Representing uncertainty in RuleML

The RuleML initiative defines a normalized markup for expressing and exchange rules in the Semantic Web. However, the syntax of the language is still limited and lacks features for representing rule-based languages capable of handling uncertainty and vagueness. It is desirable to have a general extension of RuleML which accommodates major existing languages proposed in the latest two decades. The main contribution of the paper is to propose such a general extension, showing how to encode many of the existing languages in this extension. It is detailed the important case of fuzzy rule languages. We hope this work can also provide some insights on how to cover uncertainty in the RIF framework.