Getoor L., Taskar B. (Eds.) Introduction to Statistical Relational Learning

Massachusetts Institute of Technology, Adaptive Computation and Machine Learning series, 2007.
— 608 p. — ISBN: 0262072882, 978-0262072885.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.Graphical Models in a Nutshell
Inductive Logic Programming in a Nutshell
An Introduction to Conditional Random Fields for Relational Learning
Probabilistic Relational Models
Relational Markov Networks
Probabilistic Entity-Relationship Models, PRMs, and Plate Models
Relational Dependency Networks
Logic-based Formalisms for Statistical Relational Learning
Bayesian Logic Programming: Theory and Tool
Stochastic Logic Programs: A Tutorial
Markov Logic: A Unifying Framework for Statistical Relational Learning
BLOG: Probabilistic Models with Unknown Objects
The Design and Implementation of IBAL: A General-Purpose Probabilistic Language
Lifted First-Order Probabilistic Inference
Feature Generation and Selection in Multi-Relational Statistical Learning
Learning a New View of a Database: With an Application in Mammography
Reinforcement Learning in Relational Domains: A Policy-Language Approach
Statistical Relational Learning for Natural Language Information Extraction
Global Inference for Entity and Relation Identification via a Linear Programming Formulation