Statistical relational learning (SRL) is a subdiscipline of

artificial intelligence Artificial intelligence (AI) is the capability of computer, computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of re ...

and

machine learning Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task ( ...

that is concerned with

domain model In software engineering, a domain model is a conceptual model of the domain (software engineering), domain that incorporates both behavior and data.Fowler, Martin. "P of EAA - Domain Model"/ref> In ontology engineering, a domain model is a Knowl ...

s that exhibit both

uncertainty Uncertainty or incertitude refers to situations involving imperfect or unknown information. It applies to predictions of future events, to physical measurements that are already made, or to the unknown, and is particularly relevant for decision ...

(which can be dealt with using statistical methods) and complex, relational structure. Typically, the

knowledge representation Knowledge representation (KR) aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems whereas knowledge representation and reasoning (KRR, KR&R, or KR²) also aims to understand, reason, and ...

formalisms developed in SRL use (a subset of)

first-order logic First-order logic, also called predicate logic, predicate calculus, or quantificational logic, is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantified variables over ...

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", "for all", "for every", or "given an arbitrary element". It expresses that a predicate can be satisfied by e ...

) 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 (discrete mathematics), graph expresses the conditional dependence structure between random variables. Graphica ...

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). Whi ...

s or Markov networks) to model the uncertainty; some also build upon the methods of

inductive logic programming Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. The term "''inductive''" here refers to philosophical ...

. 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 Reason is the capacity of consciously applying logic by drawing valid conclusions from new or existing information, with the aim of seeking the truth. It is associated with such characteristically human activities as philosophy, religion, scien ...

(specifically probabilistic inference) and

. 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). Another term that is sometimes used in the literature is ''relational machine learning'' (RML).

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 (CF) is, besides content-based filtering, one of two major techniques used by recommender systems.Francesco Ricci and Lior Rokach and Bracha ShapiraIntroduction to Recommender Systems Handbook, Recommender Systems Handbo ...

, 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 consisting of a set of social actors (such as individuals or organizations), networks of Dyad (sociology), dyadic ties, and other Social relation, social interactions between actors. The social network per ...

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 *

Markov logic network A Markov logic network (MLN) is a probabilistic logic which applies the ideas of a Markov network to first-order logic, defining probability distributions on possible worlds on any given domain. History In 2002, Ben Taskar, Pieter Abbeel and ...

s * Multi-entity Bayesian network * Probabilistic logic programs * 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: ''[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, 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 Pedro Domingos (born 1965) is a Professor Emeritus of computer science and engineering at the University of Washington. He is a researcher in machine learning known for Markov logic network enabling uncertain inference. Education Domingos rece ...

"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

Canonical tasks

Representation formalisms

See also

Resources

References