A Temporal FRBR/FRBRoo-Based Model for Component-Level Versioning of Legal Norms

• URL: http://arxiv.org/abs/2506.07853v1
• Date: Mon, 09 Jun 2025 15:18:36 GMT
• Title: A Temporal FRBR/FRBRoo-Based Model for Component-Level Versioning of Legal Norms
• Authors: Hudson de Martim,
• Abstract summary: This paper proposes a structured, temporal model that extends the FRBRoo framework to address this gap.<n>It introduces specialized subclasses of Expressio - Temporal Version (TV) and Language Version (LV) to represent the state of a legal norm.<n>Using the Brazilian Federal Constitution as a case study, the paper demonstrates how each amendment creates new Component Temporal Versions for affected provisions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effectively representing legal norms for automated processing is a critical challenge, particularly in tracking the diachronic evolution of their hierarchical components (e.g., articles, paragraphs). While foundational frameworks like FRBR/FRBRoo and standards like Akoma Ntoso model legal documents at a macro level, they lack native mechanisms for granular, component-level versioning. This limitation hinders the deterministic point-in-time reconstruction of legal texts, a fundamental capability for reliable Legal Tech and AI applications. This paper proposes a structured, temporal model that extends the FRBRoo framework to address this gap. It introduces specialized subclasses of Expressio - Temporal Version (TV) and Language Version (LV - to represent the state of a legal norm and its linguistic variations at specific points in time. The model applies this same paradigm hierarchically, introducing Component Work (CW), Component Temporal Version (CTV), and Component Language Version (CLV) to track the lifecycle of individual articles, paragraphs, and clauses. Using the Brazilian Federal Constitution as a case study, the paper demonstrates how each amendment creates new Component Temporal Versions for affected provisions, while unaffected components retain their existing versions. This fine-grained, time-aware architecture enables the precise, deterministic retrieval and reconstruction of any part of a legal text as it existed on a specific date. The model provides a robust foundation for developing advanced legal information systems, knowledge graphs, and AI tools capable of accurate historical analysis and impact assessment, overcoming the limitations of current generative models.

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