User:Ssriniv9/SRL

Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational structure. The power of SRL is derived from three subareas: statistical learning, probabilistic reasoning, and first-order logic. 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 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.

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).

SRL is commonly used to perform structured prediction tasks. Some of the canonical tasks include collective classification, link prediction, network analysis, object identification, recommender systems, computational social science, and so on.

Definitions
There are several ways in which SRL can be defined. Fundamentally, it is a field that intersects first-order logic, statistical learning, and probabilistic reasoning to incorporate rich relational structure into predictions. Commonly, SRL is defined as : ....

Types of SRL Frameworks
How can they be classified? Directed vs. undirected etc. Place the frameworks in different parts.
 * Bayesian logic program
 * BLOG model
 * Logic programs with annotated disjunctions
 * Markov logic networks
 * Multi-entity Bayesian network
 * Probabilistic circuits
 * ProbLog
 * Probabilistic relational model – a Probabilistic Relational Model (PRM) is the counterpart of a Bayesian network in statistical relational learning.
 * Probabilistic soft logic
 * Recursive random field
 * Relational Bayesian network
 * Relational dependency network
 * Relational logistic regression
 * Relational Markov network
 * Relational Kalman filtering

Tasks in SRL

 * Structure learning
 * Grounding
 * Weight learning
 * Inference

Applications
SRL has been applied to several areas to obtain state-of-the-art results:
 * NLP tasks:
 * Computer vision:
 * Recommender systems:
 * Bioinformatics:
 * Computational social science:
 * Social network:

Resources

 * 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
 * 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 ISBN 9781627058414.