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

Hudson de Martim

TL;DR

This paper addresses the need for deterministic, auditable representations of how legal norms evolve over time, enabling exact reconstruction of texts as they stood on any date. It introduces a structured diachronic pattern based on the IFLA $LRMoo$ ontology, using a two-tier model with $TV$ (Temporal Version) and $LV$ (Language Version) realized by $F1$ Works and $F2$ Expressions, extended to Components via parallel hierarchies and $F27$ Work Creation events. The Brazilian Constitution case study demonstrates how the approach traces amendments from macro-level instruments to micro-level textual changes, linking each state to its provenance. The work provides a semantically rich backbone for Legal Knowledge Graphs, supporting deterministic reasoning and trustworthy AI in high-stakes legal contexts. Future directions include a full OWL ontology, scalable knowledge-graph population tooling, and benchmarks for formal, deterministic reconstruction.

Abstract

Representing the temporal evolution of legal norms is a critical challenge for automated processing. While foundational frameworks exist, they lack a formal pattern for granular, component-level versioning, hindering the deterministic point-in-time reconstruction of legal texts required by reliable AI applications. This paper proposes a structured, temporal modeling pattern grounded in the LRMoo ontology. Our approach models a norm's evolution as a diachronic chain of versioned F1 Works, distinguishing between language-agnostic Temporal Versions (TV)-each being a distinct Work-and their monolingual Language Versions (LV), modeled as F2 Expressions. The legislative amendment process is formalized through event-centric modeling, allowing changes to be traced precisely. Using the Brazilian Constitution as a case study, we demonstrate that our architecture enables the exact reconstruction of any part of a legal text as it existed on a specific date. This provides a verifiable semantic backbone for legal knowledge graphs, offering a deterministic foundation for trustworthy legal AI.

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

TL;DR

This paper addresses the need for deterministic, auditable representations of how legal norms evolve over time, enabling exact reconstruction of texts as they stood on any date. It introduces a structured diachronic pattern based on the IFLA ontology, using a two-tier model with (Temporal Version) and (Language Version) realized by Works and Expressions, extended to Components via parallel hierarchies and Work Creation events. The Brazilian Constitution case study demonstrates how the approach traces amendments from macro-level instruments to micro-level textual changes, linking each state to its provenance. The work provides a semantically rich backbone for Legal Knowledge Graphs, supporting deterministic reasoning and trustworthy AI in high-stakes legal contexts. Future directions include a full OWL ontology, scalable knowledge-graph population tooling, and benchmarks for formal, deterministic reconstruction.

Abstract

Representing the temporal evolution of legal norms is a critical challenge for automated processing. While foundational frameworks exist, they lack a formal pattern for granular, component-level versioning, hindering the deterministic point-in-time reconstruction of legal texts required by reliable AI applications. This paper proposes a structured, temporal modeling pattern grounded in the LRMoo ontology. Our approach models a norm's evolution as a diachronic chain of versioned F1 Works, distinguishing between language-agnostic Temporal Versions (TV)-each being a distinct Work-and their monolingual Language Versions (LV), modeled as F2 Expressions. The legislative amendment process is formalized through event-centric modeling, allowing changes to be traced precisely. Using the Brazilian Constitution as a case study, we demonstrate that our architecture enables the exact reconstruction of any part of a legal text as it existed on a specific date. This provides a verifiable semantic backbone for legal knowledge graphs, offering a deterministic foundation for trustworthy legal AI.

Paper Structure

This paper contains 18 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. LRMoo Overview
  4. Related Standards: Akoma Ntoso, LexML Brazil, and ELI
  5. LRMoo-Based Representation of Legal Norms
  6. Practical Application: Brazilian Constitution
  7. The Concept (F1)
  8. Initial State (1988)
  9. Legislative Event (EC1-1992-03-31)
  10. New State (Post-Amendment)
  11. Legal Norm’s Components Representation
  12. Temporal Evolution and Event Granularity of Components
  13. Practical Example: The Lifecycle of a Component
  14. The Component Concept (CC)
  15. The Initial State (1988)
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Relationship among the Concept, a chain of Temporal Versions (TVs) and their Language Versions (LVs).
  • Figure 2: F27 Work Creation of a modification event.
  • Figure 3: Parallel hierarchies of Works (structured by R67 has part) and Expressions (structured by R5 has component).