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

TL;DR

This paper introduces a formal, component-level, event-centric modeling pattern for representing the temporal evolution of legal norms within knowledge graphs, enabling precise reconstruction of legal texts over time.

Contribution

It presents a novel LRMoo-based approach for detailed, versioned, and event-driven legal norm modeling, enhancing the accuracy and reliability of legal knowledge graphs.

Findings

Enables exact reconstruction of legal texts at any historical point

Formalizes legislative amendments through event-centric modeling

Demonstrates effectiveness using the Brazilian Constitution case study

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.