Statistical relational learning


Statistical relational learning is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty and complex, relational structure.
Typically, the knowledge representation formalisms developed in SRL use first-order logic to describe relational properties of a domain in a general manner and draw upon probabilistic graphical models 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.
As is evident from the characterization above, the field is not strictly limited to learning aspects; it is equally concerned with reasoning and knowledge representation. Therefore, alternative terms that reflect the main foci of the field include statistical relational learning and reasoning and first-order probabilistic languages.
Another term that is sometimes used in the literature is relational machine learning.

Canonical tasks

A number of canonical tasks are associated with statistical relational learning, the most common ones being.
One of the fundamental design goals of the representation formalisms developed in SRL is to abstract away from concrete entities and to represent instead general principles that are intended to be universally applicable. Since there are countless ways in which such principles can be represented, many representation formalisms have been proposed in recent years. In the following, some of the more common ones are listed in alphabetical order: