## Introduction to Statistical Relational Learning
Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In |

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### Contents

1 Introduction | 1 |

2 Graphical Models in a Nutshell | 13 |

3 Inductive Logic Programming in a Nutshell | 57 |

4 An Introduction to Conditional Random Fields for Relational Learning | 93 |

5 Probabilistic Relational Models | 129 |

6 Relational Markov Networks | 175 |

7 Probabilistic EntityRelationship Models PRMs and Plate Models | 201 |

8 Relational Dependency Networks | 239 |

Probabilistic Models with Unknown Objects | 373 |

A GeneralPurpose Probabilistic Language | 399 |

15 Lifted FirstOrder Probabilistic Inference | 433 |

16 Feature Generation and Selection in MultiRelational Statistical Learning | 453 |

With an Application in Mammography | 477 |

A PolicyLanguage Approach | 499 |

19 Statistical Relational Learning for Natural Language Information Extraction | 535 |

20 Global Inference for Entity and Relation Identification via a Linear Programming Formulation | 553 |