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 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.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.