Statistical relational learning: Difference between revisions

Revision as of 13:19, 12 August 2020

Latest revision as of 09:48, 27 May 2025

(9 intermediate revisions by 8 users not shown)

Line 1:

{{short description|Subdiscipline of artificial intelligence}}

'''Statistical relational learning''' ('''SRL''') is a subdiscipline of [[artificial intelligence]] and [[machine learning]] that is concerned with [[___domain model]]s that exhibit both [[uncertainty]] (which can be dealt with using statistical methods) and complex, [[relation (mathematics)|relational]] structure.<ref name=getoor:book07 /><ref name=rossi:jair12 />

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]] to describe relational properties of a ___domain in a general manner ([[universal quantification]]) and draw upon [[probabilistic graphical model]]s (such as [[Bayesian network]]s or [[Markov network]]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.<ref name=getoor:book07 />

<ref name=getoor:book07 />

<ref name=rossi:jair12 />

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]] to describe relational properties of a ___domain in a general manner ([[universal quantification]]) and draw upon [[probabilistic graphical model]]s (such as [[Bayesian network]]s or [[Markov network]]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.

<ref name=getoor:book07 />

As is evident from the characterization above, the field is not strictly limited to learning aspects; it is equally concerned with [[Semantic_reasonerSemantic reasoner|reasoning]] (specifically [[statistical inference|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).

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 .<ref name=richardson:ml06 />.

* [[collective classification]], i.e. the (simultaneous) [[classification (machine learning)|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 [[cluster analysis|grouping]] of similar objects, where similarity is determined according to the links of an object, and the related task of [[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]] modelling

* [[record linkage|object identification/entity resolution/record linkage]], i.e. the identification of equivalent entries in two or more separate databases/datasets

Line 24 ⟶ 22:

* [[Bayesian logic program]]

* [[BLOG model]]

* Logic programs with annotated disjunctions

* [[Markov logic network]]s

* [[Multi-entity Bayesian network]]

* [[Probabilistic logic program]]s

* Probabilistic relational model – a Probabilistic Relational Model (PRM) is the counterpart of a [[Bayesian network]] in statistical relational learning.<ref name=friedman:ijcai99 /><ref name=sommestad:compsec10 />

* [[Probabilistic soft logic]]

Line 40 ⟶ 38:

* [[Fuzzy logic]]

* [[Grammar induction]]

* [[Knowledge graph embedding]]

== 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]{{dead link|date=May 2025|bot=medic}}{{cbignore|bot=medic}}'', 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]]: ''[http://www.ai.sri.com/~braz/papers/sci-chapter.pdf 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: ''[http://www.cs.ubc.ca/~hkhosrav/pub/survey.pdf 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]]: ''[http://www.jair.org/media/3659/live-3659-6589-jair.pdf 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 (researcher)|David Poole]], "Statistical Relational Artificial Intelligence: Logic, Probability, and Computation", Synthesis Lectures on Artificial Intelligence and Machine Learning" March 2016 {{ISBN|9781627058414}}.

== References ==

Line 64 ⟶ 63:

</ref>

<ref name=sommestad:compsec10>

Teodor Sommestad, Mathias Ekstedt, Pontus Johnson (2010) "A probabilistic relational model for security risk analysis", ''Computers & Security'', 29 (6), 659-679 {{DOIdoi|10.1016/j.cose.2010.02.002}}

</ref>

}}

[[Category:Computational statistics]]