Modular Norm Models: A Lightweight Approach for Modeling and Reasoning about Legal Compliance

Complying with legal regulatory requirements in privacy and security is necessary for critical software systems. Analysis of complex and voluminous legal text can benefit from the automation and traceability of logic-based models. We propose such a model based on norms. Norms are legal rights and as...

Full description

Saved in:
Bibliographic Details
Published in2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) pp. 657 - 662
Main Authors Mandal, Sayonnha, Gandhi, Robin, Siy, Harvey
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2017
Subjects
Analytical models
applicability
Cognition
compliance
Frame semantics
Hohfeld rights
Law
laws
Natural language processing
norms
Privacy
satisfiability
Semantics
Online AccessGet full text
DOI10.1109/DASC-PICom-DataCom-CyberSciTec.2017.115

Cover

More Information
Summary:Complying with legal regulatory requirements in privacy and security is necessary for critical software systems. Analysis of complex and voluminous legal text can benefit from the automation and traceability of logic-based models. We propose such a model based on norms. Norms are legal rights and associated duties expressed in regulatory documents. Such norm models help reason about available rights and required duties based on the satisfiability of situations, a state-of-affair, in a given scenario. But model extraction from natural language as well as compliance reasoning in complex scenarios needs subject matter expertise. Our method enables modular norm model extraction and reasoning. For extraction, using the theory of frame-semantics we construct two foundational norm templates that cover Hohfeld's concepts of claim-right and its jural correlative, duty. Template instantiations from legal text result in a repeatable method for extraction of modular norm models. For reasoning, we introduce the notion of a super-situation. Super-situations contain other norm models. Compliance results from a modular norm are propagated to its containing super-situation, which in turn participates in other modular norms. This modularity allows on-demand incremental modeling and reasoning using simpler model primitives than previous approaches.
DOI:10.1109/DASC-PICom-DataCom-CyberSciTec.2017.115