- From: Heiko Paulheim <heiko@informatik.uni-mannheim.de>
- Date: Tue, 18 Mar 2014 12:04:07 +0100
- 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 http://knowalod2014.informatik.uni-mannheim.de/en/linked-data-mining-challenge/ ----- 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 http://knowalod2014.informatik.uni-mannheim.de/en/linked-data-mining-challenge/ ----- 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 11:05:55 UTC