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Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes

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成果类型:
期刊论文
作者:
Mao, Jin;Liang, Zhentao*;Cao, Yujie;Li, Gang
通讯作者:
Liang, Zhentao
作者机构:
[Liang, Zhentao; Li, Gang; Mao, Jin] Wuhan Univ, Ctr Studies Informat Resources, Bayi Rd 299, Wuhan 430072, Peoples R China.
[Cao, Yujie] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
通讯机构:
[Liang, Zhentao] W
Wuhan Univ, Ctr Studies Informat Resources, Bayi Rd 299, Wuhan 430072, Peoples R China.
语种:
英文
关键词:
Cascade;Diffusion pattern;Interdisciplinary research;Knowledge diffusion;Knowledge relationship
期刊:
Journal of Informetrics
ISSN:
1751-1577
年:
2020
卷:
14
期:
4
页码:
101092
基金类别:
National Natural Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC) [71804135, 71921002, 71874129, 71790612]; world class discipline of the Ministry of Education "Library, Information, and Data Science"
机构署名:
本校为其他机构
院系归属:
信息管理学院
摘要:
Knowledge flow between disciplines is typically measured through citations among publications. In this study, we quantify cross-disciplinary knowledge diffusion from the novel perspective of content by introducing knowledge memes, a special type of knowledge unit. Diffusion cascade is proposed to model the diffusion process of knowledge memes. By taking Medical Informatics (MI) as an exemplary interdisciplinary discipline, we measure the knowledge relationships between it and four related disciplines. The diffusion patterns of cross-disciplinary memes are also identified by analyzing the netwo...

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