Universidade Federal do Rio de Janeiro COPPE
Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia
Instituto de Matemática
Data: Friday, April 18, 2008 At 08:00
CFP: 18th International Conference on Inductive Logic Programming (ILP 2008)
April 18 (Friday): Abstracts due
April 21 (Monday): Papers due
June 2 (Monday): Notification
June 13 (Friday): Camera ready papers due from authors
September 10-12: Conference
Invited speakers from ILP-inspiring fields:
* Cognitive Science: Josh Tenenbaum, Massachusetts Institute of Technology
* Knowledge Representation: Frank van Harmelen, Vrije Universiteit Amsterdam
* Bioinformatics: Mark Craven, University of Wisconsin in Madison
* Kristian Kersting: Statistical relational learning without tears: an ILP perspective
MLJ special issue:
* Selected papers will be published in a special issue of the Machine Learning Journal
* The best student paper will be awarded a monetary prize sponsored by the Machine Learning journal.
The ILP conference series, started in 1991, is the premier international forum on learning from structured data.
Originally focusing on the induction of logic programs, it broadened its scope and attracted a lot of attention and interest in recent years. In keeping with its tradition, but also reflecting its broadening scope, authors are invited to submit papers presenting original results on all aspects of learning in logic, as well as multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and learning in other non-propositional knowledge representation frameworks.
Typical, but not exclusive, topics of interest for submissions include the following aspects of the above-outlined learning frameworks:
- theoretical aspects: data/model representation frameworks, computational and/or statistical learning theoretic results, specialization and generalization operators, etc.
- algorithmic and implementational aspects including the design of algorithms along with theoretical and/or empirical analysis, scalability issues, pattern and algorithm management issues (such as inductive databases, knowledge discovery workflows), etc.;
- applications including, but not restricted to, multi-relational learning from structured (e.g., labeled graphs, tree patterns) and semi-structured data (e.g., XML documents), in areas of science (bioinformatics, cheminformatics, medical informatics, etc.), natural language processing (computational linguistics, relational text and web mining etc.), engineering or the arts.
Authors are required to submit a paper title and a short abstract of about 200 words before submitting the paper.
Papers must be original (not previously published or under review), written in English and should be between 12 and 18 pages long in Springer's LNCS/LNAI style. The cover page should include the paper title, each author's name, affiliation, and e-mail address, and an abstract of about 200 words. Only electronic submissions in PDF or PostScript file format will be accepted. It is the author's responsibility to ensure that the file is printable. All submissions will be acknowledged shortly after receipt. For more information, please visit the LNCS Information for Authors web page at: www.springer.de/comp/lncs/authors.html
The review process will be double-blind. Submission will be done in two steps. In the first step authors should submit the title of the paper, the complete list of authors, their affiliations and the abstract. The corresponding author will receive an identification code that must be used in the second step of the submission process. In the second step the identification code must be used to upload the full paper. The paper must not have any reference to the authors. Any (bibliographic) references to the authors and their prior work should be disguised by suitable use of third-person form, e.g. use "Prior work by [Ng98] has shown..." instead of "As we have shown in prior work [Ng98] ..."