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An Expertise Recommender System Based on Data from an Institutional Repository (DiVA)

Milena Angelova (1), Vishnu Devagiri (2), Veselka Boeva (2), Peter Linde (2), Niklas Lavesson (2)
(1) Technical University of Sofia [Bulgaria]
(2) Blekinge Institute of Technology
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Détails de publication
Soumis le
June 20, 2018
Accepté le
June 20, 2018
Publié le
June 20, 2018
Modifié le
March 31, 2025
Acte de conférence 1
Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure
Long Papers
DOI
10.4000/proceedings.elpub.2018.17
Licence
Attribution 4.0 International (CC BY 4.0)
Indicateurs
513
Vues
593
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An Expertise Recommender System Based on Data from an Institutional Repository (DiVA)

Milena Angelova (1), Vishnu Devagiri (2), Veselka Boeva (2), Peter Linde (2), Niklas Lavesson (2)
(1) Technical University of Sofia [Bulgaria]
(2) Blekinge Institute of Technology
Abstract
Finding experts in academics is an important practical problem, e.g. recruiting reviewers for reviewing conference, journal or project submissions, partner matching for research proposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss an expertise recommender system that is built on data extracted from the Blekinge Institute of Technology (BTH) instance of the institutional repository system DiVA. The developed prototype system is evaluated and validated on information extracted from the BTH DiVA installation, concerning thesis supervision of researchers affiliated with BTH. The extracted DiVA classification terms are used to build an ontology that conceptualizes the thesis domain supported by the university. The supervisor profiles of the tutors affiliated with the BTH are constructed based on the extracted DiVA data. These profiles can further be used to identify and recommend relevant subject thesis supervisors.
Mots-clés
français
  • [SHS.INFO]Humanities and Social Sciences/Library and information sciences
anglais
  • natural language processing
  • data mining
  • DiVA
  • expertise retrieval
  • knowledge management
Cité par

Source : OpenCitations

  • Driving Innovation Ecosystem Transformation via Digital Platforms and Knowledge Co-Creation

    Advances in business information systems and analytics book series

    Auteurs/Autrices : Denilson Sell ORCID, Luiz Márcio Spinosa, Ramiro Wahrhaftig ORCID, Gérson Luiz Koch, Maria Angélica Jung Marques ORCID, Alfredo Cesar dos Anjos, Neri dos Santos ORCID, Roberto Carlos dos Santos Pacheco ORCID

    Référence de la revue : Volume , 2024, pp. 80-107

    DOI : 10.4018/979-8-3693-1058-8.ch005
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