Semantic Web Dog Food

Presented at: Social Data on the Web (SDoW2009)

by Chris van Aart, Ronald Siebes, Vicky Buser, Lora Aroyo, Yves Raimond, Dan Brickley, Guus Schreiber, Michele Minno, Libby Miller, Davide Palmisano, Michele Mostarda

In this paper we present our current experience of aggregating user data from various Social Web applications and outline several key challenges in this area. The work is based on a concrete use case: reusing activity streams to determine a viewer's interests and generating television programme recommendations from these interests. Three system components are used to realise this goal: (1) an intelligent remote control: iZapper for capturing viewer activities in a cross-context television environment; (2) a backend: BeanCounter for aggregation of viewer activities from the iZapper and from different social web applications; and (3) a recommendation engine: iTube for recommending relevant television programmes. The focus of the paper is the BeanCounter as the first step to apply Social Web data for viewer and context modelling on the Web. This is work in progress of the NoTube project.

Resource URI on the dog food server: http://data.semanticweb.org/workshop/sdow/2009/paper/1

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