Statistical relational learning (SRL) is a subdiscipline of

artificial intelligence Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech r ...

and

machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...

that is concerned with

domain model In software engineering, a domain model is a conceptual model of the domain that incorporates both behavior and data.Fowler, Martin. ''Patterns of Enterprise Application Architecture''. Addison Wesley, 2003, p. 116. In ontology engineering, a do ...

s that exhibit both

uncertainty Uncertainty refers to epistemic situations involving imperfect or unknown information. It applies to predictions of future events, to physical measurements that are already made, or to the unknown. Uncertainty arises in partially observable ...

(which can be dealt with using statistical methods) and complex, relational structure. Note that SRL is sometimes called Relational Machine Learning (RML) in the literature. Typically, the knowledge representation formalisms developed in SRL use (a subset of)

first-order logic First-order logic—also known as predicate logic, quantificational logic, and first-order predicate calculus—is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantifie ...

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

universal quantification In mathematical logic, a universal quantification is a type of quantifier, a logical constant which is interpreted as "given any" or "for all". It expresses that a predicate can be satisfied by every member of a domain of discourse. In other ...

) and draw upon

probabilistic graphical model A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability ...

s (such as

Bayesian network A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bay ...

s or

Markov network In the domain of physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. In other words, a random field is said to ...

s) 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 (specifically probabilistic inference) and knowledge representation. 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 Grouping may refer to: * Muenchian grouping * Principles of grouping * Railways Act 1921, also known as Grouping Act, a reorganisation of the British railway system * Grouping (firearms), the pattern of multiple shots from a sidearm See also ...

of similar objects, where similarity is determined according to the links of an object, and the related task of

collaborative filtering Collaborative filtering (CF) is a technique used by recommender systems.Francesco Ricci and Lior Rokach and Bracha ShapiraIntroduction to Recommender Systems Handbook Recommender Systems Handbook, Springer, 2011, pp. 1-35 Collaborative filtering ...

, 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 A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for ...

modelling * object identification/entity resolution/record linkage, 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 program * BLOG model * Logic programs with annotated disjunctions *

Markov logic network A Markov logic network (MLN) is a probabilistic logic which applies the ideas of a Markov network to first-order logic, enabling uncertain inference. Markov logic networks generalize first-order logic, in the sense that, in a certain limit, all u ...

s * Multi-entity Bayesian network * Probabilistic relational model – a Probabilistic Relational Model (PRM) is the counterpart of a

in statistical relational learning. *

Probabilistic soft logic Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains. It is applicable to a variety of machine learning problems, such as collective classification, entity reso ...

* Recursive random field * Relational Bayesian network * Relational dependency network * Relational Markov network * Relational Kalman filtering

Resources

* Brian Milch, and

Stuart J. Russell Stuart Jonathan Russell (born 1962) is a British computer scientist known for his contributions to artificial intelligence (AI). He is a professor of computer science at the University of California, Berkeley and was from 2008 to 2011 an adjunct ...

: ''[ftp://nozdr.ru/biblio/kolxo3/Cs/CsLn/Inductive%20Logic%20Programming,%2016%20conf.,%20ILP%202006(LNCS4455,%20Springer,%202006)(ISBN%203540738460)(466s).pdf#page=20 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 * Ryan A. Rossi, Luke K. McDowell, David W. Aha, and Jennifer Neville:
Transforming Graph Data for Statistical Relational Learning
', Journal of Artificial Intelligence Research (JAIR), Volume 45, page 363-441, 2012 * Luc De Raedt,

Kristian Kersting Kristian Kersting (born November 28, 1973 in Cuxhaven, Germany) is a German computer scientist. He is Professor of Artificial intelligence and machine learning, Machine Learning at the Department of Computer Science of TU Darmstadt, Department of ...

, Sriraam Natarajan and David Poole, "Statistical Relational Artificial Intelligence: Logic, Probability, and Computation", Synthesis Lectures on Artificial Intelligence and Machine Learning" March 2016 .

References

{{reflist, refs= {{cite book , last1=Getoor , first1=Lise , last2=Taskar , first2=Ben , author-link1=Lise Getoor , author-link2=Ben Taskar , date=2007 , title=Introduction to Statistical Relational Learning , url=https://linqs.github.io/linqs-website/publications/#id:getoor-book07 , publisher=MIT Press , isbn=978-0262072885 Ryan A. Rossi, Luke K. McDowell, David W. Aha, and Jennifer Neville,
Transforming Graph Data for Statistical Relational Learning.
''Journal of Artificial Intelligence Research (JAIR)'', Volume 45 (2012), pp. 363-441. Matthew Richardson and Pedro Domingos
"Markov Logic Networks.
''Machine Learning'', 62 (2006), pp. 107–136. Friedman N, Getoor L, Koller D, Pfeffer A. (1999
"Learning probabilistic relational models"
In: ''International joint conferences on artificial intelligence'', 1300–09 Teodor Sommestad, Mathias Ekstedt, Pontus Johnson (2010) "A probabilistic relational model for security risk analysis", ''Computers & Security'', 29 (6), 659-679 {{doi, 10.1016/j.cose.2010.02.002 Computational statistics Machine learning