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Review of Latent Knowledge Discovery Methods Based on Association Between Scientific Papers and Technology Patents |
Wang Shiwei1,2,Chen Chun1,2() |
1Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China 2Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China |
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Abstract [Objective] This paper reviews the latent knowledge discovery methods based on scientific papers and technology patents to identify deficiencies in current studies and future development directions.[Coverage] A total of 75 representative articles were retrieved using keywords such as “Patents and Papers”, “Science and Technology”, and “Knowledge Discovery” from the Web of Science, Springer Link, and CNKI. [Methods] Based on the scientific-technical association, we reviewed the literature from four aspects: data association, subject association, theme association, and multi-dimensional association. [Results] The existing research methods have limitations, such as the need for more data sources for identifying corpus and the non-standardization of heterogeneous data sources. The potential knowledge discovery of the recognition method needs more semantics and better granularity. The knowledge system and measurement index based on papers and patents still need to be completed. The recognition results need more comprehensiveness, dynamic and exploratory nature. [Limitations] Mainly select some representative literature to review, in-depth elaboration is not deep enough. At the level of content analysis, the multi-strategy comprehensive analysis method of science-technology correlation is a hot research at present, but the analysis of this method is not systematic enough in this paper. The selection of representative review literature obtained from the search has a certain degree of individual subjectivity. [Conclusions] In future research, we should integrate multi-source databases and standardize heterogeneous data, enhance the semantic analysis ability of recognition methods, and refine the recognition granularity. We also need to improve the knowledge organization system, enrich the measurement indicators, and strengthen the research on the dynamic evolution of latent knowledge discovery.
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Received: 19 September 2022
Published: 07 September 2023
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Fund:Intellectual Property Projects in Gansu Province(20ZSCQ025) |
Corresponding Authors:
Chen Chun,ORCID:0000-0003-4351-4696,E-mail: chenc@llas.ac.cn。
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