## Inductive Logic Programming: 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected PapersILP 2007, the 17th Conference on Inductive Logic Programming, was held in Corvallis, Oregon, USA, June 19–21, and was collocated with the 24th Inter- tional Conferenceon Machine Learning.The programconsisted of 15 full and 14 short presentations, a poster session, keynote talks by Paolo Frasconi (Learning withKernelsandLogicalRepresentations)andDavidJensen(BeyondPrediction: Directions for Probabilistic and Relational Learning), and several joint sessions with ICML. Thirty-eight submissions were received this year, out of which ?fteen were accepted for publication in the proceedings as full papers and eleven as short papers.Inclusionin the proceedings was decided bytaking into accountnotonly the relevance and quality of the work described, but also the quality and level of maturityofthetext.Severalmoresubmissionswereacceptedaswork-in-progress presentations. Thus the 2007 edition of ILP continued the tradition of adopting high selectivity for published papers, while at the same time o?ering a forum for work in progress. All accepted papers were made available in temporary online proceedings during the conference. Revised versions of the submitted papers, incorporating feedback from discussions at the conference, are included either in the proce- ings of the conference (this volume) or, for a small number of selected papers, in a special issue of theMachine Learning journal (abstracts of these are included in this volume). Papers reporting on work in progress remain available in the online proceedings, athttp://pages.cs.wisc.edu/~shavlik/ilp07wip/. |

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### Contents

Learning with Kernels and Logical Representations | 1 |

Directions for Probabilistic and Relational Learning | 4 |

Learning Probabilistic Logic Models from Probabilistic Examples Extended Abstract | 22 |

OrderingSearch | 24 |

Learning to Assign Degrees of Belief in Relational Domains | 25 |

BiasVariance Analysis for Relational Domains | 27 |

Induction of Optimal Semantic Semidistances for Clausal Knowledge Bases | 29 |

Clustering Relational Data Based on Randomized Propositionalization | 39 |

Applying Inductive Logic Programming to Process Mining | 132 |

A Reﬁnement Operator Based Learning Algorithm for the ALC Description Logic | 147 |

Foundations of Reﬁnement Operators for Description Logics | 161 |

A Relational Hierarchical Model for DecisionTheoretic Assistance | 175 |

Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming | 191 |

Revising FirstOrder Logic Theories from Examples Through Stochastic Local Search | 200 |

Using ILP to Construct Features for Information Extraction from Semistructured Text | 211 |

ModeDirected Inverse Entailment for Full Clausal Theories | 225 |

Structural Statistical Software Testing with Active Learning in a Graph | 49 |

Learning Declarative Bias | 63 |

Just Trie It | 78 |

Learning Relational Options for Inductive Transfer in Relational Reinforcement Learning | 88 |

Empirical Comparison of Hard and Soft Label Propagation for Relational Classiﬁcation | 98 |

A Phase TransitionBased Perspective on Multiple Instance Kernels | 112 |

with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates | 122 |

Mining of Frequent Block Preserving Outerplanar Graph Structured Patterns | 239 |

Relational Macros for Transfer in Reinforcement Learning | 254 |

Learning a Comprehensible Model from a First Order Ensemble | 269 |

Building Relational World Models for Reinforcement Learning | 280 |

An Inductive Learning System for XML Documents | 292 |

Author Index | 307 |

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action Aleph AMBIL approach Artiﬁcial Intelligence background knowledge Bayesian network Berlin Heidelberg 2008 bias block tree pattern Blockeel bpo-graph pattern causal classiﬁcation clustering complete computed concept constraints construct dataset decision tree deﬁned Deﬁnition denoted description logics diﬀerent disjunct domain eﬀective eﬃcient ensemble evaluate feasible paths ﬁnd ﬁnding ﬁnite ﬁrst ﬁrst-order ﬂow goal graph pattern Heidelberg hierarchical iﬀ ILP system Inductive Logic Programming inference inﬂuence input instances kernel labeled language learning algorithm literals LNCS LNAI Machine Learning macro MDIE methods negation normal form negative examples node optimal option outerplanar graph parameters performance positive examples predicate preimage probabilistic probability problem propositional Q-learning QEDs random reﬁnement operators reinforcement learning relational learning representation revision RoboCup rules satisﬁability score search space Section Software Testing speciﬁc Springer stochastic structure Support Vector Machines task theory training set transfer user’s values variables

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Page 306 - In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing (EMNLP-2), 1998.

Page 278 - Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT), the Fund for Scientific Research-Flanders (FWO-Flanders), the Belgian Federal Science Policy Office (BFSPO), and the European Union.