The ILP conference series, started in 1991, is the premier international forum for 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 data mining and learning, statistical relational learning, graph and tree mining, and learning in other (non-propositional) logic-based knowledge representation frameworks.


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

* theoretical aspects (logical foundations, computational and/or statistical learning theory, specialization and generalization operators, etc.) of learning in logic (logic programs, constraint logic programs, Datalog, first-order logic, description logics, higher-order logic, etc.), or from relational or graph databases
* algorithmic and implementation aspects of learning in logic including the design of algorithms along with theoretical and/or empirical analysis, probabilistic and statistical approaches, distance and kernel-based methods, relational reinforcement learning, learning from multi-relational databases, scalability issues, inductive databases, link discovery, multi-instance learning, 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.