Guest editors’ introduction: special issue on Inductive Logic Programming and on Multi-Relational Learning

Abstract

We are delighted to introduce the 2013 special issue on Inductive Logic Programming and Multi-Relational Learning. This issue focuses on the problems in Machine Learning that are specific to data organised as multiple tables. Often this data is structured, and often items in the data-base are interconnected and dependent. Originally based on the induction of logic programs, the area broadened its scope and attracted a lot of attention and interest in recent years. Active topics include 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. The special issue is on the occasion of the 23rd International Conference on Inductive Logic Programming ILP’13, held on August 28–30, 2014 in Rio de Janeiro, Brazil. The ILP conference series, started in 1991, is the premier international forum on learning from structured data. The format of the 2013 conference followed the format of previous editions, with one invited talk per day. followed by long and short oral presentations. This edition of the conference accepted three types of contributions: 1. long papers (12 pages) describing original mature work containing appropriate experimental evaluation and/or representing a self-contained theoretical contribution. 2. short papers (6 pages) 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 above category. 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 journals such as MLJ, DMKD, JMLR, etc. ILP’13 received42 submissions, 18 long, 21 short submissions, and3previously published papers. Each submission was reviewed by at least 3 program committee members. The short