Chris Dijkshoorn

Crowdsourcing knowledge-intensive tasks in cultural heritage

Large datasets such as Cultural Heritage collections require detailed annotations when digitised and made available online. Annotating different aspects of such collections requires a variety of knowledge and expertise which is not always possessed by the collection curators. Artwork annotation is an example of a knowledge intensive image annotation task, i.e. a task that demands annotators to have domain-specific knowledge in order to be successfully completed. This paper describes the results of a study aimed at investigating the applicability of crowdsourcing techniques to knowledge intensive image annotation tasks. We observed a clear relationship between the annotation difficulty of an image, in terms of number of items to identify and annotate, and the performance of the recruited workers.

Using Linked Data to Diversify Search Results a Case Study in Cultural Heritage

In this study we consider wether, and to what extent, additional semantics in the form of Linked Data can help diversifying search results. We undertake this study in the domain of cultural heritage. The data consists of collection data of the Rijksmuseum Amsterdam together with a number of relevant external vocabularies, which are all published as Linked Data. We apply an existing graph search algorithm to this data, using entries from the museum query log as test set. The results show that in this domain an increase in diversity can be achieved through adding external vocabularies. We also analyse why some vocabularies have a significant effect, while others influence the results only marginally.

The Rijksmuseum collection as Linked Data

Many museums are currently providing online access to their collections. The state of the art research in the last decade shows that it is beneficial for institutions to provide their datasets as Linked Data in order to achieve easy cross-referencing, interlinking and integration. In this paper, we present the Rijksmuseum linked dataset (accessible at http://datahub.io/dataset/rijksmuseum), along with collection and vocabulary statistics, as well as lessons learned from the process of converting the collection to Linked Data. The version of March 2016 contains over 350,000 objects, including detailed descriptions and high-quality images released under a public domain license.

Crowd vs. experts: nichesourcing for knowledge intensive tasks in cultural heritage

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 flowers. We created a testbed to collect annotations from flower 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.

Grasping the Long Tail: Personalized Search for Cultural Heritage Annotators.

The social web is having a clear impact in our field of user modeling and adaptation. ‘Links’ and ‘Likes’ as well as ‘Followers’ and ‘Friends’ are part of a large source of data that is generated by users themselves, often for different purposes, and that provides an unprecedented potential for systems to understand their users and to adapt based on that understanding. As we can see from researchers and projects in a number of relevant fields, data on various manifestations of what users do socially on the web brings new opportunities. Exciting ideas are generated and first explorations show promising results. In this talk we take a look back at recent proposals and studies that consider the social web. We determine interesting patterns and we aim to understand the impact on methods and techniques for user modeling and adaptation. At the same time, the social web brings even more challenges. We look forward by identifying challenges that can drive our research. From technical challenges to explore the different social web sources to social challenges to understand how users behave when this potential is unlocked.

Grasping the Long Tail: Personalized Search for Cultural Heritage Annotators

Online collections of museums are often hard to access, because the artworks lack appropriate annotations. We develop a framework that supports niches of experts in the crowd in adding annotation of high quality. This thesis focuses on search strategies that match experts with artworks to annotate. Our approach uses explicit semantics for modeling the relations between the properties of the collection items, content-based filtering aimed at diversification, and trust-aware ranking of the results.