STATISTICAL RELATIONAL LEARNING: A STATE-OF-THE-ART REVIEW

Muhamet Kastrati, Marenglen Biba
2019 Journal of Engineering Technology and Applied Sciences  
Citation: Kastrati, M., Biba, M., "Statistical Relational Learning: A State-of-the-Art Review". Abstract The objective of this paper is to review the state-of-the-art of statistical relational learning (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
more » ... 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 SRL [28] also known as probabilistic inductive logic programming (PILP) [16] is based on the idea that we can build models that can effectively represent, reason and learn in domains with presence of uncertainty and complex relational structure. In doing so, it addresses one of the major concerns of artificial intelligence, where key principle is integration of probabilistic reasoning, first-order logic and machine learning [16] . As shown in Figure 1 , SRL combines a logic-based representation with probabilistic modeling and machine learning. SRL models are usually represented as combination of probabilistic graphical models (PGMs) with first-order logic (FOL) to handle the uncertainty and probabilistic correlations in relational domains. Figure 1: Statistical relational learning aka. probabilistic inductive logic programming combines probability, logic and learning. Adopted from [68]. In recent years, many formalisms and representations have been developed in SRL. Muggleton [42] and Cussens [6] upgraded stochastic grammars towards stochastic logic programs. Friedman [25] combined advantages of relational logic with Bayesian networks (BNs). Kersting et.al [34] combined definite logic programs with BNs. Neville and Jensen [52] extended dependency networks to relational dependency networks. Taskar et al. [71] extended Markov networks (MNs) into relational Markov networks (RMNs), and Domingos and Richardson [18] into Markov logic networks (MLNs). Recently, many works have been done about further development of new techniques and application of SRL models in science and industry. Therefore, the goal of this paper is to provide a state-of-the-art review of SRL models, in the same time to contribute to research community with a cohesive overview of state-of-the-art results for a wide range of SRL problems (during the five years) and to identify possible opportunities for future research. The rest of this paper is structured as the following: The second section introduces some background theory and notation, starting by the concepts of PGMs, FOL and Inductive Programming Language. The third section explores recent research into a range to SRL from its origins to the present day, including a discussion of relational models and of the successful role of the SRL problems/limitation and application in real world problem. The last section provides concluding remarks and some recommendations. 143
doi:10.30931/jetas.594586 fatcat:qoei3pteibd6la4oqin6rvrxqi