Regulatory Impact Assessment is an essential process for evaluating policy feasibility and balancing stakeholder interests prior to enactment. However, manual analysis is often time-consuming and error-prone due to the unstructured nature of legal documents and complex cross-referencing networks. To address this challenge, we propose a framework that automates legal information extraction and multi-label impact classification by integrating a Large Language Model (LLM) with ontologybased reasoning. Initially, an LLM utilizes few-shot prompt engineering to extract structured entities (agents, actions, states) and primary legal signals (deontic words, impacted targets) from raw texts. Subsequently, this extracted data is mapped into a domain-specific ontology, where a HermiT reasoner applies formal Description Logic axioms to automatically classify impacts into qualitative or quantitative benefits, costs, and constraints. Experimental evaluations on a curated dataset demonstrate the framework's effectiveness, achieving an F1-Score of 84.30% in impact identification, and a Macro-F1 of 84.71% with an Exact Match of 75.15% in complex multi-label classification. By delegating reasoning logic to the ontology, our approach effectively mitigates LLM hallucination, delivering precise, consistent, and explainable legal analysis.

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This work addresses the challenge of establishing systematic links between recitals and normative provisions in European Union legislation. Recitals, while non-binding, play a crucial role in legal interpretation as they express the rationale, objectives, an

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.

This mini review surveys published work on automatic summarization of legal judgments. We focus on how natural language processing handles argument structure, discourse, and the role of citations and precedent when systems generate shorter version

Le LAB Juriconnexion s’associe au Village de la justice pour lancer une nouvelle chronique dédiée aux outils documentaires juridiques. Dans un contexte d’innovation accélérée, entre intelligence artificielle, nouveaux usages et transformation des pratiques, cette collaboration propose un espace d’analyse indépendant, nourri par l’expérience de (...)