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.
|Publication status||Published - 2017|
|Event||Atlanta Conference on Science and Innovation Policy 2015 - Atlanta, United States|
Duration: 17 Sep 2015 → 19 Sep 2015
Conference number: 6
|Conference||Atlanta Conference on Science and Innovation Policy 2015|
|Period||17/09/15 → 19/09/15|
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.