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New Technology of Library and Information Service  2016, Vol. 32 Issue (7-8): 42-50    DOI: 10.11925/infotech.1003-3513.2016.07.06
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Radical Innovation Identification Based on Topic Mutation of Scientific Knowledge Cited in Patents
Zhang Jinzhu1(),Zhang Xiaolin2
1School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094, China
2National Science Library, Chinese Academy of Sciences, Beijing 100190, China
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[Objective] Technical fields that closely related to basic research require radical innovation identification from the content of scientific knowledge cited in patents (SKCP). [Methods] This paper firstly extracts keywords and subject categories of scientific references in patents to represent SKCP, then identifies topics in keywords co-occurrence network and combinations of subject categories, finally proposes the method of topic mutation degree calculation based on keywords and subject categories, to identify technical topics of radical innovation. [Results] In the domain of Nano electronics, Nano circuit is an approved radical innovation. The related topics about this are confirmed using proposed method including Nano wire, carbon nanotubes, computing circuit, and Nano materials and the manufacturing technologies. Moreover, the corresponding combination of subject categories is materials science, chemistry, optics, biology and applied physics. [Limitations] The accuracy of SKCP’s extraction, preprocessing and matching needs to be improved and the generality of method needs to be validated in other areas. [Conclusions] This method is an important improvement and supplement of radical innovation identification based on patent information and could be extended to other technical fields that are closely related to basic research.

Key wordsRadical innovation      Scientific knowledge cited in patents      Topic mutation      Mutation rate      Nano electronics     
Received: 04 May 2016      Published: 29 September 2016

Cite this article:

Zhang Jinzhu,Zhang Xiaolin. Radical Innovation Identification Based on Topic Mutation of Scientific Knowledge Cited in Patents. New Technology of Library and Information Service, 2016, 32(7-8): 42-50.

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