Research on Bibliographic Description of Literature Resources from the Perspective of Cognitive Computing

Abstract

Abstract: Cognitive computing is a subject system that perceives and thinks about the information processing process. The cognitive computing system extracts relevant concepts and individual associations from existing bibliographic data, and uses machine intelligence to create bibliographic data models independent of bibliographic format or past learning experience. The new bibliographic description model emphasizes the organic combination of data science, cognitive system and RDA cataloging rules. By analyzing the relationship between “data layer -information layer -knowledge layer” in cognitive computing system and the three groups of entities in FRBR bibliographic world, the problems of complicated data content, dynamic information distribution and knowledge hidden level in document cataloging can be properly handled. With the help of cognitive means such as data mining, visual cognition and natural language processing, the cataloging work of literature resources requires four steps:knowledge acquisition, knowledge coding, knowledge reasoning and knowledge verification. Finally, the informationization of bibliographic data will eventually be realized, which will provide intelligent support for the rational layout of library collection, the innovation of reading promotion and the production of personalized information service.

Jin Hua (Tianjin University of Commerce Library). Research on Bibliographic Description of Literature Resources from the Perspective of Cognitive Computing[J]. Libraly Journal, 2022, 41(9): 43-51.

References

[ 1 ] Ulrich Furbach，Stefen H?lldobler，Marco Ragni，et al. Cognitive Reasoning：A Personal View[J].KI—Künstliche Intelligenz，2019（33）：209–217.

[ 2 ] Mona Nagy ElBedwehy，Mohamed Elsayed Ghoneim，Aboul Ella Hassanien， et al. A computational knowledge representation model for cognitive computers[J]. Neural Comput & Applic，2014（25）：1517–1534.
[ 3 ] The Library of Congress. MARC XML：Uses and Features[EB/OL]. [2013-02-25]. http://www.loc.gov/standards/marcxml/marcxml-overview.html.
[ 4 ] 张晓林 . 颠覆性变革与后图书馆时代——推动知识服务的供给侧结构性改革 [J]. 中国图书馆学报，2018（1）：4–16.
[ 5 ] 认知计算、人工智能的区别与理解[EB/OL].[2019-10-02]. https://blog.csdn.net/tiandiwuya/article/details/78027660.ViolinoB.
[ 6 ] What is cognitive computing？ Here’s what youshould know[EB/OL]. [2019-10-02]. https://www.infoworld.com/ar-ticle/3198633/primer-makesense-of-cognitive-computing.html .
[ 7 ] Hu Jin. Data processing model and performance analysis of cognitive computing based on machine learning in Internet environment[J]. Soft Computing，2019（23）：9141–9151.
[ 8 ] Jordi Fernandez. Explanation by Computer Simulation in Cognitive Science[J]. Minds and Machines，2003（13）：269–284.
[ 9 ] Guoyin Wang.DGCC：data-driven granular cognitive computing[J]. Granul Comput，2017（2）：
343–355.
[10] OCLC. FRBR Work-set Algorithm Version 2.0[EB/OL]. [2017-11-22]. http://www.oclc.org/content/
dam/research/activities/frbralgorithm/2009-08l.pdf.
[11] 辛苗 .《中国文献编目规则》与RDA 关于著录信息源规定的对比分析——以普通图书题名和责任说明为例 [J]. 图书馆杂志，2019（6）：45–49.
[12] Bibliographic Framework as a Web of Data：Linked Data Model and Supporting Services[EB/
OL].（ 2012-11-21）[2013-09-12]. http://www.loc.gov/bibframe/pdf/marcld-report-11-21-2012.pdf.
[13] Eric C C Tsang，Bingjiao Fan，Degang Chen，et al. Multi-level cognitive concept learning method oriented to data sets with fuzziness：a perspective from features[J]. Soft Computing，2020（24）：3753–3770.
[14] RDA 发展联合指导委员会 . 资源描述与检索：RDA[M]. 北京：北京图书馆出版社，2014：33.
[15] Network Development and MARC Standards Office. FRBR Display Tool Version 2.0[EB/OL].[2017-11-20]. http://www.loc.gov/marc/marcfunctional-analysis/tool.html.
[16] Gordana Dodig-Crnkovic. Cognitive revolution，virtuality and good life[J]. AI & Soc，2013（28）：
319–327.
[17] Sahoo J， Das A K， Goswami A. An efficient approach for mining association rules from high utility item sets[J]. Expert Systems with Applications，2015（42）：5754– 5778.
[18] Pentti O A Haikonen. The Role of Associative Processing in Cognitive Computing[J]. Cogn Comput，2009（1）：42–49.
[19] Chen Y，Argentinis J E，Weber G. IBM Watson：how cognitive computing can be applied to big
data challenges in life sciences research[J]. Clinical Therapeutics，2016，38（4）：688.
[20] Jamesh H Fetzer. Thinking and Computing：Computers as Special Kinds of Signs[J]. Minds and Machines，1997（7）：345–364.