  In order to automate verification process, regulatory rules written in
natural language need to be translated into a format that machines can
understand. However, none of the existing formalisms can fully represent the
elements that appear in legal norms. For instance, most of these formalisms do
not provide features to capture the behavior of deontic effects, which is an
important aspect in automated compliance checking. This paper presents an
approach for transforming legal norms represented using LegalRuleML to a
variant of Modal Defeasible Logic (and vice versa) such that a legal statement
represented using LegalRuleML can be transformed into a machine-readable format
that can be understood and reasoned about depending upon the client's
preferences.
