## 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 Lise Getoor is Assistant Professor in the Department of Computer Science at the University of Maryland. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania. |

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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 |