情报认知模型库构建研究

doi:10.13998/j.cnki.issn1002-1248.20-0969

农业图书情报学报 ›› 2021, Vol. 33 ›› Issue (1): 32-40.doi: 10.13998/j.cnki.issn1002-1248.20-0969

情报认知模型库构建研究

刘细文^1,2, 郭世杰^1,2

1.中国科学院大学经济与管理学院图书情报与档案管理系,北京 100049;
2.中国科学院文献情报中心,北京 100190

收稿日期:2020-10-28 出版日期:2021-01-05 发布日期:2021-02-05
作者简介:刘细文（ORCID：0000-0003-0820-3622）,男,博士,二级研究员,博士生导师,研究方向为情报学。郭世杰（ORCID：0000-0003-2782-7553）,男,博士在读,助理研究员,研究方向为情报学理论与方法、空间光电科技情报
基金资助:
中国科学院文献情报能力建设专项课题“科技知识服务大数据基础设施”的子课题“技术领域情报挖掘模型”（2020WQZX0001）; 中国科学院文献情报中心青年人才创新团队项目“面向科研设施的信息抽取”（G180141）

A Database Construction of S&T Intelligence Cognition Models

LIU Xiwen^1,2, GUO Shijie^1,2

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

摘要/Abstract

摘要： [目的/意义] 研究情报认知模型库的组织、建设方法,帮助科研人员和情报分析人员快速形成对学科领域的准确认知,协助识别科技机遇、甄别技术威胁。[方法/过程] 情报认知模型库包含跨领域的各种技术要素和文献信息,因此在结构上包括文献库、算法库、学科领域知识库、研究应用案例库等,支持研究热点识别、技术性能对比、智能分析方法推荐、算法辅助设计等功能。模型库的建设流程包括对情报认知模型的收集、验证、存储、组织、利用等,其中对模型的验证是至关重要的一步。[结果/结论] 构建情报认知模型库对开展科学技术领域情报工作具有重要意义,能够发挥科技数据基础设施、科技情报分析工具箱的效果。情报认知模型库的构建需要情报人员、科学技术领域专家、信息技术人员的通力合作,未来需要进一步考虑模型库的维护升级、推广应用等问题。

关键词: 情报认知模型, 模型库, 情报研究基础设施, 学术情报研究

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

中图分类号:

G350

刘细文, 郭世杰. 情报认知模型库构建研究[J]. 农业图书情报学报, 2021, 33(1): 32-40.

LIU Xiwen, GUO Shijie. A Database Construction of S&T Intelligence Cognition Models[J]. Journal of Library and Information Science in Agriculture, 2021, 33(1): 32-40.

参考文献

[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.

情报认知模型库构建研究

A Database Construction of S&T Intelligence Cognition Models

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