Knowledge transfer from technology to science: The longevity of paper‐to‐patent citations

Hsiao, Tzu Kun; Torvik, Vetle I.

doi:10.1002/pra2.41

Cited by 7 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scope of subject areas made the PMCOA subset a suitable corpus for studying paper-to-patent citations since Glänzel and Meyer (2003) had reported that many paper-cited patents were in the fields of chemical/organic compounds, drugs, and medical. At the time of data collection (May 2019), there were 2,407,660 articles in the PMCOA subset, and 7,223 articles with at least one paper-to-patent citation were identified through the method described in Hsiao and Torvik (2019). In the 429,698 references cited by 7,223 papers, 11,165 (2.6%) were paper-to-paper citations, and 418,533 (97.4%) were paper-to-patent citations.…”

Section: Methodsmentioning

confidence: 99%

Technology footprints in scientific discovery: Citation contexts of paper‐to‐patent citations

Hsiao

Torvik

2020

Proc Assoc Inf Sci Technol

Self Cite

View full text Add to dashboard Cite

Citations between papers and patents indicate knowledge transfer between science and technology. This study discusses the function of paper‐to‐patent citations through citation contexts. 7,223 articles having at least one paper‐to‐patent citation are identified from a collection of 2.4 million PubMed Central open access articles. Within 429,698 references cited by the 7,223 articles, 11,165 (2.6%) are paper‐to‐patent citations, while 418,533 (97.4%) are paper‐to‐paper citations. In comparison to paper‐to‐paper citations, paper‐to‐patent citations were more concentrated at the beginning of articles. Higher proportions of patents are cited in the introduction and methods sections and are only cited once. The results indicate that patents are likely to be cited for providing background knowledge. The appearance of paper‐to‐patent citations in the method sections implies that patents might also contribute to the development of research methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Technology footprints in scientific discovery: Citation contexts of paper‐to‐patent citations

Hsiao

Torvik

2020

Proc Assoc Inf Sci Technol

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

“…Citation relationships between papers and patents map the mutual influence of S&T advances. This method includes papers citing patents (Wagner‐Döbler, 1997), patents citing papers (Ahmadpoor & Jones, 2017; Callaert et al, 2006), and mutual citations (Hsiao & Torvik, 2019; Wang & Ye, 2021). Given the unfortunate fact that patents do not cite relevant papers as revealed by NPRs (Meyer, 2000), there is limited reliability in identifying linkages using citations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Chasing these historical knowledge dynamics can detect critical drivers of S&T change (Hu & Rousseau, 2018), understand contractual linkages among governments and S&T (Stokes, 1997), and weaken the crisis of trust between S&T (Marsh & Dibben, 2010). Hence, it is especially critical to measure these linkages based on knowledge relationships between S&T (Hsiao & Torvik, 2019; Xu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Meng,

Ba,

et al. 2023

Asso for Info Science & Tech

View full text Add to dashboard Cite

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.

show abstract

“…However, the transmission from technology to science has not been well studied [29]. While patents contain detailed methodological information on successful innovations, references to patents are rarely found in applied science or science texts [29,30,31]. According to Glanzel and Meyer [29], the publications that have such reverse citations account for only 0.98% of all total publications between 1996 and 2000, of which 30% are in chemical-related fields.…”

Section: Introductionmentioning

confidence: 99%

Extracting Problem Linkages to Improve Knowledge Exchange between Science and Technology Domains using an Attention-based Language Model

Sasaki

Yamamoto²,

Agchbayar³

et al. 2020

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Science and technology activities can be considered problem-solving activities, and scientific papers and patent publications can be viewed as providing explicit knowledge gained from the problem-solving of academia and industry respectively. However, even in the same field, the approach to the same problem is not consistent between a paper and the patented technology. The creation of information silos in science and technology generates inefficiency in human intellectual production. Therefore, this study examines whether insights from technical problems can be shared with academics to solve scientific problems. We propose a concept to link the problems between these two domains using a linguistic approach for knowledge discovery that connects science and technology. We extracted scientific papers from the Association for Computational Linguistics dataset, and patent literature from the Derwent Innovation platform. From these, pairs of problem defining sentences were identified and extracted using an attention-based language model. For example, we were able to extract examples of issues that do not necessarily arise from scientific papers, such as annotation difficulties in the analysis of social network data, but can be hinted at by patented techniques prior to the paper. These results suggest that scientific problems and industrial solutions can provide mutual insight. This knowledge discovery approach is recommended not only for benefiting corporate activities but also for grasping research trends.

show abstract

Knowledge transfer from technology to science: The longevity of paper‐to‐patent citations

Cited by 7 publications

References 14 publications

Technology footprints in scientific discovery: Citation contexts of paper‐to‐patent citations

Technology footprints in scientific discovery: Citation contexts of paper‐to‐patent citations

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

Extracting Problem Linkages to Improve Knowledge Exchange between Science and Technology Domains using an Attention-based Language Model

Contact Info

Product

Resources

About