2nd Call for Challenge Participation: 2nd Linked Data Mining Challenge
organized in connection with the Know@LOD 2014 workshop at ESWC 2014,
May 25, Crete, Greece
http://knowalod2014.informatik.uni-mannheim.de/en/linked-data-mining-challenge/
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New - Prizes announced!
* The best result in the predictive task will be awarded by a licence to
RapidMiner Studio Professional Edition (with catalog price about $3000),
thanks to the LDMC sponsor - RapidMiner, Inc.
* The best LDMC paper will be awarded by an Amazon voucher worth 500
EUR, thanks to the LDMC sponsor - EU LOD2 project
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Submission dates (other dates available from the website):
31 March 2014: Submission deadline for predictive task results
3 April 2014: Submission deadline for LDMC papers
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The Linked Data Mining Challenge (LDMC) will consist of two tracks, each
with a different domain and dataset. It is possible to participate in a
single track or in both tracks.
Track A addresses linked government data, more specifically, the public
procurement domain. Data from this domain are frequently analyzed by
investigative journalists and ‘transparency watchdog’ organizations;
these, however, 1) rely on interactive tools such as OLAP and
spreadsheets, incapable of spotting hidden patterns, and 2) only deal
with isolated datasets, thus ignoring the potential of interlinking to
external datasets. LDMC could possibly initiate a paradigm shift in
analytical processing of this kind of data, eventually leading to
large-scale benefits to the citizenship. It is also likely to spur the
research collaboration between the Semantic Web community (represented
by the linked data sub-community as its practice-oriented segment) and
the Data Mining community.
Track B addresses the domain of scientific research collaboration, in
particular cross-disciplinary collaboration. While collaboration between
people within the same community often emerges naturally, many possible
cross-disciplinary collaborations never form due to a lack of awareness
of cross-boundary synergies. Finding meaningful patterns in
collaborations can help revealing potential cross-disciplinary
collaborations that might otherwise have remained hidden.
Each track requires the participants to download a real-world RDF
dataset and accomplish at least one pre-defined task on it using their
own or publicly available data mining tool. The tracks involve 1
predictive and 2 exploratory data mining tasks in total.
Partial mapping to external datasets is also available, which allows for
extraction of further features from the Linked Open Data cloud in order
to augment the core dataset.
The best participant in each track will be awarded. The ranking of the
participants will be made by the LDMC evaluation panels, and will take
into account both the quality of the submitted LDMC paper and the
prediction quality measure in the predictive task (Track A only, if
addressed by the participant).
More detail on the datasets, tasks, results/paper submission and
evaluation is in
http://knowalod2014.informatik.uni-mannheim.de/en/linked-data-mining-challenge/
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Contact persons:
Track A:
Vojtech Svatek, University of Economics, Prague
Jindrich Mynarz, University of Economics, Prague
Track B:
Heiko Paulheim, University of Mannheim, Germany
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Dr. Heiko Paulheim
Research Group Data and Web Science
University of Mannheim
Phone: +49 621 181 2646
B6, 26, Room C1.08
D-68159 Mannheim
Mail: [email protected]
Web: www.heikopaulheim.com
