## Inductive Logic Programming: 19th International Conference, ILP 2009, Leuven, Belgium, July 2-4, 2010, Revised PapersThe ILP conference series has been the premier forum for work on logic-based approaches to machine learning for almost two decades. The 19th International Conference on Inductive Logic Programming, which was organized in Leuven, July2-4,2009, continuedthistraditionbutalsoreachedouttoothercommunities as it was colocated with SRL-2009 - the International Workshop on Statistical RelationalLearning, andMLG-2009-the7thInternationalWorkshoponMining andLearningwithGraphs. While thesethreeseriesofeventseachhavetheirown focus, emphasis andtradition, they essentiallysharethe problemthatis studied: learning about structured data in the form of graphs, relational descriptions or logic. The colocation of the events was intended to increase the interaction between the three communities. There was a single program with joint invited and tutorial speakers, a panel, regular talks and poster sessions. The invited speakers and tutorial speakers were James Cussens, Jason Eisner, Jure Leskovec, Raymond Mooney, Scott Sanner, and Philip Yu. The panel featured Karsten Borgwardt, Luc De Raedt, Pedro Domingos, Paolo Frasconi, Thomas Gart ] ner, Kristian Kersting, Stephen Muggleton, and C. David Page. Video-recordings of these talks can be found atwww. videolectures. net. The overall program featured 30 talks presented in two parallel tracks and 53 posters. The talks and posters were selected on the basis of an extended abstract. These abstracts can be found at http: // dtai. cs. kuleuven. be/ilp-mlg-srl/. Inaddition, asinpreviousyears, a- lectionofthepapersofILP2009havebeenpublishedinavolumeintheLectures Notes in Arti?cial Intelligence seriesandinaspecialissueoftheMachine Lea- ing Journal. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

KnowledgeDirected Theory Revision | 1 |

Towards Clausal Discovery for Stream Mining | 9 |

On the Relationship between Logical Bayesian Networks and Probabilistic Logic Programming Based on the Distribution Semantics | 17 |

Induction of Relational Algebra Expressions | 25 |

A LogicBased Approach to Relation Extraction from Texts | 34 |

Discovering Rules by Metalevel Abduction | 49 |

Inductive Generalization of Analytically Learned Goal Hierarchies | 65 |

Ideal Downward Refinement in the EL Description Logic | 73 |

An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge | 149 |

Boosting FirstOrder Clauses for Large Skewed Data Sets | 166 |

Incorporating Linguistic Expertise Using ILP for Named Entity Recognition in Data Hungry Indian Languages | 178 |

Transfer Learning via Relational Templates | 186 |

Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data | 194 |

Finding Relational Associations in HIV Resistance Mutation Data | 202 |

Euclidean Embedding of Coproven Queries | 209 |

Parameter Screening and Optimisation for ILP Using Designed Experiments | 217 |

Nonmonotonic OntoRelational Learning | 88 |

CPLogic Theory Inference with Contextual Variable Elimination and Comparison to BDD Based Inference Methods | 96 |

Speeding Up Inference in Statistical Relational Learning by Clustering Similar Query Literals | 110 |

Acquiring the Rules of Chess Variants through FOL Theory Revision from Examples | 123 |

A System Based on Relative Minimal Generalisation | 131 |

Sampling for ProbabilisticLogic Sequence Models | 226 |

Policy Transfer via Markov Logic Networks | 234 |

Can ILP Be Applied to Large Datasets? | 249 |

257 | |

### Other editions - View all

Inductive Logic Programming: 19th International Conference, ILP 2009, Leuven ... Luc De Raedt No preview available - 2011 |

### Common terms and phrases

abduction Aleph algorithm applied approach ARMGs Artiﬁcial Intelligence atoms AURPC background knowledge Bayesian networks bottom clause BreakAway causal chess chess variant classiﬁers Computer concept confactors CP-theory database Datalog dataset decision tree deﬁned Deﬁnition dependency trees description logics diﬀerent DL+log domain eﬀects eﬃcient ﬁeld ﬁnal ﬁnd ﬁnding ﬁnite ﬁrst ﬁrst-order generalisation goal Golem Heidelberg hexose hypothesis ICL theory identiﬁer ILP system Inductive Logic Programming inference input language literals LNCS LNAI Machine Learning Markov logic networks Markov network method minimal MLN policy MLN Q-function modiﬁed Muggleton mutations negation negative examples nodes ontology parameters performance positive examples PRankBoost.Clause predicate probabilistic logic probability problem ProbLog Progol ProGolem protein Q-value queries Raedt RankBoost reﬁned reﬁnement operator relational relational algebra represent RLGG rules satisﬁed Section semantics skill source-task speciﬁc Springer subsumption target templates theory revision transfer values variables weak learner