Reflecting the original idea, the workshop is focused mainly on inductive logic programming and multi-relational data mining. Authors are invited to submit papers presenting results on theoretical, algorithmic and implementation aspects of relational learning methods, such as

• inductive logic programming
• classification and regression trees
• association rules

Authors are invited to submit an extended abstract, first. Abstracts will be reviewed by the program committee. Accepted papers will be presented on the workshop. After the workshop autors of accepted papers will be invited to submit a full paper. The full papers will be reviewed again by program committee.

Accepted full papers will be published in the post-workshop proceedings with ISBN. Assuming that the workshop proceedings can be further used as a study material, the papers must include good introductory parts and detailed discussions of related works. Papers must be written in English. Authors are expected to use the Springer's lncs style.

Accepted papers

Alan Eckhardt, Peter Vojtáš: Indukce uživatelských preferencí s využitím ontologií a pravděpodobnosti.

Ján Rauch: GUHA Method and Relational Data Mining.

Radek Jun: Background Knowledge Formalization and Visualization.

Martin Ralbovský, Tomáš Kuchař, Alexander Kuzmin, Daniel Kupka: Současný výzkum metody GUHA a projekt Ferda.

Filip Železný, Monika Žáková: Využití subsumpční relace v propozičních algoritmech učení.

Tomáš Horváth, Peter Vojtáš: Fuzzy Inductive Logic Programming: Approaches and Problems.

Jan Blaťák, Luboš Popelínský: Časté vzory v predikátové logice.