Depending on the amount of data to process, file generation may take longer.

If it takes too long to generate, you can limit the data by, for example, reducing the range of years.

Chapter

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Title

Linking Scholarly Datasets - The EOSC Perspective

Authors

[ 1 ] Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ 2 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ S ] student | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2023

Chapter type

chapter in monograph / paper

Publication language

english

Keywords
EN
  • Big scholarly datasets
  • Entity linking
  • EOSC
  • Microsoft Academic Graph
  • OpenAIRE Graph
Abstract

EN A plethora of publicly available, open scholarly data has paved the way for many applications and advanced analytics on science. However, a single dataset often contains incomplete or inconsistent records, significantly hindering its use in real-world scenarios. To address this problem, we propose a framework that allows linking scientific datasets. The resulting connections can increase the credibility of information about a given entity and serve as a link between different scholarly graphs. The outcome of this work will be used in the European Open Science Cloud (EOSC) as a base for introducing new recommendation features.

Date of online publication

26.06.2023

Pages (from - to)

608 - 623

DOI

10.1007/978-3-031-35995-8_43

URL

https://link.springer.com/chapter/10.1007/978-3-031-35995-8_43

Book

Computational Science – ICCS 2023 : 23rd International Conference, Prague, Czech Republic, July 3–5, 2023, Proceedings, Part I

Presented on

23rd International Conference on Computational Science ICCS 2023, 3-5.07.2023, Prague, Czech Republic

Ministry points / chapter

20

Ministry points / conference (CORE)

140

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