When identifying and elaborating compliance requirements, analysts need to follow the cross references in legal texts and consider the additional information in the cited provisions. Enabling easier navigation and handling of cross references requires automated support for the detection of the natural language expressions used in cross references, the interpretation of cross references in their context, and the linkage of cross references to the targeted provisions. In this article, we propose an approach and tool support for automated detection and resolution of cross references. The approach leverages the structure of legal texts, formalized into a schema, and a set of natural language patterns for legal cross reference expressions. These patterns were developed based on an investigation of Luxembourg's legislation, written in French. To build confidence about their applicability beyond the context where they were observed, these patterns were validated against the Personal Health Information Protection Act (PHIPA) by the Government of Ontario, Canada, written in both French and English. We report on an empirical evaluation where we assess the accuracy and scalability of our framework over several Luxembourgish legislative texts as well as PHIPA.

Abstract. Many laws, e.g., those concerning taxes and social benefits, need to be operationalized and implemented into public administration procedures and eGovernment applications. Where such operationalization is warranted, the legal frameworks that interpret the underlying laws are typically prescriptive, providing procedural rules for ensuring legal compliance. We propose a UML-based approach for modeling procedural legal rules. With help from legal experts, we investigate actual legal texts, identifying both the information needs and sources of complexity in the formalization of procedural legal rules. Building on this study, we develop a UML profile that enables more precise modeling of such legal rules. To be able to use logic-based tools for compliance analysis, we automatically transform models of procedural legal rules into the Object Constraint Language (OCL). We report on an application of our approach to Luxembourg's Income Tax Law providing initial evidence for the feasibility and usefulness of our approach.

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When elaborating compliance requirements, analysts need to follow the cross references in the underlying legal texts and consider the additional information in the cited provisions. To enable easier navigation and handling of cross references, automation is necessary for recognizing the natural language patterns used in cross reference expressions (cross reference detection), and for interpreting these expressions and linking them to the target provisions (cross reference resolution). In this paper, we propose a solution for automated detection and resolution of legal cross references. We ground our work on Luxembourg's legislative texts, both for studying the natural language patterns in cross reference expressions and for evaluating the accuracy and scalability of our solution.