The Elsevier Grand Challenge: Knowledge Enhancement in the Life Sciences is a contest created to improve the way scientific information is communicated and used. The contest invites members of the scientific community to describe and prototype a tool to improve the interpretation and identification of meaning in (online) journals and text databases relating to the life sciences. Specifically we are looking for new ways to:
This year's discovery challenge presents two tasks in the new area
of social bookmarking. One task covers spam detection and
the other covers tag recommendations. As we are hosting the social bookmark and
publication sharing system BibSonomy, we are able to provide a dataset
of BibSonomy for the challenge. A training dataset for both tasks is provided at the beginning of the competition.
The test dataset will be released 48 hours before the final deadline. Due to a very tight schedule we cannot grant any deadline
extension.
The presentation of the results will take place at the ECML/PKDD workshop where the top teams are
invited to present their approaches and results.
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
A. Hotho, D. Benz, R. Jäschke, and B. Krause (Eds.) Workshop at 18th Europ. Conf. on Machine Learning (ECML'08) / 11th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'08), (2008)
R. Jäschke, A. Hotho, F. Mitzlaff, and G. Stumme. Recommender Systems for the Social Web, volume 32 of Intelligent Systems Reference Library, Springer, Berlin/Heidelberg, (2012)