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The Twelfth International Conference on Inductive Logic Programming was held in Sydney, Australia, July 9–11, 2002. The conference was colocated with two other events, the Nineteenth International Conference on Machine Learning (ICML2002) and the Fifteenth Annual Conference on Computational Learning Theory (COLT2002). Startedin1991,InductiveLogicProgrammingistheleadingannualforumfor researchers working in Inductive Logic Programming and Relational Learning. Continuing a series of international conferences devoted to Inductive Logic Programming and Relational Learning, ILP 2002 was the central event in 2002 for researchers interested in learning relational knowledge from examples. The Program Committee, following a resolution of the Community Me- ing in Strasbourg in September 2001, took upon itself the issue of the possible change of the name of the conference. Following an extended e-mail discussion, a number of proposed names were subjected to a vote. In the?rst stage of the vote, two names were retained for the second vote. The two names were: Ind- tive Logic Programming, and Relational Learning. It had been decided that a 60% vote would be needed to change the name; the result of the vote was 57% in favor of the name Relational Learning. Consequently, the name Inductive Logic Programming was kept.

this Book Constitutes The Thoroughly Refereed Post-proceedings Of The 12th International Conference On Inductive Logic Programming, Ilp 2002, Held In Sydney, Australia In July 2002.

the 22 Revised Full Papers Presented Were Carefully Selected During Two Rounds Of Reviewing And Revision From 45 Submissions. Among The Topics Addressed Are First Order Decision Lists, Learning With Description Logics, Bagging In Ilp, Kernel Methods, Concept Learning, Relational Learners, Description Logic Programs, Bayesian Classifiers, Knowledge Discovery, Data Mining, Logical Sequences, Theory Learning, Stochastic Logic Programs, Machine Discovery, And Relational Pattern Discovery.

The conference now focuses 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. Automatically building conceptual classifications has been the subject of much research during the last twenty years (Michalski 81, German 89, De Raedt 01).