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

Paolo Frasconi (auth.), Hendrik Blockeel, Jan Ramon, Jude Shavlik, Prasad Tadepalli (eds.)3540784683, 9783540784685

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.

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Table of contents :
Front Matter….Pages –
Learning with Kernels and Logical Representations….Pages 1-3
Beyond Prediction: Directions for Probabilistic and Relational Learning….Pages 4-21
Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract)….Pages 22-23
Learning Directed Probabilistic Logical Models Using Ordering-Search….Pages 24-24
Learning to Assign Degrees of Belief in Relational Domains….Pages 25-26
Bias/Variance Analysis for Relational Domains….Pages 27-28
Induction of Optimal Semantic Semi-distances for Clausal Knowledge Bases….Pages 29-38
Clustering Relational Data Based on Randomized Propositionalization….Pages 39-48
Structural Statistical Software Testing with Active Learning in a Graph….Pages 49-62
Learning Declarative Bias….Pages 63-77
ILP :- Just Trie It….Pages 78-87
Learning Relational Options for Inductive Transfer in Relational Reinforcement Learning….Pages 88-97
Empirical Comparison of “Hard” and “Soft” Label Propagation for Relational Classification….Pages 98-111
A Phase Transition-Based Perspective on Multiple Instance Kernels….Pages 112-121
Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates….Pages 122-131
Applying Inductive Logic Programming to Process Mining….Pages 132-146
A Refinement Operator Based Learning Algorithm for the $mathcal{ALC}$ Description Logic….Pages 147-160
Foundations of Refinement Operators for Description Logics….Pages 161-174
A Relational Hierarchical Model for Decision-Theoretic Assistance….Pages 175-190
Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming….Pages 191-199
Revising First-Order Logic Theories from Examples Through Stochastic Local Search….Pages 200-210
Using ILP to Construct Features for Information Extraction from Semi-structured Text….Pages 211-224
Mode-Directed Inverse Entailment for Full Clausal Theories….Pages 225-238
Mining of Frequent Block Preserving Outerplanar Graph Structured Patterns….Pages 239-253
Relational Macros for Transfer in Reinforcement Learning….Pages 254-268
Seeing the Forest Through the Trees….Pages 269-279
Building Relational World Models for Reinforcement Learning….Pages 280-291
An Inductive Learning System for XML Documents….Pages 292-306
Back Matter….Pages –