Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with models of domains that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational structure. Typically, the knowledge representation formalisms developed in SRL use (a subset of) first-order logic to describe relational properties of a domain in a general manner (universal quantification) and draw upon probabilistic graphical models (such as Bayesian networks or Markov networks) to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s.
CITATION STYLE
De Raedt, L. (2005). Statistical Relational Learning: An Inductive Logic Programming Perspective (pp. 3–5). https://doi.org/10.1007/11564126_3
Mendeley helps you to discover research relevant for your work.