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.

Building upon a formal, event-centric model for the diachronic evolution of legal norms grounded in the IFLA Library Reference Model (LRMoo), this paper addresses the essential first step of publishing this model's foundational entity-the abstract legal Work (F1)-on the Semantic Web. We propose a detailed, property-by-property mapping of the LRMoo F1 Work to the widely adopted schema.org/Legislation vocabulary. Using Brazilian federal legislation from the Normas.leg.br portal as a practical case study, we demonstrate how to create interoperable, machine-readable descriptions via JSON-LD, focusing on stable URN identifiers, core metadata, and norm relationships. This structured mapping establishes a stable, URI-addressable anchor for each legal norm, creating a verifiable "ground truth". It provides the essential, interoperable foundation upon which subsequent layers of the model, such as temporal versions (Expressions) and internal components, can be built. By bridging formal ontology with web-native standards, this work paves the way for building deterministic and reliable Legal Knowledge Graphs (LKGs), overcoming the limitations of purely probabilistic models.

Legal decision-making process requires the availability of comprehensive and detailed legislative background knowledge and up-to-date information on legal cases and related sentences/decisions. Legal Knowledge Graphs (KGs) would be a valuable tool to facilitate access to legal information, to be queried and exploited for the purpose, and to enable advanced reasoning and machine learning applications. Indeed, legal KGs may act as knowledge intensive component to be used by pre-dictive machine learning solutions supporting the decision process of the legal expert. Nevertheless, a few KGs can be found in the legal domain. To fill this gap, we developed a legal KG targeting legal cases of violence against women, along with clear adopted methodologies. Specifically, the paper introduces two complementary approaches for automated legal KG construction; a systematic bottom-up approach, customized for the legal domain, and a new solution leveraging Large Language Models. Starting from legal sentences publicly available from the European Court of Justice, the solutions integrate structured data extraction, ontology development, and semantic enrichment to produce KGs tailored for legal cases involving violence against women. After analyzing and comparing the results of the two approaches, the developed KGs are validated via suitable competency questions. The obtained KG may be impactful for multiple purposes: can improve the accessibility to legal information both to humans and machine, can enable complex queries and may constitute an important knowledge component to be possibly exploited by machine learning tools tailored for predictive justice.

This paper presents a knowledge graph construction method for legal case documents and related laws, aiming to organize legal information efficiently and enhance various downstream tasks. Our approach consists of three main steps: data crawling, information extraction, and knowledge graph deployment. First, the data crawler collects a large corpus of legal case documents and related laws from various sources, providing a rich database for further processing. Next, the information extraction step employs natural language processing techniques to extract entities such as courts, cases, domains, and laws, as well as their relationships from the unstructured text. Finally, the knowledge graph is deployed, connecting these entities based on their extracted relationships, creating a heterogeneous graph that effectively represents legal information and caters to users such as lawyers, judges, and scholars. The established baseline model leverages unsupervised learning methods, and by incorporating the knowledge graph, it demonstrates the ability to identify relevant laws for a given legal case. This approach opens up opportunities for various applications in the legal domain, such as legal case analysis, legal recommendation, and decision support.

A legal knowledge graph constructed from court cases, judgments, laws and other legal documents can enable a number of applications like question answering, document similarity, and search. While the use of knowledge graphs for distant supervision in NLP tasks is well researched, using knowledge graphs for downstream graph tasks like node similarity presents challenges in selecting node types and their features. In this demo, we describe our solution for predicting similar nodes in a case graph derived from our legal knowledge graph.

Recent years have witnessed the prosperity of legal artificial intelligence with the development of technologies. In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs. We formulate this task as a challenging knowledge graph completion problem, which requires not only text understanding but also graph reasoning. To this end, we propose a novel text-guided graph reasoning approach. We collect amounts of real-world legal provision data from the Guangdong government service website and construct a legal dataset called LegalLPP. Extensive experimental results on the dataset show that our approach achieves better performance compared with baselines. The code and dataset are available in \url{https://github.com/zjunlp/LegalPP} for reproducibility.

Legal technology is currently receiving a lot of attention from various angles. In this contribution we describe the main technical components of a system that is currently under development in the European innovation project Lynx, which includes partners from industry and research. The key contribution of this paper is a workflow manager that enables the flexible orchestration of workflows based on a portfolio of Natural Language Processing and Content Curation services as well as a Multilingual Legal Knowledge Graph that contains semantic information and meaningful references to legal documents. We also describe different use cases with which we experiment and develop prototypical solutions.