Predicting Quality of Crowdsourced Annotations Using Graph Kernels

  • Archana Nottamkandath
  • Jasper Oosterman
  • Davide Ceolin
  • Gerben Klaas Dirk de Vries
  • Wan Fokkink
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 454)

Abstract

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 Steve.museum 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.

Keywords

Trust Machine learning Crowdsourcing RDF graph kernels 

Notes

Acknowledgement

This publication is supported by the Dutch national program COMMIT.

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Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Archana Nottamkandath
    • 1
  • Jasper Oosterman
    • 2
  • Davide Ceolin
    • 1
  • Gerben Klaas Dirk de Vries
    • 3
  • Wan Fokkink
    • 1
  1. 1.VU University AmsterdamAmsterdamThe Netherlands
  2. 2.Delft University of TechnologyDelftThe Netherlands
  3. 3.University of AmsterdamAmsterdamThe Netherlands

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