A Scholarly Knowledge Graph-Powered Dashboard: Implementation and User Evaluation.

ORKG dashboard scholarly communication scholarly knowledge graph user evaluation

Journal

Frontiers in research metrics and analytics
ISSN: 2504-0537
Titre abrégé: Front Res Metr Anal
Pays: Switzerland
ID NLM: 101718019

Informations de publication

Date de publication:
2022
Historique:
received: 03 05 2022
accepted: 21 06 2022
entrez: 5 8 2022
pubmed: 6 8 2022
medline: 6 8 2022
Statut: epublish

Résumé

Scholarly knowledge graphs provide researchers with a novel modality of information retrieval, and their wider use in academia is beneficial for the digitalization of published works and the development of scholarly communication. To increase the acceptance of scholarly knowledge graphs, we present a dashboard, which visualizes the research contributions on an educational science topic in the frame of the Open Research Knowledge Graph (ORKG). As dashboards are created at the intersection of computer science, graphic design, and human-technology interaction, we used these three perspectives to develop a multi-relational visualization tool aimed at improving the user experience. According to preliminary results of the user evaluation survey, the dashboard was perceived as more appealing than the baseline ORKG-powered interface. Our findings can be used for the development of scholarly knowledge graph-powered dashboards in different domains, thus facilitating acceptance of these novel instruments by research communities and increasing versatility in scholarly communication.

Identifiants

pubmed: 35928800
doi: 10.3389/frma.2022.934930
pmc: PMC9343766
doi:

Types de publication

Journal Article

Langues

eng

Pagination

934930

Informations de copyright

Copyright © 2022 Lezhnina, Kismihók, Prinz, Stocker and Auer.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Références

Front Res Metr Anal. 2021 Dec 23;6:748095
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Front Res Metr Anal. 2022 Jan 31;6:751553
pubmed: 35178498
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pubmed: 33954273
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Pers Soc Psychol Rev. 2021 Aug;25(3):251-272
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Auteurs

Olga Lezhnina (O)

Learning and Skill Analytics Research Group, TIB-Leibniz Information Centre for Science and Technology, Hannover, Germany.

Gábor Kismihók (G)

Learning and Skill Analytics Research Group, TIB-Leibniz Information Centre for Science and Technology, Hannover, Germany.

Manuel Prinz (M)

Data Science and Digital Libraries Group, TIB-Leibniz Information Centre for Science and Technology, Hannover, Germany.

Markus Stocker (M)

Knowledge Infrastructures Research Group, TIB-Leibniz Information Centre for Science and Technology, Hannover, Germany.
L3S Research Center, Leibniz University of Hannover, Hannover, Germany.

Sören Auer (S)

L3S Research Center, Leibniz University of Hannover, Hannover, Germany.
TIB-Leibniz Information Centre for Science and Technology, Hannover, Germany.

Classifications MeSH