Abstract
Statistical relational learning constructs statistical models from relational databases, combining the powers of relational learning and statistical learning. Its strong ability and special property make statistical relational learning become one of the important areas in machine learning. In this paper, the general concepts and characteristics of statistical relational learning are presented firstly. Then some major branches of this newly emerging field are discussed, including logic and rule-based approaches, frame and object-oriented approaches, and several other important approaches. After that some methods of applying rough set in statistical relational learning are described, such as gRS-ILP and VPRSILP. Finally applications of statistical relational learning are briefly introduced and some future directions of statistical relational learning and the prospects of rough set in this area are pointed out.
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