ABSTRACT

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.

摘要

欧盟 (EU) 已经认识到大学和研究机构在区域智能专业化过程中发挥着关键作用。我们的研究旨在确定北极斯堪的纳维亚半岛跨空间和学科的主题跨境研究领域。我们使用无监督机器学习技术（主题建模）识别潜在领域。我们根据研究论文词汇的相似性发现潜在主题。所提出的方法可用于几乎实时地识别跨地区和跨学科的共同研究领域，从而充当促进知识生产者之间合作的决策支持系统。