Inductive Logic Programming 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers

This book constitutes the thoroughly refereed post-conference proceedings of the 17th International Conference on Inductive Logic Programming, ILP 2007, held in Corvallis, OR, USA, in June 2007 in conjunction with ICML 2007, the International Conference on Machine Learning.
The 15 revised full papers and 11 revised short papers presented together with 2 invited lectures were carefully reviewed and selected from 38 initial submissions. The papers present original results on all aspects of learning in logic, as well as multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and learning in other non-propositional knowledge representation frameworks. Thus all current topics in inductive logic programming, ranging from theoretical and methodological issues to advanced applications in various areas are covered.

Invited Talks.- Learning with Kernels and Logical Representations.- Beyond Prediction: Directions for Probabilistic and Relational Learning.- Extended Abstracts.- Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract).- Learning Directed Probabilistic Logical Models Using Ordering-Search.- Learning to Assign Degrees of Belief in Relational Domains.- Bias/Variance Analysis for Relational Domains.- Full Papers.- Induction of Optimal Semantic Semi-distances for Clausal Knowledge Bases.- Clustering Relational Data Based on Randomized Propositionalization.- Structural Statistical Software Testing with Active Learning in a Graph.- Learning Declarative Bias.- ILP :- Just Trie It.- Learning Relational Options for Inductive Transfer in Relational Reinforcement Learning.- Empirical Comparison of “Hard” and “Soft” Label Propagation for Relational Classification.- A Phase Transition-Based Perspective on Multiple Instance Kernels.- Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates.- Applying Inductive Logic Programming to Process Mining.- A Refinement Operator Based Learning Algorithm for the Description Logic.- Foundations of Refinement Operators for Description Logics.- A Relational Hierarchical Model for Decision-Theoretic Assistance.- Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming.- Revising First-Order Logic Theories from Examples Through Stochastic Local Search.- Using ILP to Construct Features for Information Extraction from Semi-structured Text.- Mode-Directed Inverse Entailment for Full Clausal Theories.- Mining of Frequent Block Preserving Outerplanar Graph Structured Patterns.- Relational Macros for Transfer in Reinforcement Learning.- Seeing the Forest Through the Trees.- Building Relational World Models for Reinforcement Learning.- An Inductive Learning System for XML Documents.