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 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 Hohfeldu0027s 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.