Bayes-SWRL: A Probabilistic Extension of SWRL
Yu Liu, Shihong Chen, Shuoming Li, Yunhua Wang · 2013
In order to deal with some real-world problems, the uncertainty reasoning for Semantic Web has been widely studied, though lots of researchers tend to combine fuzzy theory with Description Logic Programs (DLP) and Semantic Web Rule Language (SWRL). Since probability theory is more suitable than fuzzy logic to make prediction about event from a state of partial knowledge, a probabilistic extension of SWRL, named Bayes-SWRL, is introduced in this paper. Based on the syntax and model-theoretic semantic defined for Bayes-SWRL, we propose a probabilistic reasoning algorithm, which is employed to implement the prototype reasoner of Bayes-SWRL. In addition, we point out some constrains of Bayes-SWRL that users should pay attention to.