In the devolved legal system of the United Kingdom (UK), legislative provisions may apply differently across regions such as England, Wales, Scotland, and Northern Ireland. Accurately determining this territorial scope is essential for legal interpretation and AI-assisted legal tools. However, metadata capturing jurisdictional applicability is inconsistently format, as only a few Acts include Territorial Application Annexes. This study presents a case study using the Education Act 2005 to evaluate the accuracy of automated methods for identifying territorial scope. We found that only 46.9% of sections matched in jurisdictional coverage. The best-performing approach achieved nearly 80% accuracy, showing that LLMs can effectively support scalable and explainable territorial disambiguation.

Abstract This paper introduces a computational ontology for deontic reasoning, fully implemented in RDF* and SPARQL*, designed to support reasoning in the presence of irresolvable conflicts. These are situations in which two or more norms prescribe incompatible obligations, prohibitions or permissions, without any clear priority among them. Existing approaches in formal deontic logic are typically limited to the propositional level, focused primarily on obligation as the central modality, and are rarely implemented in a way that is compatible with Semantic Web standards. The framework presented here addresses these limitations by providing a first-order, Resource Description Framework (RDF)-based formalization of all standard deontic modalities: obligations, permissions, optionality and their negations. It supports the explicit representation and reasoning about violations and conflicts while also accounting for contextual constraints. The ontology integrates contributions from three research areas that have so far largely developed in isolation: RDF-based LegalTech solutions, reification-based models of Natural Language Semantics and conflict-tolerant approaches in formal deontic logic. By incorporating contradictions and conflicts into the object language, the ontology supports advanced reasoning tasks within a framework that adheres to W3C standards. This makes it suitable for integration into industrial LegalTech applications where normative reasoning is required1.

Abstract Automated compliance checking, i.e. the task of automatically assessing whether states of affairs comply with normative systems, has recently received a lot of attention from the scientific community, also as a consequence of the increasing investments in Artificial Intelligence technologies for the legal domain (LegalTech). The authors of this paper deem as crucial the research and implementation of compliance checkers that can directly process data in RDF format, as nowadays more and more (big) data in this format are becoming available worldwide, across a multitude of different domains. Among the automated technologies that have been used in recent literature, to the best of our knowledge, only two of them have been evaluated with input states of affairs encoded in RDF format. This paper formalizes a selected use case in these two technologies and compares the implementations, also in terms of simulations with respect to shared synthetic datasets.

Reified Input/Output logic [29] has been recently proposed to handle natural language meaning in Input/Output logic [17]. So far, the research in reified I/O logic has focused only on KR issues, specifically on how to use the formalism for representing contextual meaning of norms (see [28]). This paper is the first attempt to investigate reasoning in reified I/O logic, specifically compliance checking. This paper investigates how to model reified I/O logic formulae in Shapes Constraint Language (SHACL) [2], a recent W3C recommendation for validating and reasoning with RDFs/OWL.

This paper is concerned with the goal of maintaining legal information and compliance systems: the ‘resource consumption bottleneck’ of creating semantic technologies manually. The use of automated information extraction techniques could significantly reduce this bottleneck. The research question of this paper is: How to address the resource bottleneck problem of creating specialist knowledge management systems? In particular, how to semi-automate the extraction of norms and their elements to populate legal ontologies? This paper shows that the acquisition paradox can be addressed by combining state-of-the-art general-purpose NLP modules with pre- and post-processing using rules based on domain knowledge. It describes a Semantic Role Labeling based information extraction system to extract norms from legislation and represent them as structured norms in legal ontologies. The output is intended to help make laws more accessible, understandable, and searchable in legal document management systems such as Eunomos (Boella et al., 2016).

The DAPRECO knowledge base (D-KB) is a repository of rules written in LegalRuleML, an XML formalism designed to represent the logical content of legal documents. The rules represent the provisions of the General Data Protection Regulation (GDPR). The D-KB builds upon the Privacy Ontology (PrOnto) (Palmirani et al., 2018), which provides a model for the legal concepts involved in the GDPR, by adding a further layer of constraints in the form of if-then rules, referring either to standard first order logic implications or to deontic statements. If-then rules are formalized in reified I/O logic (Robaldo and Sun, 2017) and then codified in (LegalRuleML, 2019). To date, the D-KB is the biggest knowledge base in LegalRuleML freely available online at (Robaldo et al., 2019).

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).

This paper presents a unifying text similarity measure (USM) for automated identification of national implementations of European Union (EU) directives. The proposed model retrieves the transposed provisions of national law at a fine-grained level for each article of the directive. USM incorporates methods for matching common words, common sequences of words and approximate string matching. It was used for identifying transpositions on a multilingual corpus of four directives and their corresponding national implementing measures (NIMs) in three different languages : English, French and Italian. We further utilized a corpus of four additional directives and their corresponding NIMs in English language for a thorough test of the USM approach. We evaluated the model by comparing our results with a gold standard consisting of official correlation tables (where available) or correspondences manually identified by domain experts. Our results indicate that USM was able to identify transpositions with average F-score values of 0.808, 0.736 and 0.708 for French, Italian and English Directive-NIM pairs respectively in the multilingual corpus. A comparison with state-of-the-art methods for text similarity illustrates that USM achieves a higher F-score and recall across both the corpora.

This paper presents a concept recognition system for European and national legislation. Current named entity recognition (NER) systems do not focus on identifying concepts which are essential for interpretation and harmonization of European and national law. We utilized the IATE (Inter-Active Terminology for Europe) vocabulary, a state-of-the-art named entity recognition system and Wikipedia to generate an annotated corpus for concept recognition. We applied conditional random fields (CRF) to identify concepts on a corpus of European directives and Statutory Instruments (SIs) of the United Kingdom. The CRF-based concept recognition system achieved an F1 score of 0.71 over the combined corpus of directives and SIs. Our results indicate the usability of a CRF-based learning system over dictionary tagging and state-of-the-art methods.

In this article, we propose to combine Input/Output logic, a well-known formalism for normative reasoning, with the reification-based approach of Jerry R. Hobbs. The latter is a wide-coverage logic for Natural Language Semantics (NLS) able to handle a fairly large set of linguistic phenomena into a simple logical formalism. The result is a new framework that we will call ‘reified Input/Output logic’. This article represents the first step of a long-term research aiming at filling the gap between Input/Output logic and the richness of NLS. We plan in our future work to use reified Input/Output logic as the underlying formalism for applications in legal informatics to process and reason on existing legal texts, which are available in natural language only.

In this paper we describe how the EUCases FP7 project is addressing the problem of lifting Legal Open Data to Linked Open Data to develop new applications for the legal information provision market by enriching structurally the documents (first of all with navigable references among legal texts) and semantically (with concepts from ontologies and classification). First we describe the social and economic need for breaking the accessibility barrier in legal information in the EU, then we describe the technological challenges and finally we explain how the EUCases project is addressing them by a combination of Human Language Technologies.

This paper looks at the use of recitals in the interpretation of EU legislation, and mechanisms for connecting them to normative provisions. The purposive approach to the interpretation of EU legislation taken by the European Court of Justice makes frequent references to recitals as helping to establish the purpose of normative provisions. Our research uses a cosine similarity based approach to link articles with relevant provisions to help legal professionals and lay end-users interpret the law. Such support can be used in legal knowledge-based systems.

In this work we present a working system for automatic classification of text documents into the EuroVoc multilingual thesaurus. EuroVoc contains around 7,000 categories with different levels of specificity. The system relies on a simple approach for the treatment of multi-label texts where each document may have more than one associated category. The classifier is based on the well-known Support Vector Machine algorithm trained using the JRC-Acquis corpus, containing around 23,000 documents labeled with six EuroVoc categories in average. The demonstration scenario will show the ability of the system to classify documents taken on site from the Eur-Lex web portal of the European Union, together with features for visualization and navigation of the texts at different granulatity.

We are witnessing a paradigm shift in Human Language Technology (HLT) that may well have an impact on the field comparable to the statistical revolution: acquiring large-scale resources by exploiting collective intelligence. An illustration of this new approach is Phrase Detectives , an interactive online game with a purpose for creating anaphorically annotated resources that makes use of a highly distributed population of contributors with different levels of expertise. The purpose of this article is to first of all give an overview of all aspects of Phrase Detectives, from the design of the game and the HLT methods we used to the results we have obtained so far. It furthermore summarizes the lessons that we have learned in developing this game which should help other researchers to design and implement similar games.

In this paper we present a novel method for the automatic classification of multi-label text documents. In principle, automatic classification of text is usually tackled by supervised Machine Learning techniques like Support Vector Machines (SVM), that typically achieve state-of-the-art accuracy in several domains. Nevertheless, SVM can not handle multi-labeled documents, thus a specific preprocessing of the data is needed. In this paper we present a novel technique for the transformation of multi-label data into mono-label that is able to maintain all the information, allowing the use of standard approaches like SVM. We then evaluate our system using JRC-Acquis-it, a large dataset of italian legislation that has been manually annotated according to EuroVoc, demonstrating the potential of our approach compared to the current state of the art.

In this paper we present a prototype for automatically identifying and classifying types of modifications in Italian legal text. The prototype is part of the Eunomos system, a legal knowledge management service that integrates and makes available legislation from various sources, while finding definitions and explanations of legal concepts in a given context. The design of the prototype is grounded on the error analysis of a previous prototype. The latter made use of dependency relations provided by the TUP parser, a multi-purpose parser for Italian. Since those syntactic relations were responsible of the majority of errors, we decided in the present tool to ignore them, and to rewrite an ad-hoc shallow parsing, based on the morphological analysis of the legal text (still provided by the TUP parser). We obtained performances much greater than those of the initial prototype. In particular, the level of precision of the classification in output is now close to 100%.

Before starting, I would like to ask reader’s opinion about the truth/falsity of certain NL statements. The statements are about figures depicting dots connected to stars. In the figures, we distinguish between dots and stars that are connected, i.e. such that every dot is connected with at least one star and every star is connected with at least one dot, and dots and stars that are totally connected, i.e. such that every dot is connected to every star. For instance, in (1), the dots d1, d2, and d3 are connected with the stars s1, s2, and s3 (on the left) while d4 and d5 are totally connected with s4, s5, and s6 (on the right).