A Survey on Statistical Relational Learning

The vast majority of work in Machine Learning has focused on propositional data which is assumed to be identically and independently distributed, however, many real world datasets are relational and most real world applications are characterized by the presence of uncertainty and complex relational structure where the data distribution is neither identical nor independent. An emerging research area, Statistical Relational Learning(SRL), attempts to represent, model, and learn in relational domain. Currently, SRL is still at a primitive stage in Canada, which motivates us to conduct this survey as an attempt to raise more attention to this field. Our survey presents a brief introduction to SRL and a comparison with conventional learning approaches. In this survey we review four SRL models(PRMs, MLNs, RDNs, and BLPs) and compare them theoretically with respect to their representation, structure learning, parameter learning, and inference methods. We conclude with a discussion on limitations of current methods.