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. Authors are invited to submit papers presenting original results on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data.

Typical, but not exclusive, topics of interest for submissions include:

• theoretical aspects: learning scenarios, data/model representation frameworks, their computational and/or statistical properties, etc.
• algorithmic and implementation aspects: sclability, efficiency, parallelism, management of algorithms and/or discovered patterns, discovery workflows, etc.
• applications of learning from relational data in areas of science (bioinformatics, cheminformatics, medical informatics, etc.), natural language processing (computational linguistics, text and web mining etc.), engineering, the arts, etc.

We solicit three kinds of papers:

1. Long papers describing original mature work containing appropriate experimental evaluation and/or representing a self-contained theoretical contribution. Long papers will be reviewed by 3 members of the program committee. Authors will be notified prior to the conference on acceptance/rejection for the Springer post-conference proceedings. Authors of accepted papers will be assigned a standard time slot for presentation.
2. Short papers describing original work in progress, brief accounts of original ideas without conclusive experimental evaluation, and other relevant work of potentially high scientific interest but not yet qualifying for the long paper category. The PC chairs will accept/reject short papers on the grounds of relevance. Authors of accepted short papers will be assigned a reduced time slot for presentation. Each short paper will be reviewed by 3 members of the program committee on the basis of both the manuscript and its presentation, and the authors of selected papers will be invited to submit a long version for the Springer post-conference proceedings; the paper will be finally accepted if satisfactorily addressing the reviewer’s requirements.
3. Papers relevant to the conference topics and recently published or accepted for publication by a first-class conference such as ECML/PKDD, ICML, KDD, ICDM etc. or journal such as MLJ, DMKD, JMLR etc. The PC chairs will accept/reject such papers on the grounds of relevance and quality of the original publication venue. Authors of accepted papers will be assigned a reduced time slot for presentation. These papers will not appear in the Springer post-conference proceedings.

Submissions in category 1 or 2 must not have been published or be under review for a journal or for another conference with published proceedings. They should be submitted in the Springer LNCS format. Long (short) papers must not exceed 12 (6) pages. Papers in category 3 should be submitted in their original format and the authors should indicate the original publication venue.

A special issue of the Machine Learning journal is planned following the conference, with papers selected by the PC chairs from all the three categories above, significantly revised and/or extended to meet the MLJ criteria, and re-reviewed by the PC.