Predicting Quality of Crowdsourced Annotations Using Graph Kernels

Archana Nottamkandath, Jasper Oosterman, Davide Ceolin, Gerben Klaas Dirk de Vries, Wan Fokkink

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)


Annotations obtained by Cultural Heritage institutions from the crowd need to be automatically assessed for their quality. Machine learning using graph kernels is an effective technique to use structural information in datasets to make predictions. We employ the Weisfeiler-Lehman graph kernel for RDF to make predictions about the quality of crowdsourced annotations in dataset, which is modelled and enriched as RDF. Our results indicate that we could predict quality of crowdsourced annotations with an accuracy of 75 %. We also employ the kernel to understand which features from the RDF graph are relevant to make predictions about different categories of quality.
Original languageEnglish
Title of host publicationTrust Management IX - 9th IFIP WG 11.11 International Conference, IFIPTM 2015, Hamburg, Germany, May 26-28, 2015, Proceedings
EditorsChristian Damsgaard Jensen, Stephen Marsh, Theo Dimitrakos, Yuko Murayama
Number of pages15
ISBN (Print)978-3-319-18490-6
Publication statusPublished - 1 May 2015

Publication series

NameIFIP Advances in Information and Communication Technology


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