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.
|Title of host publication||Trust Management IX - 9th IFIP WG 11.11 International Conference, IFIPTM 2015, Hamburg, Germany, May 26-28, 2015, Proceedings|
|Editors||Christian Damsgaard Jensen, Stephen Marsh, Theo Dimitrakos, Yuko Murayama|
|Number of pages||15|
|Publication status||Published - 1 May 2015|
|Name||IFIP Advances in Information and Communication Technology|
Nottamkandath, A., Oosterman, J., Ceolin, D., Vries, G. K. D. D., & Fokkink, W. (2015). Predicting Quality of Crowdsourced Annotations Using Graph Kernels. In C. D. Jensen, S. Marsh, T. Dimitrakos, & Y. Murayama (Eds.), Trust Management IX - 9th IFIP WG 11.11 International Conference, IFIPTM 2015, Hamburg, Germany, May 26-28, 2015, Proceedings (Vol. 454, pp. 134-148). (IFIP Advances in Information and Communication Technology). Springer. https://doi.org/10.1007/978-3-319-18491-3_10