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Challenge Entry: EEXCESS – Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources

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Title: EEXCESS – Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources

Short Description

In the last decade, Europe has conducted a tremendous effort to make cultural, educational and scientific resources publicly available. Although such massive amounts of culturally and scientifically rich content are available, the potential of its use for educational and scientific purposes remains largely untapped. One reason can be seen in current web content dissemination mechanisms which are dominated by a small number of large central hubs like major search engines (e.g. Google), social networks (e.g. Facebook) or online encyclopaedias (e.g. Wikipedia). In order to maintain their valuable services, those large hubs have to focus on commercially viable mainstream content. While cultural and scientific resources provide valuable and educational content, they cannot be considered as ‘mainstream’. Quite contrary, most of this can be considered as high-quality niche content for a rather small community and forms part of the so-called Long Tail. The Long Tail theory , first introduced by Chris Anderson, argues that in internet-based markets, niche content adds up to a huge body of knowledge, but is hidden from most users. In the Long Tail, content is maintained and curated by a large number of small to medium-sized institutions such as memory organisations (e.g. archives and museums), national and digital libraries and open educational repositories. However, the few large Web hubs hardly support the dissemination of this Long Tail content leaving a gap for bringing cultural and scientific wealth into educational and scientific processes.

Long Description

Towards Long Tail Content for the Masses

In order to reshape dissemination mechanisms for highly specialised Long Tail content, EEXCESS introduces the idea of augmenting existing web channels with high quality content through personalised, contextualised and privacy preserving, federated recommendations. In order to communicate the knowledge contained in the content, EEXCESS researches visualisation and interaction techniques for presenting recommendation results. The main underlying concept is to bring the content to the user, i.e. injecting content into channels habitually visited by users, instead of bringing the user to the content, i.e. creating additional portals that compete for user attention in the Long Tail.

In order to realise new content dissemination mechanisms, EEXCESS’s primary research goal is to research and develop an extensible, open source framework to dynamically augment the World Wide Web ecosystems with high-quality, personalized recommendations based on dynamically integrated and socially enriched cultural, scientific and educational resources. Our application partners, consisting of niche content providers, aggregators and hubs to science, education and the general public will be the first ones incubating this framework. Clearly, establishing this new content dissemination mechanism needs to be implemented by attracting new potential application partners which are not part of the EEXCESS consortium. At the end we hope to unfold the treasure of cultural, scientific and educational long-tail content for the benefit of all users.

Comparable to existing frameworks for publishing cultural content like DSPACE or omeka , EEXCESS will become the framework for disseminating this content. For realizing such a system, we have defined five research objectives to realize the EEXCESS vision:

Objective 1 – Adaptive Augmentation User Interfaces will research and develop intelligent, adaptive visual interfaces for content consumption and content authoring. Such interfaces will be based on knowledge visualization principles and allow for example the automatic creation of visually appealing visualisations, reveal relations among interlinked data sets (e.g. historically) or support users during content creation processes.

Objective 2 – Personalized Recommendation will research and develop decentralized, personalized and contextualized recommendation technology capable to scale to large numbers of users. Personalization and contextualization targets the satisfaction of EEXCESS’s users, while decentralization focuses on the potential uptake of the EEXCESS framework by different organizations.

Objective 3 – Integration and Enrichment will research and develop adaptive data harmonization and enrichment technologies for cultural, scientific and educational resources, which improve through usage. Data integration will utilize Linked Data principles and – due to its adaptive nature – be capable of adapting to different use cases and domain.

Objective 4 – User and Usage Mining will research and develop algorithms and services to learn from user interactions and usage behaviour on how to best fulfil a users information need. This includes analysing the long and short-term context of a user and automatically requesting relevant resources through the personalized recommendation mechanisms. The developed methods will give users full privacy control.

Objective 5 – Privacy-Preservation will research and develop methodologies and technologies for guaranteeing privacy-preserving augmentation, recommendation, and user mining.

EEXCESS partners

The EEXCESS consortium consists of a sensible combination of scientific and application partners. All partners have manifested their strong commitment to this project proposal and treat it with highest priority. Two strong points make up the uniqueness of the EEXCESS consortium, namely the high scientific and technological expertise and research experience of the scientific partners and the high-impact, wide range of the application partners.

The application partners provide a wide range of test bed opportunities in the cultural, scientific and educational domain ranging from regional museums and educational providers over world leading digital libraries to organisations disseminating knowledge to the general public and large scale aggregators.


  1. Gerhard Paar

    April 23, 2015

    It could have made sense to highlight the differences to Google, Wikipedia etc. also in the Video.

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