Introduction to Statistical Relational Learning Edited by Lise Getoor and Ben Taskar Published by The MIT Press 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 Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout. |

Table of Contents

- Introduction

*Lise Getoor, Ben Taskar* - Graphical Models in a Nutshell

*Daphne Koller, Nir Friedman, Lise Getoor, Ben Taskar* - Inductive Logic Programming in a Nutshell

*Saso Dzeroski* - An Introduction to Conditional Random Fields for Relational Learning

*Charles Sutton, Andrew McCallum* - Probabilistic Relational Models

*Lise Getoor, Nir Friedman, Daphne Koller, Avi Pfeffer, Ben Taskar* - Relational Markov Networks

*Ben Taskar, Pieter Abbeel, Ming-Fai Wong, Daphne Koller* - Probabilistic Entity-Relationship Models, PRMs, and Plate Models

*David Heckerman, Chris Meek, Daphne Koller* - Relational Dependency Networks

*Jennifer Neville, David Jensen* - Logic-based Formalisms for
Statistical Relational Learning

*James Cussens* - Bayesian Logic Programming: Theory and Tool

*Kristian Kersting, Luc De Raedt* - Stochastic Logic Programs: A Tutorial

*Stephen Muggleton, Niels Pahlavi* - Markov Logic: A Unifying Framework for Statistical Relational Learning

*Pedro Domingos, Matthew Richardson* - BLOG: Probabilistic Models with Unknown Objects

*Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong, Andrey Kolobov* - The Design and Implementation of IBAL: A General-Purpose Probabilistic
Language

*Avi Pfeffer* - Lifted First-Order Probabilistic Inference

*Rodrigo de Salvo Braz, Eyal Amir, Dan Roth* - Feature Generation and Selection in Multi-Relational Statistical Learning

*Alexandrin Popescul, Lyle H. Ungar* - Learning a New View of a Database: With an Application in Mammography

*Jesse Davis, Elizabeth Burnside, Ines Dutra, David Page, Raghu Ramakrishnan, Jude Shavlik, Vitor Santos Costa* - Reinforcement Learning in Relational
Domains: A Policy-Language Approach

*Alan Fern, SungWook Yoon, Robert Givan* - Statistical Relational Learning for Natural Language Information Extraction

*Razvan C. Bunescu, Raymond J. Mooney* - Global Inference for Entity and Relation
Identification via a
Linear Programming Formulation

*Dan Roth, Wen-tau Yih*

Software and Data:

Courses:

- Statistical Relational Learning and Link Mining at UMD
- Statistical Relational Learning at Purdue University
- Artificial Intelligence II: Statistical Relational Learning at University of Washington
- Statistical Relational Learning at University of Wisconsin
- Advanced methods in artificial intelligence, with biomedical applications at University of Wisconsin

Workshops and Meetings:

- Learning Statistical Models from Relational Data (SRL2000)
- Learning Statistical Models from Relational Data (SRL2003)
- Statistical Relational Learning and its Connections to Other Fields (SRL2004)
- Probabilistic, Logical and Relational Learning - Towards a Synthesis (Dagstuhl 2005)
- Open Problems in Statistical Relational Learning (SRL 2006)
- Probabilistic, Logical and Relational Learning - A Further Synthesis (Dagstuhl 2007)