Reporting requests in EU legislation require institutions to submit reports for legislative monitoring and policy implementation. As these meta-norms grow in complexity, compliance becomes increasingly challenging and burdensome. This paper leverages a Hybrid AI approach to detect, extract, and track reporting requests within the EU legislation using the RRVM ontology and European legal standards (AKN4EU, CELLAR, ELI). We investigate four interconnected and integrated areas: (1) detecting reporting requests and their concepts in legislative texts, (2) navigating normative references, (3) converting extracted legal knowledge into RDF for a Knowledge Graph, and (4) tracking modifications of reporting requests over time. Our approach compares machine learning (ML) and large language models (LLMs) for detection, demonstrating the advantages and limitations of both. By structuring Reporting Requests into a dynamic knowledge graph, our method improves the handling of Reporting Requests, reduces their administrative burden, and supports better legislative drafting and policy monitoring.

We propose a novel tool to provide judicial explanations from provisions encoded in LegalRuleML. The tool performs reasoning based on a translation of the provisions in Defeasible Deontic Logic.

One of the most important legal assessments during legislative drafting proposals in Parliament is to check whether the bills have some provisions clashing with the Constitutional norms. Sometimes this situation is intentionally created by the proposer of a party to underline inconsistencies or incompleteness in the legal systems according to the reality of the society, but frequently, they are mistakes to avoid. This paper presents a method to calculate a multi-factor index of presumption of unconstitutionality of the provisions of a bill, to inform the proponent concerning a possible constitutional conflict. The paper uses a large dataset of bills, coming from the Chamber of Deputies of Italy, combined with Constitutional Court judgments and abstracts, EU and National legislation (9095 Italian laws, 3710 bills, 2979 judgments from the Constitutional Court). The methodology used is hybrid, considering different technological components, linked in a pipeline: i) embedding similarity; ii) normative references navigation; iii) semantic topic of the bill; iv) legislation cited in the judgments score based on the abrogated.

The application of AI-based methods and Large Language Models (LLMs) to the legislative domain poses unique challenges, including the extensive usage of normative references, the complexity of legal language, as well as the ever-changing nature of legal documents. We propose a multilingual (English-Italian) LLM-based method to both retrieve and generate legislative definitions in the context of the European and Italian legislation. These definitions are a crucial aspect of legislative documents, as they create new meaning for specific concepts, and their generation is an open challenge for any automatic method. New definitions should not conflict with pre-existing ones, be consistent with the specific legal domain (e.g., food, energy, finance), and instead leverage them when necessary. Our method fosters a Retrieval Augmented Generation approach, using LLM and Agentic AI, which considers the validity of existing definitions, the hierarchy of legal sources, and investigates strategies to mitigate hallucinations in the generation of definitions. We provide a quantitative and qualitative evaluation of the results of our experiments.

This paper presents classification tools to detect hate speech topics using NLP tools in four different languages (Italian, Spanish, Germany, English) using the selected case-law from national and international jurisdiction. The research is conducted inside the FAST-LISA European project with the aim to classify the hate speech in online public debate.

This work focuses on the automatic classification of deontic sentences. It presents a novel Machine Learning approach which combines the power of Transfer Learning with the information provided by two famous LegalXML formats. In particular, different BERT-like neural architectures have been fine-tuned on the down-stream task of classifying rules from the European General Data Protection Regulation (GDPR) encoded in Akoma Ntoso and LegalRuleML. This work shows that fine-tuned language models can leverage the information provided in LegalXML documents to achieve automatic classification of deontic sentences and rules.

International Private Law (PIL) is a complex legal domain that presents frequent conflicting norms between the hierarchy of legal sources, legal domains, and the adopted procedures. Scientific research on PIL reveals the need to create a bridge between European and national laws. In this context, legal experts have to access heterogeneous sources, being able to recall all the norms and to combine them using case-laws and following the principles of interpretation theory. This clearly poses a daunting challenge to humans, whenever Regulations change frequently or are big-enough in size. Automated reasoning over legal texts is not a trivial task, because legal language is very specific and in many ways different from a commonly used natural language. When applying state-of-the-art language models to legalese understanding, one of the challenges is always to figure how to optimally use the available amount of data. This makes hard to apply state-of-the-art sub-symbolic question answering algorithms on legislative texts, especially the PIL ones, because of data scarcity. In this paper we try to expand previous works on legal question answering, publishing a larger and more curated dataset for the evaluation of automated question answering on PIL.

This paper presents an AI use-case developed in the project “Study on legislation in the era of artificial intelligence and digitization” promoted by the EU Commission Directorate-General for Informatics. We propose a hybrid technical framework where AI techniques, Data Analytics, Semantic Web approaches and LegalXML modelisation produce benefits in legal drafting activity. This paper aims to classify the corrigenda of the EU legislation with the goal to detect some criteria that could prevent errors during the drafting or during the publication process. We use a pipeline of different techniques combining AI, NLP, Data Analytics, Semantic annotation and LegalXML instruments for enriching the non-symbolic AI tools with legal knowledge interpretation to offer to the legal experts.

This study discusses the interplay between metrics used to measure the explainability of the AI systems and the proposed EU Artificial Intelligence Act. A standardisation process is ongoing: several entities (e.g. ISO) and scholars are discussing how to design systems that are compliant with the forthcoming Act and explainability metrics play a significant role. This study identifies the requirements that such a metric should possess to ease compliance with the AI Act. It does so according to an interdisciplinary approach, i.e. by departing from the philosophical concept of explainability and discussing some metrics proposed by scholars and standardisation entities through the lenses of the explainability obligations set by the proposed AI Act. Our analysis proposes that metrics to measure the kind of explainability endorsed by the proposed AI Act shall be risk-focused, model-agnostic, goal-aware, intelligible & accessible. This is why we discuss the extent to which these requirements are met by the metrics currently under discussion.

This paper presents the Open Knowledge Extraction (OKE) tools combined with natural language analysis of the sentence in order to enrich the semantic of the legal knowledge extracted from legal text. In particular the use case is on international private law with specific regard to the Rome I Regulation EC 593/2008, Rome II Regulation EC 864/2007, and Brussels I bis Regulation EU 1215/2012. A Knowledge Graph (KG) is built using OKE and Natural Language Processing (NLP) methods jointly with the main ontology design patterns defined for the legal domain (e.g., event, time, role, agent, right, obligations, jurisdiction). Using critical questions, underlined by legal experts in the domain, we have built a question answering tool capable to support the information retrieval and to answer to these queries. The system should help the legal expert to retrieve the relevant legal information connected with topics, concepts, entities, normative references in order to integrate his/her searching activities.

This paper presents a refinement of PrOnto ontology using a validation test based on legal experts’ annotation of privacy policies combined with an Open Knowledge Extraction algorithm. Three iterations were performed, and a final test using new privacy policies. The results are 75% of detection of concepts and relationships in the policy texts and an increase of 29% in the accuracy using the new refined version of PrOnto enriched with SKOSXL lexicon terms and definitions.

In the last fifteen years, Semantic Web technologies have been successfully applied to the legal domain. By composing all those techniques and theoretical methods, we propose an integrated framework for modelling legal documents and legal knowledge to support legal reasoning, in particular checking compliance. This paper presents a proof-of-concept applied to the GDPR domain, with the aim to detect infringements of privacy compulsory norms or to prevent possible violations using BPMN and Regorous engine.

This paper introduces PrOnto, the privacy ontology that models the GDPR main conceptual cores: data types and documents, agents and roles, processing purposes, legal bases, processing operations, and deontic operations for modelling rights and duties. The explicit goal of PrOnto is to support legal reasoning and compliance checking by employing defeasible logic theory (i.e., the LegalRuleML standard and the SPINDle engine).

In this paper we present the BO-ECLI Parser, an open framework for the extraction of legal references from case-law issued by judicial authorities of European member States. The problem of automatic legal links extraction from texts is tackled for multiple languages and jurisdictions by providing a common stack which is customizable through pluggable extensions in order to cover the linguistic diversity and specific peculiarities of national legal citation practices. The aim is to increase the availability in the public domain of machine readable references metadata for case-law by sharing common services, a guided methodology and efficient solutions to recurrent problems in legal references extraction, that reduce the effort needed by national data providers to develop their own extraction solution.

Akoma Ntoso is an OASIS Committee Specification Draft standard for the electronic representations of parliamentary, normative and judicial documents in XML. Recently, it has been officially adopted by the United Nations (UN) as the main electronic format for making UN documents machine-processable. However, Akoma Ntoso does not force nor define any formal ontology for allowing the description of real-world objects, concepts and relations mentioned in documents. In order to address this gap, in this paper we introduce the United Nations System Document Ontology (UNDO), i.e. an OWL 2 DL ontology developed and adopted by the United Nations that aims at providing a framework for the formal description of all these entities.

This paper presents the application of the Akoma Ntoso XML standard to the Swiss Federal Chancellery, in particular to the Official Publications Centre document workflow in relation of the IRIs/URIs naming convention. A robust proof-of-concept was conducted on a variety of document types and a IRIs/URIs resolver was implemented for managing the dereferencing to the official sources.

In this paper we present the motivation, use cases, design principles, abstract syntax, and initial core of LegalRuleML. The LegalRuleML-core is sufficiently rich for expressing legal sources, time, defeasibility, and deontic operators. An example is provided. LegalRuleMLis compared to related work.

In the last few years University of Turin and CIRSFID University of Bologna collaborated to pair NLP techniques and legal knowledge to detect modificatory provisions in normative texts. Annotating these modifications is a relevant and interesting problem, in that modifications affect the whole normative system; and legal language, though more regular than unrestricted language, is sometimes particularly convoluted, and poses specific linguistic issues. This paper focuses on two major aspects. First, we explore a combination between parsing and regular expressions; to the best of our knowledge, such hybrid strategy has never been proposed before to tackle the problem at hand. Secondly, we significantly extend past works coverage (basically focussed on substitution, integration and repeal modifications) in order to account for further twelve modification kinds. For the sake of conciseness, we fully illustrate and discuss only few modification types that are more relevant and interesting: suspension, prorogation of efficacy, postponement of efficacy and exception/derogation. These sorts of modifications appear particularly challenging, in that modifications in these categories make use of similar linguistic speech acts and verbs, and exhibit strong similarities in the linguistic syntactical patterns, to such an extent that to discern them is difficult for the legal expert, too. We describe the implemented system and report about an extensive experimentation on the new modificatory provisions. Results are discussed in order to improve both system's accuracy and annotation practice.

One open problem in the AI & Law community is how to provide computers with a basic understanding of legal concepts, and their relationship with legal texts and with the legal lexicon. We propose to add a layer to connect the linguistic description of the provisions to syntactic patterns using FramNet that can be exploited thought NLP tools. A deep-parsing and shallow-semantics approach has been devised to interpret and retrieve the characterizing components of legal modificatory provisions. In this paper we single out the case of efficacy suspension and show how FrameNet approach can provide profit especially to isolate temporal parameters and their interpretation.

Legal reasoning involves multiple temporal dimensions but the existing state of the art of legal representation languages does not allow us to easily combine expressiveness, performance and legal reasoning requirements. Moreover we also aim at the combination of legal temporal reasoning with the defeasible logic approach, maintaining a computable complexity. The contribution of this work is to extend LKIF-rules with temporal dimensions and defeasible tools, extending our previous work [17].

Typically legal reasoning involves multiple temporal dimensions. The contribution of this work is to extend LKIF-rules (LKIF is a proposed mark-up language designed for legal documents and legal knowledge in ESTRELLA Project [3]) with temporal dimensions. We propose an XML-schema to model the various aspects of the temporal dimensions in legal domain, and we discuss the design choices. We illustrate the use of the temporal dimensions in rules with the help of real life examples.

One of the main emerging research challenge in the legal documentation is to penetrate in the meaningful and in the semantic of the norm content using NLP techniques and isolate relevant part of the linguistic speech. This paper wants present a methodology for modeling the modificatory provisions in deep in order to provide all the necessary formalization for managing semi-automatically the consolidation process.

This paper presents a legal metadata interchange framework for adding semantics on top of the CEN Metalex standard. Indeed for exploiting its potentialities CEN Metalex needs to be enriched with a legal document ontology and moreover to an intermediate layer called LMIF for managing in automatic way the mapping between different local metadata on the general structural layer.

This paper presents a solution for managing heterogeneous legal XML resources using a native XML repository—called eXistrella—designed to enable a common query layer within a temporal environment equipped to manage the evolution of legislative documents over time. With eXistrella, the structure of a legal document is directly connected with the legal knowledge embedded in the corresponding norms, exploiting the XML syntax used in modeling such norms, and in this way, the gap can be bridged between the container (the document) and the way the content (the norm) is modeled.

We are concerned with the automatic semantic interpretation of legal modificatory provisions. We propose a novel approach which pairs deep syntactic parsing and a fine-grained taxonomy of legal modifications. Although still in a developmental stage, the implemented system can be used to annotate with meta-information modificatory provisions of NormaInRete documents.

This paper proposes some variants of Temporal Defeasible Logic (TDL) to reason about normative modifications. These variants make it possible to differentiate cases in which, for example, modifications at some time change legal rules but their conclusions persist afterwards from cases where also their conclusions are blocked.

Italian Ministry of Justice, with the contributions of the researcher centres, universities and public bodies, are presently engaged in an effort to work out shared standards with which to represent legal texts. Documents standardised under uniform formats and structures make it possible to link up distinct bodies of norms, and this in turn makes it easier to find and look up norms and design tools with which to process them, as when doing legal drafting and bringing out consolidated texts. This function is enabled by marking up the different parts of a legal text: its identification data (indicating text type, text number, date of delivery, and the like), its partitions (e.g., the articles and sections that make up its layout), and the normative references it contains.