Elsevier’s Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.
Elsevier’s Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.
With the Web serving as a huge worldwide data repository, issues related to data semantics (familiar to database modelers since the 1970s) have again become of paramount importance. As Web data comes from heterogeneous, possibly ...
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
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.
D. Schlör, J. Pfister, and A. Hotho. 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), page 136–141. New York, NY, USA, Association for Computing Machinery, (2023)
D. Wangsadirdja, J. Pfister, K. Kobs, and A. Hotho. Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), page 1090--1095. Toronto, Canada, Association for Computational Linguistics, (July 2023)
A. Dulny, A. Hotho, and A. Krause. Machine Learning and Knowledge Discovery in Databases: Research Track, page 438--455. Cham, Springer Nature Switzerland, (2023)