Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia

The European Union (EU) has recognized that universities and research institutes play a critical role in regional Smart Specialisation processes. Our research aims to identify thematic cross-border research domains across space and disciplines in Arctic Scandinavia. We identify potential domains usi...

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Bibliographic Details
Published in:Regional Studies
Main Authors: Moilanen, Mikko, Østbye, Stein, Jaakko, Simonen
Format: Article in Journal/Newspaper
Language:English
Published: Taylor & Francis 2021
Subjects:
VDP::Technology: 500::Information and communication technology: 550
VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550
Arctic
Online Access:https://hdl.handle.net/10037/22909
https://doi.org/10.1080/00343404.2021.1925237
Description
Summary:The European Union (EU) has recognized that universities and research institutes play a critical role in regional Smart Specialisation processes. Our research aims to identify thematic cross-border research domains across space and disciplines in Arctic Scandinavia. We identify potential domains using an unsupervised machine-learning technique (topic modelling). We uncover latent topics based on similarities in the vocabulary of research papers. The proposed methodology can be utilized to identify common research domains across regions and disciplines in almost real time, thereby acting as a decision support system to facilitate cooperation among knowledge producers.

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