Abstract
| Original language | English |
|---|---|
| Publication status | Published - 2017 |
| Event | 7th Global TechMining Conference, GTM 2017 - Atlanta, United States Duration: 9 Oct 2017 → … |
Conference
| Conference | 7th Global TechMining Conference, GTM 2017 |
|---|---|
| Abbreviated title | GTM 2017 |
| Country | United States |
| City | Atlanta |
| Period | 9/10/17 → … |
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Human assigned vs. machine created : Links between patents and scholarly publications. / Suominen, Arho.
2017. Abstract from 7th Global TechMining Conference, GTM 2017, Atlanta, United States.Research output: Contribution to conference › Conference Abstract › Scientific
TY - CONF
T1 - Human assigned vs. machine created
T2 - Links between patents and scholarly publications
AU - Suominen, Arho
N1 - Published: Abstract only
PY - 2017
Y1 - 2017
N2 - To measure knowledge flows between scholarly literature and patents, studies have used a several approaches, such as keyword search based count of science publications and patenting in a technology with an expectation of linearity of innovation, patents citing publications or vice versa, or author-inventor co-occurrences. Patent citations, specifically non-patent references has often been seen as a proxy of for the "science-dependence" or "science-base" of a technology, although this has been critiqued as an over simplification. Another avenue to classify patents and publications would be to rely on machine learning, specifically unsupervised learning. In this study, we analyse the relationship between publication and patents by looking at the intersection of human assigned and machine learned linkages between science and patents. We use a macro level approach focusing on the whole science publication and patents from one country.
AB - To measure knowledge flows between scholarly literature and patents, studies have used a several approaches, such as keyword search based count of science publications and patenting in a technology with an expectation of linearity of innovation, patents citing publications or vice versa, or author-inventor co-occurrences. Patent citations, specifically non-patent references has often been seen as a proxy of for the "science-dependence" or "science-base" of a technology, although this has been critiqued as an over simplification. Another avenue to classify patents and publications would be to rely on machine learning, specifically unsupervised learning. In this study, we analyse the relationship between publication and patents by looking at the intersection of human assigned and machine learned linkages between science and patents. We use a macro level approach focusing on the whole science publication and patents from one country.
M3 - Conference Abstract
ER -