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Identifying Promising Research Topics in Computer Science

  • Rajmund KlemińskiEmail author
  • Przemyslaw Kazienko
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

In this paper, we investigate an interpretable definition of promising research topics, complemented with a predictive model. Two methods of topic identification were employed: bag of words and the LDA model, with reflection on their applicability and usefulness in the task of retrieving topics on a set of publication titles. Next, different criteria for promising topic were analyzed with respect to their usefulness and shortcomings. For verification purposes, the DBLP data set, an online open reference of computer science publications, is used. The presented results reveal potential of the proposed method for identification of promising research topics.

Keywords

Research prediction Promising topic Topic modelling DBLP 

Notes

Acknowledgements

This work was partially supported by the National Science Centre, Poland, project no. 2016/21/B/ST6/01463 and by European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant no. 691152 (RENOIR); by the Polish Ministry of Science and Higher Education fund for supporting internationally co-financed projects in 2016-2019, no. 3628/H2020/2016/2.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computational Intelligence, ENGINE - The European Centre for Data Science, Faculty of Computer Science and ManagementWroclaw University of Science and TechnologyWroclawPoland

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