NEW: Deadline extended

NEW: Journal Paper Publication

SAC 2009

For additional information, visit the SAC 2009 web page

Special Track on Relational Learning

Relational Learning (RL) algorithms allow the use of more complex representations for both data and models when compared with traditional algorithms that use attribute-value representations. The difference in the complexity of the representation makes RL algorithms applicable to a wider range of applications but also rises new problems. It is an important topic for data analysis in any field that processes structured data an requires complex models. Problems of scalability and efficient implementations are critical for RL algorithms.

The track aims at providing a forum for researchers and practitioners to exchange findings and review knowledge and understanding on topics related to Relational Learning. It will be also an opportunity to present novel applications and new RL systems.

As part of the SAC 2009 track on Relational Learning, we solicit original research papers in all areas of Relational Learning with a special interest in the following topics:

• Applications of Relational Learning (bioinformatics, drug design, text-mining, ...)
• Distributed Computing for Relational Learning
• Efficiency issues of Relational Learning
• Graph Mining
• Implementation issues of relational learning
• Inductive Logic Programming
• New Relational Learning algorithms
• Propositionalization methods
• Statistical Relational Learning

Important dates

Journal Publication After the conference, the best papers will be selected to be extended and published in the Intelligent Data Analysis (IDA) journal. The selection of the track papers will be done first by the Track organizers and then by IDA editorial board members.

Organization

Rui Camacho, LIAAD, Universidade do Porto, Portugal
rcamacho at fe.up.pt
Vitor Santos Costa, CRACS, Universidade do Porto, Portugal
vsc at dcc.fc.up.pt
Nuno Fonseca, IBMC, Universidade do Porto, Portugal
nf at ibmc.up.pt

Submission and guidelines

Papers should respect the ACM SAC format as specified in: http://www.acm.org/conferences/sac/sac2009. Submission of papers is made using the SAC submission web page.

According to the conference reviewing rules, all papers will be evaluated by at least three reviewers following a blind review process (reviewers will be blind to the identities of the authors and their institutions). The author(s) name(s) and address(es) must NOT appear in the body of the paper, and self-reference should be in the third person. This is to facilitate blind review. Only the title should be shown at the first page without the author's information.

Papers submitted for reviews should not be more than 5000 words long, and may not be more than 8 ACM style pages in final format, or more that 15 pages long in 11 pt submission/review format (11-point font, double space and 1 inch margins on all four sides on letter size paper). But if the paper is more than 5 pages in ACM format, authors should keep in mind that reviewers may reject the paper if they think that the readability will suffer if the paper is reduced to 5 free pages generally allowed in the proceedings, or maximum 8 pages with the purchase of maximum three additional pages. Papers that fail to comply with length limitations risk rejection.

Please note that hard copy and fax submissions will not be accepted. Additional pages up to a total of 8 pages can be purchased at ACM standard rate. Consequently, final camera ready papers can be up to 8 pages long with the understanding that the authors will reduce the paper to comply with the length restriction, or purchase additional pages.

All enquiries and questions should be directed to the Track Chairs.

Programme Committee

Hendrik Blockeel	(Katholieke Universiteit Leuven, Belgium)
Rui Camacho	(University of Porto, Portugal)
Jesse Davis	(University of Washington, USA)
Ines Dutra	(University of Porto, Portugal)
Nuno Fonseca	(University of Porto, Portugal)
Paolo Frasconi	(Universit degli Studi di Firenze, Italy)
Kristian Kersting	(Fraunhofer IAIS, Germany)
Ross King	(University of Aberystwyth, UK)
Stefan Kramer	(Technische Universitaet Muenchen, Germany)
Siegfried Nijssen	(Katholieke Universiteit Leuven, Belgium)
Irene Ong	(University of Wisconsin, USA)
Vitor Santos Costa	(University of Porto, Portugal)
Jude W. Shavlik	(University of Wisconsin, USA)
Fernando Silva	(University of Porto, Portugal)
Ashwin Srinivasan	(IBM India Research Lab, India)
Jan Struyf	(Katholieke Universiteit Leuven, Belgium)
Gerson Zavarucha	(Federal University of Rio de Janeiro, Brasil)
Filip Zelezny	(Czech Technical University in Prague, Czech Republic)