Enhancing Knowledge Organization Through Implicit Collaboration in Crowdsourcing Process

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Date
2021
Authors
Odumuyiwa, V.
Zaid, Y.A
Barber, O.
Journal Title
Journal ISSN
Volume Title
Publisher
Nomos Verlagsgesellschaft mbH & Co. KG
Abstract
This paper presents our approach in removing noisy labels from crowdsourced data and enhancing understanding and communication through crowdsourcing process aimed at creating metadata for describing digitalized artworks of a University Library Museum. A responsive Web application was created for the crowdsourcing activity and made open to the University community for interested individuals to participate in annotating the images. The collected annotation in form of tags were preprocessed and filtered to generate a subset of tags by removing duplicates and also eliminating some noisy labels using majority voting. The resulting subset was used as labels for the images for a second round of crowdsourcing process where users chose from the filtered labels. Comparing the output of the second round with the label (tags) from an expert shows a high level of similarity between the selected tags and the expert generated tags.
Description
Proceedings of the Sixteenth International ISKO Conference, 2020 Aalborg, Denmark
Keywords
Library Museum , Knowledge organization , Crowdsourcing process , Knowledge sharing , Communication , Artworks , Research Subject Categories::SOCIAL SCIENCES::Other social sciences::Library and information science
Citation
Odumuyiwa, V., Zaid, Y.A & Barber, O. (2021). Enhancing Knowledge Organization Through Implicit Collaboration in Crowdsourcing Process. Advances in Knowledge Organization, 17, International Society for Knowledge Organziation (ISKO), Marianne Lykke, Tanja Svarre, Mette Skov, Daniel Martínez-Ávila (ed.) Knowledge Organization at the Interface, page 507 - 511 Proceedings of the Sixteenth International ISKO Conference, 2020 Aalborg, Denmark.