Statistical relational learning (SRL) is a subdiscipline of artificial intelligence

Artificial intelligence

Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...

 and machine learning

Machine learning

Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

 that is concerned with models of domains

Domain model

A domain model in problem solving and software engineering can be thought of as a conceptual model of a domain of interest which describes the various entities, their attributes, roles and relationships, plus the constraints that govern the integrity of the model elements comprising that problem...

 that exhibit both uncertainty

Uncertainty

Uncertainty is a term used in subtly different ways in a number of fields, including physics, philosophy, statistics, economics, finance, insurance, psychology, sociology, engineering, and information science...

 (which can be dealt with using statistical methods) and complex, relational

Relation (mathematics)

In set theory and logic, a relation is a property that assigns truth values to k-tuples of individuals. Typically, the property describes a possible connection between the components of a k-tuple...

 structure. Typically, the knowledge representation

Knowledge representation

Knowledge representation is an area of artificial intelligence research aimed at representing knowledge in symbols to facilitate inferencing from those knowledge elements, creating new elements of knowledge...

 formalisms developed in SRL use (a subset of) first-order logic

First-order logic

First-order logic is a formal logical system used in mathematics, philosophy, linguistics, and computer science. It goes by many names, including: first-order predicate calculus, the lower predicate calculus, quantification theory, and predicate logic...

 to describe relational properties of a domain in a general manner (universal quantification

Universal quantification

In predicate logic, universal quantification formalizes the notion that something is true for everything, or every relevant thing....

) and draw upon probabilistic graphical models (such as Bayesian networks

Bayesian network

A Bayesian network, Bayes network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph . For example, a Bayesian network could represent the probabilistic...

 or Markov networks

Markov network

A Markov random field, Markov network or undirected graphical model is a set of variables having a Markov property described by an undirected graph. A Markov random field is similar to a Bayesian network in its representation of dependencies...

) to model the uncertainty; some also build upon the methods of inductive logic programming

Inductive logic programming

Inductive logic programming is a subfield of machine learning which uses logic programming as a uniform representation for examples, background knowledge and hypotheses...

.
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 (specifically probabilistic inference

Statistical inference

In statistics, statistical inference is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation...

) and knowledge representation

Knowledge representation

Knowledge representation is an area of artificial intelligence research aimed at representing knowledge in symbols to facilitate inferencing from those knowledge elements, creating new elements of knowledge...

. Therefore, alternative terms that reflect the main foci of the field include statistical relational learning and reasoning (emphasizing the importance of reasoning) and first-order probabilistic languages (emphasizing the key properties of the languages with which models are represented).

Canonical tasks

A number of canonical tasks are associated with statistical relational learning, the most common ones being

• collective classification, i.e. the (simultaneous) prediction of the class of several objects given objects' attributes and their relations
• 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, and the related task of collaborative filtering
  Collaborative filtering
  Collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets...
  
  , i.e. the filtering for information that is relevant to an entity (where a piece of information is considered relevant to an entity if it is known to be relevant to a similar entity).
• social network
  Social network
  A social network is a social structure made up of individuals called "nodes", which are tied by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige.Social...
  
   modelling
• object identification/entity resolution/record linkage
  Record linkage
  Record linkage refers to the task of finding records in a data set that refer to the same entity across different data sources...
  
  , i.e. the identification of equivalent entries in two or more separate databases/datasets

Representation formalisms

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:

• Bayesian logic programs
• BLOG models
• Logic programs with annotated disjunctions
• Markov logic networks
  Markov logic network
  A Markov logic network is a probabilistic logic which applies the ideas of a Markov network to first-order logic, enabling uncertain inference...
  
  
• Multi-entity Bayesian networks
• Probabilistic relational models
  Probabilistic relational model
  A Probabilistic relational model is the counterpart of a Bayesian network in statistical relational learning.-References:*Friedman N, Getoor L, Koller D, Pfeffer A....
  
  
• Recursive random fields
• Relational Bayesian networks
• Relational dependency networks
• Relational Markov networks

Resources

• Lise Getoor and Ben Taskar: Introduction to statistical relational learning, MIT Press, 2007
• Brian Milch, and Stuart J. Russell: First-Order Probabilistic Languages: Into the Unknown, Inductive Logic Programming, volume 4455 of Lecture Notes in Computer Science, page 10-24. Springer, 2006
• Rodrigo de Salvo Braz, Eyal Amir, and Dan Roth: A Survey of First-Order Probabilistic Models, Innovations in Bayesian Networks, volume 156 of Studies in Computational Intelligence, Springer, 2008
• Hassan Khosravi and Bahareh Bina: A Survey on Statistical Relational Learning, Advances in Artificial Intelligence, Lecture Notes in Computer Science, Volume 6085/2010, 256-268, Springer, 2010