Handling uncertainty and exploiting structure is fundamental to understanding and designing large-scale systems. Statistical relational learning (SRL) is a novel field of machine learning that applies techniques from probability theory and statistics, on the one hand, while benefitting from advances in logic, database systems, and programming languages. I introduce SRL through two very different approaches. The CLP(BN) language aims at constructing Bayesian Networks with a constraint logic programming framework. CLP(BN) programs are therefore a straightforward extension of logic programming. On the other hand, ProbLog shows a different probabilistic extension of Prolog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these
probabilities are mutually independent. We give application examples for both languages.