Documentation juridique

Navigating and retrieving relevant excerpts of legislation is challenging, requiring time and effort, especially to fine-tune appropriate input search queries. Furthermore, the continuously growing, heterogeneous body of laws, combined with a deep interconnection among normative acts, adds a layer of complexity: some potentially relevant rules may be hidden in articles that, through multiple citations and references, might be relevant for the input query. Traditional search systems, based on keywords or more sophisticated approaches as BM25 or TF-IDF, do not support such flexible exploration, being ineffective at handling contextual information. To address these challenges, recent research proposed using graph data models for legislative knowledge management, introducing a straightforward approach to handling network complexity. They adopted the Property Graph data structure, demonstrating how it provides semantics and navigation power, supporting advanced querying tools for legislative acts, and implemented it on the Italian legislation. In this paper, we build on recent results on legislative knowledge management with graphs by proposing LegisSearch, an effective navigation system that, combining the graph data model with pre-trained Large Language Models and universal text embeddings, allows users to conduct powerful searches within a legislative system. We implement LegisSearch within the Italian graph of national laws, and we test its performance across multiple domains by comparing its search results with those provided in specific thematic areas by Italian ministries on their official websites, demonstrating its superior retrieval performance over traditional search systems and testing the contribution of each component.

Mise à jour 2025 Qui sont les fonctions support des cabinets d'avocats. Quelques références pour en savoir plus sur documentalistes juridiques, KM, communicants, assistants juridiques, paralegals et informaticiens

Un nouveau processus d’alignement entre la base de connaissance nationale BACON et le Sudoc est désormais opérationnel. Son objectif : faciliter et améliorer le signalement des ressources électroniques dans le Sudoc, au bénéfice de l’ensemble des établissements documentaires de l’ESR. Pourquoi ce nouveau flux ? Plusieurs constats ont motivé la mise en place de ce […]

Legal academia is likely stuck with law faculty scholarly impact rankings-that is, using citation counts to judge a law faculty's reputation and influence. But

This study addresses the limitations of traditional keyword-based legal information systems by proposing a robust model for managing legislative changes. We introduce the Legal-Onto model, integrated with Vietnamese SBert, to enhance the semantic representation and accessibility of legal documents. The model leverages an ontology-based framework to represent legal structures and employs a Sentence-BERT variant tailored for Vietnamese to compare legal texts at multiple granularities–chapters, sections, articles, clauses, and points. Unlike prior methods that rely solely on the highest similarity score, our approach retains multiple high-similarity matches to mitigate misalignment caused by overlapping legal provisions. This strategy ensures accurate mapping of new legislative elements and prevents erroneous prioritization. The model architecture integrates relational and conceptual graphs with natural language processing techniques to systematically detect and highlight legislative updates. Experimental evaluations on the Vietnamese Land Laws of 2013 and 2024 demonstrate the model’s effectiveness in identifying both structural and content changes. Results show that the Legal-Onto model significantly improves legal knowledge management by achieving high alignment accuracy and reducing manual analysis efforts.