ICML-2007 TUTORIAL ON PRACTICAL STATISTICAL RELATIONAL LEARNING http://www.cs.washington.edu/homes/pedrod/psrl.html
Statistical relational learning (SRL) focuses on learning when samples are non-i.i.d. (independent and identically distributed). Domains where data is non-i.i.d. are widespread; examples include Web search, information extraction, perception, medical diagnosis/epidemiology, molecular and systems biology, social science, security, ubiquitous computing, and others. In all of these domains, modeling dependencies between examples can greatly improve predictive performance, and lead to better understanding of the relevant phenomena. However, doing this can be much more complex than treating examples independently. The goal of this tutorial is to provide researchers and practitioners with the tools needed to learn from interdependent examples with no more difficulty than they learn from isolated examples today. We begin with an introduction to the four foundational areas of SRL: logical inference, inductive logic programming, probabilistic inference, and statistical learning. We then show how to combine them in a principled and efficient way and survey major approaches, using Markov logic as the foundation. Finally, we show how to apply these techniques to a wide variety of problems, using the Alchemy open-source software as a concrete tool.
