Research Progress in Statistical Relational Learning
Liu Dayou, Yu Peng, Gao Ying, Qi Hong, and Sun Shuyang
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Interest in statistical relational learning (SRL) has grown rapidly in recent years. SRL integrates the relational or logical representations, probabilistic reasoning mechanisms with machine learning, and it can solve many complicated relational problems in real world. It has important applications in many fields such as World Wide Web, social networks, computational biology, information extraction, computer vision, speech recognition etc. In the past few years, SRL has received a lot of attention and a rich variety of approaches have been developed by many researchers, and they have different relational or logical representations, probabilistic reasoning mechanisms or machine learning principles. The goal of this paper is to provide an introduction to and an overview of these works. First the research fields and different tasks of SRL are introduced and summarized. And then an introductory survey and overview of the SRL approaches is provided, and the approaches are classified into four families, which are the approaches based on Bayesian networks, stochastic grammars, Markov networks, and (hidden) Markov models, according to probabilistic representations and reasoning mechanisms. For each approach family, the probabilistic logical models, parameter estimation and structure learning, and the states-of-the-art are introduced. Finally, the current problems in SRL are discussed and future research directions are pointed out.