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Enabling reasoning with LegalRuleML

Published online by Cambridge University Press:  14 September 2018

HO-PUN LAM  and
MUSTAFA HASHMI
HO-PUN LAM
Affiliation:
Data61, CSIRO, Sydney, NSW, Australia (e-mail: brian.lam@data61.csiro.au)
MUSTAFA HASHMI
Affiliation:
Data61, CSIRO, Brisbane, QLD, Australia (e-mail: mustafa.hashmi@data61.csiro.au)
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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.

Keywords

Deontic logicmodal defeasible logiclegal reasoningLegalRuleMLbusiness contracts
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 19 , Issue 1 , January 2019 , pp. 1 - 26
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

A preliminary version of this paper appeared in the Proceedings of the 10th International Web Rule Symposium (RuleML 2016), J. J. Alferes, L. Bertossi, G. Governatori, P. Fodor and J. Hall, Eds, Springer, 2016, pp. 241–257.

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