The successful execution of knowledge crowdsourcing (KC) tasks requires contributors to possess knowledge or mastery in a specific domain. The need for expert contributors limits the capacity of online crowdsourcing marketplaces to cope with KC tasks. While online social platforms emerge as a viable alternative source of expert contributors, how to successfully invite them remains an open research question. We contribute an experiment in expert contributors invitation where we study the performance of two invitation strategies: one addressed to the individual expert contributors, and one addressed to communities of knowledge. We target reddit, a popular social bookmarking platform, to seek expert contributors in the botany and ornithology domains of knowledge, and to invite them to contribute an artwork annotation KC task. Results provide novel insights on the effectiveness of direct invitations strategies, but show how soliciting collaboration through communities yields, in the context of our experiment, more contributions.

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This demo presents the CrowdKnowledge Curator (CroKnow), a novel web-based platform that streamlines the processes required to enrich existing knowledge bases (e.g. Wikis) by tapping on the latent knowledge of expert contributors in online platforms. The platform integrates a number of tools aimed at supporting the identification of missing data from existing structured resources, the specification of strategies to identify and invite candidate experts from open communities, and the visualisation of the knowledge creation process status. CroKnow will be demonstrated through a case study focusing on the enrichment of the Rijksmuseum Amsterdams digital collection.

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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.@en

The results of our exploratory study provide new insights to crowdsourcing knowledge intensive tasks. We designed and performed an annotation task on a print collection of the Rijksmuseum Amsterdam, involving experts and crowd workers in the domain-specific description of depicted ow- ers. We created a testbed to collect annotations from ower experts and crowd workers and analyzed these in regard to user agreement. The findings show promising results, demonstrating how, for given categories, nichesourcing can provide useful annotations by connecting crowdsourcing to domain expertise.@en