Statistical Relational Learning: A State-of-the-Art Review

Authors : Muhamet KASTRATİ, Marenglen BİBA

Pages : 141-156

Doi:10.30931/jetas.594586

View : 8 | Download : 6

Publication Date : 2019-12-31

Article Type : Review Paper

Abstract :The objective of this paper is to review the state-of-the-art of statistical relational learning insert ignore into journalissuearticles values(SRL); models developed to deal with machine learning and data mining in relational domains in presence of missing, partially observed, and/or noisy data. It starts by giving a general overview of conventional graphical models, first-order logic and inductive logic programming approaches as needed for background. The historical development of each SRL key model is critically reviewed. The study also focuses on the practical application of SRL techniques to a broad variety of areas and their limitations.
Keywords : Statistical relational learning, probabilistic graphical models, inductive logic programming, probabilistic inductive logic programming