用时间维度建模法律条文演化，实现精确回溯任一日期的文本。
Modeling the Diachronic Evolution of Legal Norms: An LRMoo-Based, Component-Level, Event-Centric Approach to Legal Knowledge Graphs
- 基于LRMoo本体，将法律条文拆解为时间版本与语言版本的层级结构。
- 以事件为中心追踪立法修改，实现对巴西宪法任意日期文本的精准重建。
- 适合法律知识图谱、可信AI等需可验证历史文本的场景。
法律规范的时间演化自动处理面临关键挑战。尽管已有基础框架，但缺乏细粒度的组件级版本化形式化模式，难以支持可靠AI应用所需的确定性时间点文本重构。本文提出一种基于LRMoo本体的结构化时序建模范式。该方法将规范演化建模为一系列版本化的F1作品（Works），区分语言无关的时序版本（TV，每个为独立作品）和其单语语言版本（LV，作为F2表达式）。立法修改过程通过事件中心建模，实现变更的精确追溯。以巴西宪法为例，验证了该架构可在特定日期精确重建法律文本任意部分。为法律知识图谱提供可验证的语义基础，奠定可信法律AI的确定性根基。
原文摘要 · Abstract (English)
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.
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