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Predicting Quality of Crowdsourced Annotations Using Graph Kernels

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 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.
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Submitted on : Wednesday, December 14, 2016 - 11:09:04 AM
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Archana Nottamkandath, Jasper Oosterman, Davide Ceolin, Gerben De Vries, Wan Fokkink. Predicting Quality of Crowdsourced Annotations Using Graph Kernels. 9th IFIP International Conference on Trust Management (TM), May 2015, Hamburg, Germany. pp.134-148, ⟨10.1007/978-3-319-18491-3_10⟩. ⟨hal-01416219⟩



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