Enabling reasoning with LegalRuleML. 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.

References in zbMATH (referenced in 6 articles , 1 standard article )

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  1. Benzmüller, Christoph; Parent, Xavier; van der Torre, Leendert: Designing normative theories for ethical and legal reasoning: \textscLogiKEyframework, methodology, and tool support (2020)
  2. Robaldo, Livio; Bartolini, Cesare; Palmirani, Monica; Rossi, Arianna; Martoni, Michele; Lenzini, Gabriele: Formalizing GDPR provisions in reified I/O logic: the DAPRECO knowledge base (2020)
  3. Lam, Ho-Pun; Hashmi, Mustafa: Enabling reasoning with LegalRuleML (2019)
  4. Camilleri, John J.; Schneider, Gerardo: Modelling and analysis of normative documents (2017)
  5. Athan, Tara; Boley, Harold; Governatori, Guido; Palmirani, Monica; Paschke, Adrian; Wyner, Adam: Legalruleml: From metamodel to use cases. (A tutorial) (2013) ioport
  6. Palmirani, Monica; Governatori, Guido; Rotolo, Antonino; Tabet, Said; Boley, Harold; Paschke, Adrian: LegalRuleML: XML-based rules and norms (2011) ioport