Enabling reasoning with LegalRuleML

Abstract

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.

On Modelling Process Aspects With Deontic Event-Calculus

“…Besides, developing such transformations and applying them into some smart contract-based systems e.g., ethereum can extend the language of such systems in a way that users can make their smart contracts explicit and define them declaratively. The methodology proposed by Lam and Hashmi (2019) can be benefited for developing such transformations.…”