Statistical relational learning
 | This article, Statistical relational learning, has recently been created via the Articles for creation process. Please check to see if the reviewer has accidentally left this template after accepting the draft and take appropriate action as necessary.
Reviewer tools: |
Statistical relational learning (SRL) is a subdiscipline of artificial intelligence 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.
Canonical Tasks
A number of canonical tasks are associated with statistical relational learning, the most common ones being
- collective classification, i.e. the prediction of the class of an object given not only the attributes of the object itself but also of related objects
- link prediction, i.e. predicting whether or not two or more objects are related
- link-based clustering, i.e. the grouping of similar objects, where similarity is determined according to the links of an object
- social network modelling
- object identification/entity resolution
Representation Formalisms
- Markov logic networks
- Probabilistic relational models
- Multi-entity Bayesian networks
- Bayesian logic
- Bayesian logic programs
- Recursive random fields
- Logic programs with annotated disjunctions
Sources
- Lise Getoor and Ben Taskar: Introduction to statistical relational learning, MIT Press, 2007