Modeling the Diachronic Evolution of Legal Norms: An LRMoo-Based, Component-Level, Event-Centric Approach to Legal Knowledge Graphs

• URL: http://arxiv.org/abs/2506.07853v3
• Date: Tue, 09 Sep 2025 15:15:03 GMT
• Title: Modeling the Diachronic Evolution of Legal Norms: An LRMoo-Based, Component-Level, Event-Centric Approach to Legal Knowledge Graphs
• Authors: Hudson de Martim,
• Abstract summary: This paper proposes a structured, temporal modeling pattern grounded in the LRMoo to address this need.<n>We show how this event-generative architecture enables the precise, deterministic retrieval and reconstruction of any part of a legal text as it existed on a specific date.
• 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 temporal evolution of their hierarchical components. While foundational conceptual frameworks like IFLA LRMoo provide a generic toolkit for bibliographic data, and encoding standards like Akoma Ntoso offer a robust syntax for legal documents, a dedicated, formal modeling pattern for granular, component-level versioning is still required. This limitation hinders the deterministic point-intime reconstruction of legal texts, a fundamental capability for reliable Legal Tech and AI applications. This paper proposes a structured, temporal modeling pattern grounded in the LRMoo ontology to address this need. Our approach models the evolution of a legal norm as a diachronic chain of F2 Expressions. We introduce a key distinction between a language-agnostic Temporal Version (TV)-a semantic snapshot of the norm's structure-and its concrete monolingual realizations, the Language Versions (LV). Both are modeled as F2 Expressions linked by the canonical R76 is derivative of property. This paradigm is applied recursively to the legal text's internal structure, representing it as a parallel hierarchy of abstract Component Works (F1) and their versioned Component Expressions (F2). Furthermore, we formalize the legislative amendment process using the F28 Expression Creation event, allowing changes to be traced from an amending act to its precise effect on the amended norm. Using the Brazilian Federal Constitution as a case study, we demonstrate how this event-centric 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 building verifiable knowledge graphs and advanced AI tools, overcoming the limitations of current generative models.

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