Unsupervised learning based linkages between patents and scholarly publications

Arho Suominen, Hannes Toivanen

Research output: Contribution to conferenceConference AbstractScientific

Abstract

Bibliometrics has been used to produce measures of knowledge flows between scholarly literature and patents, most notably by using the non-patent literature citation in patents. Existing methods offer a obstructed and narrow view of interplay between science and technology. This study complements existing methods by analyzing the semantic similarity of patents and publications in the context of Finland, uncovering thematic overlap between science and technology. The study uses Latent Dirichlet Allocations to analyze 185 931 patent and publication records in a merged corpus. The data spans patents (USPTO) and publications (WOS) with one or more Finnish author or inventor. The approach enabled the discovery of patent and publication links between documents without an explicit citation between the documents. This suggests that the method could complement existing approaches to science and technology mapping by producing a novel vantage point to the issue.
Original languageEnglish
Publication statusPublished - 2017
EventAtlanta Conference on Science and Innovation Policy 2015 - Atlanta, United States
Duration: 17 Sep 201519 Sep 2015
Conference number: 6

Conference

ConferenceAtlanta Conference on Science and Innovation Policy 2015
CountryUnited States
CityAtlanta
Period17/09/1519/09/15

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patent
learning
science
Finland
semantics
knowledge
literature

Cite this

Suominen, A., & Toivanen, H. (2017). Unsupervised learning based linkages between patents and scholarly publications. Abstract from Atlanta Conference on Science and Innovation Policy 2015, Atlanta, United States.
Suominen, Arho ; Toivanen, Hannes. / Unsupervised learning based linkages between patents and scholarly publications. Abstract from Atlanta Conference on Science and Innovation Policy 2015, Atlanta, United States.
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Suominen, A & Toivanen, H 2017, 'Unsupervised learning based linkages between patents and scholarly publications' Atlanta Conference on Science and Innovation Policy 2015, Atlanta, United States, 17/09/15 - 19/09/15, .

Unsupervised learning based linkages between patents and scholarly publications. / Suominen, Arho; Toivanen, Hannes.

2017. Abstract from Atlanta Conference on Science and Innovation Policy 2015, Atlanta, United States.

Research output: Contribution to conferenceConference AbstractScientific

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T1 - Unsupervised learning based linkages between patents and scholarly publications

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AU - Toivanen, Hannes

PY - 2017

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N2 - Bibliometrics has been used to produce measures of knowledge flows between scholarly literature and patents, most notably by using the non-patent literature citation in patents. Existing methods offer a obstructed and narrow view of interplay between science and technology. This study complements existing methods by analyzing the semantic similarity of patents and publications in the context of Finland, uncovering thematic overlap between science and technology. The study uses Latent Dirichlet Allocations to analyze 185 931 patent and publication records in a merged corpus. The data spans patents (USPTO) and publications (WOS) with one or more Finnish author or inventor. The approach enabled the discovery of patent and publication links between documents without an explicit citation between the documents. This suggests that the method could complement existing approaches to science and technology mapping by producing a novel vantage point to the issue.

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M3 - Conference Abstract

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Suominen A, Toivanen H. Unsupervised learning based linkages between patents and scholarly publications. 2017. Abstract from Atlanta Conference on Science and Innovation Policy 2015, Atlanta, United States.