructures[EB/OL].[2018-05-06]. http://roadmap2018.esfri.eu/media/1060/esfri-roadmap-2018.pdf.
[15] 中华人民共和国科学技术部. 科技部财政部关于发布国家科技资源共享服务平台优化调整名单的通知[EB/OL]. [2019-06-05]. http://www.most.gov.cn/mostinfo/xinxifenlei/fgzc/gfxwj/gfxwj2019/201906/t20190610_147031.htm.
Ministry of Science and Technology of the People's Republic of China. Notice of the Ministry of science and technology and the Ministry of Finance on publishing the list of optimization and adjustment of national science and technology resource sharing service platform[EB/OL]. [2019-06-05]. http://www.most.gov.cn/mostinfo/xinxifenlei/fgzc/gfxwj/gfxwj2019/201906/t20190610_147031.htm.
[16] 中国科学院计算机网络信息中心. 中国科学院数据云[EB/OL]. [2020-11-03]. http://www.csdb.cn/pageAboutPlatform.
Computer network information center, Chinese academy of sciences. CAS data cloud [EB/OL]. [2020-11-03]. http://www.csdb.cn/pageAboutPlatform.
[17] 中国科学院文献情报中心. 科技文献大数据知识资源体系[EB/OL].[2020-11-03]. http://www.las.ac.cn/others/institute_characteristic.jsp.
National science library, Chinese academy of sciences. Big data knowledge resource system of science and technology literature[EB/OL].[2020-11-03]. http://www.las.ac.cn/others/institute_characteristic.jsp.
[1] ZHAO Youlin, CAO Hongnan. Government Microblog Information Exchange Efficiency and Its Influencing Factors for Emergency Management [J]. Journal of Library and Information Science in Agriculture, 2022, 34(9): 72-85.
[2] WAN Hao, ZHANG Fujun, LV Qianqian. The Validity of Peer Review Results of DEA Based Super Efficiency Projects [J]. Journal of Library and Information Science in Agriculture, 2022, 34(2): 88-101.
[3] FENG Shaohua, ZAN Dong, SU Ju, ZHANG Zhan. Characteristics of Global "Marine Aquatic Feed" Domain Development Based on Patent Analysis [J]. Journal of Library and Information Science in Agriculture, 2021, 33(12): 71-82.
[4] HAN Zhengbiao, ZHOU Mingfeng, YUE Hang. Rural Residents' Health Information Avoidance Behavior in Lower Risk Disease Context [J]. Journal of Library and Information Science in Agriculture, 2021, 33(11): 4-15.
[5] CHU Jingli, LIU Peiyi, WENG Yanqin, LI Nan, YAN Qun, XIAO Yue. Investigation and Analysis of Different Roles' Recognition and Acceptance of Open Access Journals [J]. Journal of Library and Information Science in Agriculture, 2021, 33(9): 4-17.
[6] AI Yuxi, XU Jian, HE Lin, QI Yun. A Construction Method of the Classification System Oriented to Content Analysis of Ancient Books [J]. Journal of Library and Information Science in Agriculture, 2021, 33(9): 18-26.
[7] MA Xiaowen, HE Lin, LIU Jianbin, LI Zhangchao, GAO Dan. The Trigger Verb Classification Method of Event Sentences in Ancient Chinese Classics Based on Bi-LSTM [J]. Journal of Library and Information Science in Agriculture, 2021, 33(9): 27-36.
[8] REN Ni, GUO Ting, SUN Yiwei, DAI Hongjun, ZHANG Chengcheng. An Analysis of Global Smart Agriculture Research Situation [J]. Journal of Library and Information Science in Agriculture, 2021, 33(9): 48-63.
[9] CHEN Yunwei. Review on Quantitative Methods of Science and Technology Evaluation [J]. Journal of Library and Information Science in Agriculture, 2020, 32(8): 4-11.
[10] CAO Qi. System Analysis of the Next-generation Library Service Platform Based on Microservice Architecture——Taking FOLIO as an Example [J]. Journal of Library and Information Science in Agriculture, 2020, 32(4): 51-58.
[11] CAO Qi. Visual Modeling of Keyword Dimension Reduction in Double First-Class University Funds Based on t-SNE Algorithm [J]. Journal of Library and Information Science in Agriculture, 2020, 32(2): 47-57.
[12] LI Feifan. Research on the Universities Scientific Cooperation Network and Evolution: Taking 211 and Co-construction of Provincial and Subordinate universities of Beijing, Tianjin and Hebei region as an Example [J]. Agricultural Library and Information, 2019, 31(8): 31-39.
[13] LIU Zhihui, WEI Juanxia. Research on SMEs' Competitive Technology Intelligence Methodology System Oriented Open Innovation [J]. Agricultural Library and Information, 2019, 31(6): 12-20.
[14] YANG Siluo, YU Yonghao. Comparison of Artificial Intelligence Papers and Books Based on Citation and Altmetric Indicators [J]. Agricultural Library and Information, 2019, 31(5): 5-12.
[15] ZHAO Bingfeng. On the Development of National Intelligence Force and the Enlightenment of China-US Practice [J]. Agricultural Library and Information, 2019, 31(4): 29-36.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!