A novel approach to measuring science-technology linkage: From the perspective of knowledge network coupling

Ba, Zhichao; Liang, Zhentao

doi:10.1016/j.joi.2021.101167

Cited by 28 publications

(14 citation statements)

References 69 publications

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“…Some scholars believe that the scientific research represented by papers has a certain role in promoting technological research and development represented by patents. A knowledge network coupling method was proposed to compare the coupling strength between science and technology networks, and conclude that scientific development is ahead of technological progress (Ba & Liang, 2021). As a direct form of linkage, citations confirmed and illustrated the social character of knowledge diffusion and transfer between science and technology (Li et al, 2017).…”

Section: Papers-patents Correlation Analysismentioning

confidence: 99%

“…In addition, to improve the accuracy of the selection results of patent topics. The details include the following steps: Some scholars believe that drawing on the views of existing research, this paper argues that the scientific research represented by papers has a certain role in promoting the technological development represented by patents (Ba & Liang, 2021). Thus, the high-value paper topics are applied in this paper to guide the selection of patent topics with development potential.…”

Section: Selecting Patent Topicsmentioning

confidence: 99%

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Technology opportunity discovery of proton exchange membrane fuel cell from the perspective of papers-patents correlation analysis

Feng

Liu²,

Wang³

et al. 2023

Front. Energy Res.

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Papers and patents can respectively present the latest progress of scientific research and technological development. Combining the two for correlation analysis has certain reference significance for technology opportunity discovery. Therefore, this paper proposes a method for technology opportunity discovery of proton exchange membrane fuel cell from the perspective of papers-patents correlation analysis. Firstly, papers and patent data are collected from papers and patent databases and the LDA model is applied to extract paper and patent topics. Secondly, the indicators of topic heat and novelty are calculated to select high-value paper topics. After that, the similarity analysis between high-value paper topics and all patent topics is considered to select the patent topics with development potential. Finally, the patent texts with high relevance to these patent topics with development potential are positioned. The technical keywords in them are extracted by text mining tool and classified into innovation dimensions according to their attributes and the TEMPEST model. After that, the innovation rules in the SCAMPER model are introduced to combine with the technical keywords in each innovation dimension to identify specific technology opportunities. An empirical study on the technology of proton exchange membrane fuel cell provides sufficient evidence of the method’s ability to discover technology opportunities. This paper contributes by generating four specific technology opportunities, which can make up for the existing defects of proton exchange membrane fuel cells and provide useful guidance for companies to carry out technological innovation.

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Section: Papers-patents Correlation Analysismentioning

confidence: 99%

Section: Selecting Patent Topicsmentioning

confidence: 99%

Technology opportunity discovery of proton exchange membrane fuel cell from the perspective of papers-patents correlation analysis

Feng

Liu²,

Wang³

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

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“…Jiang et al (2019) used graph merging to combine graphs constructed based on different views and improve label propagation by merging ordered relations between reading layers. Ba and Liang (2021) measured the interaction pattern of S&T network linkages by constructing coupled S&T networks. It has been demonstrated that coupled networks outperform previous single networks in studying frontier prediction (Guan & Liu, 2016), weakening network distrust (Marsh & Dibben, 2010), and cross-domain knowledge transfer identification (Hsiao & Torvik, 2019).…”

Section: Network Coupling Measurementmentioning

confidence: 99%

A network coupling approach to detecting hierarchical linkages between science and technology

Meng,

Ba,

et al. 2023

Asso for Info Science & Tech

Self Cite

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Detecting science–technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K‐core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.

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“…As for lexical-and topic-based linkages, a popular pipeline research framework (Ba & Liang, 2021;Shibata et al, 2010;Xu et al, 2012Xu et al, , 2019Xu et al, , 2020 is to extract respective thematic structures from scholarly articles and patents, to calculate the similarities between them, and then to construct topic linkages. However, the performance of such a framework is inadequate (Shibata et al, 2010;Xu et al, 2012), since noncomparable themes with different distributions are generated from scientific publications and patents (Xu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Do academic inventors have diverse interests?

Hao

2023

Scientometrics

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Academic inventors bridge science and technology, and have attracted increasing attention. However, little is known about whether they have more diverse research interests than researchers with a single role, and whether their important position for science–technology interactions correlates with their diverse interests. For this purpose, we describe a rule-based approach for matching and identifying academic inventors, and an author interest discovery model with credit allocation schemes is utilized to measure the diversity of each researcher’s interests. Finally, extensive empirical results on the DrugBank dataset provide several valuable insights. Contrary to our intuitive expectation, the research interests of academic inventors are the least diverse, while those of authors are the most. In addition, the important position of the researchers has a certain relation with the diversity of research interests. More specifically, the degree of centrality has a significant positive correlation with the diversity of interests, and the constraint presents a significant negative correlation. A significant weaker negative correlation can also be observed between the diversity of research interests of academic inventors and their closeness centrality. The normalized betweenness centrality seems be independent from interest diversity. These conclusions help understand the mechanisms of the important position of academic inventors for science–technology interactions, from the perspective of research interests.

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A novel approach to measuring science-technology linkage: From the perspective of knowledge network coupling

Cited by 28 publications

References 69 publications

Technology opportunity discovery of proton exchange membrane fuel cell from the perspective of papers-patents correlation analysis

Technology opportunity discovery of proton exchange membrane fuel cell from the perspective of papers-patents correlation analysis

A network coupling approach to detecting hierarchical linkages between science and technology

Do academic inventors have diverse interests?

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