Predicting Quality of Crowdsourced Annotations Using Graph Kernels

Conference paper (2015)

Authors

Archana Nottamkandath Vrije Universiteit Amsterdam
J.E.G. Oosterman Web Information Systems - EEMCS
Davide Ceolin Vrije Universiteit Amsterdam
Dirk Vries Universiteit van Amsterdam
Wan Fokkink Vrije Universiteit Amsterdam
Research Group
Web Information Systems (EEMCS) (TU Delft)
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Published Date

01-05-2015

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

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.