Inductive Logic Programming (ILP) is a subfield of machine learning, which relies on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining.
The ILP conference series, started in 1991, is the premier international forum for learning from structured or semi-structured relational data. Originally focusing on the induction of logic programs, over the years it has expanded its research horizon significantly and welcomes contributions to all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches.
- February 13th 2018 - Journal Track deadline has expired. We would like to thank all the authors who submitted their manuscripts. Let's cross our fingers for the responses!
- February 7th 2018 - Journal Track deadline extended till February 12th. Be sure to submit on time!
- February 7th 2018 - Announced invited speakers! Have a look here.
- February 7th 2018 - We are glad to announce two awards for best papers and student best papers.
The awards are sponsored by Springer and Machine Learning Journal.
- January 29th 2018 - Journal Track deadline approaching (February 5th). Be sure to submit on time!
- January 10th 2018 - The Call for Papers for the Conference Track is online.
- October 19th 2017 - The Call for Papers for the Journal Track is online.