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An Expertise Recommender System based on Data from an Institutional Repository (DiVA)
Technical University of sofia, BUL.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-3371-5347
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-3128-191x
Blekinge Institute of Technology, The Library.ORCID iD: 0000-0002-4308-7332
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2019 (English)In: Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers / [ed] Leslie Chan, Pierre Mounier, OpenEdition Press , 2019, p. 135-149Chapter in book (Refereed)
Abstract [en]

Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors in academy.

Place, publisher, year, edition, pages
OpenEdition Press , 2019. p. 135-149
Keywords [en]
Text mining, Recommender system, Institutional repository, Ontology
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:bth-18095ISBN: 979-1-0365-3801-8 (print)ISBN: 979-1-0365-3802-5 (electronic)OAI: oai:DiVA.org:bth-18095DiVA, id: diva2:1326633
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge Foundation
Note

open access

Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2021-10-25Bibliographically approved

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Connecting the Knowledge Common from Projects to sustainable Infrastructure

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Vishnu Manasa, DevagiriBoeva, VeselkaLinde, PeterLavesson, Niklas

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Citation style
  • apa
  • ieee
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Language
  • de-DE
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Output format
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