In Introduction to Statistical Relational Learning, leading researchers in this area comes from the machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about rich structured systems and data. Early chapters provide tutorials for material used in the 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 model and logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs.