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2nd Call and Prizes Announced: Linked Data Mining Challenge at Know@LOD / ESWC 2014

From: Heiko Paulheim <heiko@informatik.uni-mannheim.de>
Date: Tue, 18 Mar 2014 12:04:07 +0100
Message-ID: <53282827.4020602@informatik.uni-mannheim.de>
To: semantic-web@w3.org, "public-lod@w3.org community" <public-lod@w3.org>, dbpedia-discussion <dbpedia-discussion@lists.sourceforge.net>
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
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
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
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 
Contact persons:
Track A:
Vojtech Svatek, University of Economics, Prague
Jindrich Mynarz, University of Economics, Prague
Track B:
Heiko Paulheim, University of Mannheim, Germany

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: heiko@informatik.uni-mannheim.de
Web: www.heikopaulheim.com
Received on Tuesday, 18 March 2014 11:05:49 UTC

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