[Apologies for cross-posting]
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CALL FOR CHALLENGE PARTICIPATION:
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3rd Linked Data Mining Challenge organized in connection with the Know@LOD
2015 workshop at ESWC Conference (ESWC 2015), May 31 - June 4, 2015.
Venue: Portoroz, Slovenia
Date: 01 June 2015
URL:
http://knowalod2015.informatik.uni-mannheim.de/en/linked-data-mining-challen
ge/
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IMPORTANT DATES
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27 March 2015: Submission of papers and solution deadline
03 April 2015: Notification of acceptance
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GENERAL OVERVIEW OF THE CHALLENGE
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Linked data represents a novel type of data source that has been so far
nearly untouched by advanced data mining methods. It breaks down many
traditional assumptions on source data and thus represents a number of
challenges:
- While the individual published datasets typically follow a relatively
regular, relational-like (or hierarchical, in the case of taxonomic
classification) structure, the presence of semantic links among them makes
the resulting 'hyper-dataset' akin to general graph datasets. On the other
hand, compared to graphs such as social networks, there is a larger variety
of link types in the graph.
- The datasets have been published for entirely different purposes, such as
statistical data publishing based on legal commitment of government bodies
vs. publishing of encyclopedic data by internet volunteers vs. data sharing
within a researcher community. This introduces further data modeling
heterogeneity and uneven degree of completeness and reliability.
- The amount and diversity of resources as well as their link sets is
steadily growing, which allows for inclusion of new linked datasets into the
mining dataset nearly on the fly, at the same time, however, making the
feature selection problem extremely hard.
The Linked Data Mining Challenge 2015 (LDMC) will consist of one task, which
is the prediction of the review class of movies.
The best participant in the challenge will be awarded. The ranking of the
participants will be made by the LDMC organizers, taking into account both
the quality of the submitted LDMC paper (evaluated by Know@LOD workshop PC
members) and the prediction quality (i.e., accuracy, see below).
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TASK OVERVIEW
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The task concerns the prediction of a review of movies, i.e., "good" and
"bad". The initial dataset is retrieved from Metacritic, which offers an
average rating of all time reviews for a list of movies. The ratings were
used to divide the movies into classes, i.e., movies with score above 60 are
regarded as "good" movies, while movies with score less than 40 are regarded
as "bad" movies. For each movie we provide the corresponding DBpedia URI.
The mappings can be used to extract semantic features from DBpedia or other
LOD repositories to be exploited in the learning approaches proposed in the
challenge.
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SOURCE DATA, REQUIRED RESULTS, AND EVALUATION
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The dataset is available for download here:
http://knowalod2015.informatik.uni-mannheim.de/en/linked-data-mining-challen
ge/. It consists of training data of 1,600 instances for learning the
predictive models (this data contains the value of the target attribute) and
testing data of 400 instances for evaluating the models (this data is
without the target attribute). The target attribute to be predicted is the
Label attribute.
The datasets contain semicolon separated values: movie's title "Movie",
movie's release date "Release date", movie's DBpedia URI "DBpedia_URI",
movie's label "Label" (only for the training set), and "id". A sample of the
training CSV file is as follows:
"Movie";"Release date";"DBpedia_URI";"Label";"id"
"Best Kept Secret";9/6/13 12:00
AM;"http://dbpedia.org/resource/Best_Kept_Secret_(film)";"good";1.0
The participants have to submit the achieved results on testing data, i.e.
label of the movie. The results have to be delivered in a (syntactically
correct) CSV format that includes the predicted label. The submitted results
will be evaluated on a gold standard with respect to the accuracy.
Beside the CSV file containing the predictions, the participants are
expected to submit a paper describing the used methods and techniques, as
well as the results obtained, i.e., the hypotheses perceived as interesting
either by the computational method or by the participants themselves.
The participants should provide a detailed description of their approach, so
that it can be easily reproduced. For example, it should be clearly stated
what are the used feature sets (and how they have been created), the
preprocessing steps, the type of the predictor, the model parameters' values
and tuning, etc.
The papers will be evaluated by the evaluation panel, both with respect to
the soundness and originality of the methods used and with respect to the
validity of the hypotheses and nuggets found. It should meet the standard
norms of scientific writing.
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ALLOWED DATASETS
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For building the movie review predictor, any dataset that follows the Linked
Open Data principles is allowed to be used.
Non-LOD datasets are allowed to be used only if the participants later
publish those datasets in a way that would make them accessible using some
of the standard Semantic Web technologies, e.g., RDF, SPARQL, etc.
For example, one may map the movies from the dataset to the corresponding
movies in a non-LOD dataset X, allowing to retrieve additional data from the
dataset X. Then, it is expected from the participants to publish the DBpedia
mappings to the dataset X movies, and the additional data retrieved from the
dataset X, for example, using RDF.
*IMPORTANT: Since the Metacritic dataset is publicly available, we kindly
ask the participants not to use the Metacritic movies' rating score to tune
the predictor for the movies in the test set. Any submission found not to
comply with this rule will be disqualified.
However, other information than the movies' rating score retrieved from
Metacritic is allowed, e.g., users' textual reviews for a given movie.
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SUBMISSION PROCEDURE
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Results submission
- Register using the registration web form available at:
http://ldmc15.informatik.uni-mannheim.de/signup
- Build a prediction model on the training set.
- Apply the model on the test set to predict the label.
- Submit the results at: http://ldmc15.informatik.uni-mannheim.de/submit
- Your final score will be the one computed with respect to the last result
submission made before Friday March 27th
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Paper submission
- In addition to your results, you have to submit a paper describing your
solution
- The paper format is Springer LNCS, with a limit of four pages
- Papers are submitted online via Easychair before Friday March 27th
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Presentation of Results
- Challenge papers will be included in the workshop proceedings of Know@LOD
- The authors of the best performing systems will be asked to present their
solution at the workshop
For any questions related to the submission procedure, please address the
contact persons below.
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ORGANIZATION
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Petar Ristoski, University of Mannheim, Germany, petar.ristoski (at)
informatik.uni-mannheim.de
Heiko Paulheim, University of Mannheim, Germany, heiko (at)
informatik.uni-mannheim.de
Vojtěch Svátek, University of Economics, Prague, svatek (at) vse.cz
Václav Zeman, University of Economics, Prague, vaclav.zeman (at) vse.cz
--
Petar Ristoski
Data and Web Science Group
University of Mannheim
Phone: +49 621 181 3705
B6, 26, Room C1.07
D-68159 Mannheim
Mail: <mailto:petar.risto...@informatik.uni-mannheim.de>
petar.risto...@informatik.uni-mannheim.de
Web: dws.informatik.uni-mannheim.de
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