This work uses Large Language Models to process an important piece of Spanish legislation: the Workers’ Statute. The proposed method extracts the relevant events in its articles using a GPT-3.5 model and represents the entities involved in the events and the relationships between them as RDF triples. The experiments carried out to select a high-performance strategy include both zero- and few-shot learning tests. Finally, this work proposes a strategy to uplift the extracted legal relations into a legal knowledge graph.

With the enforcement of the European Union’s General Data Protection Regulation, users of Web services – the ‘data subjects’ –, which are powered by the intensive usage of personal data, have seen their rights be incremented, and the same can be said about the obligations imposed on the ‘data controllers’ responsible for these services. In particular, the ‘Right of Access’, which gives users the option to obtain a copy of their personal data as well as relevant details such as the categories of personal data being processed or the purposes and duration of said processing, is putting increasing pressure on controllers as their execution often requires a manual response effort, and the wait time is negatively affecting the data subjects. In this context, the main goal of this work is the development of an API, which builds on the previously mentioned structured information, to assist controllers in the automation of replies to right of access requests. The implemented API method is then used in the implementation of a Solid application whose main goal is to assist users in exercising their right of access to data stored in Solid Pods.

This paper presents WhenTheFact, a tool that identifies relevant events from European judgments. It is able to extract the structure of the document, as well as when the event happened and who carried it out. WhenTheFact builds then a timeline that allows the user to navigate through the annotations in the document.

Language resources are a key component of natural language processing and related research and applications. Users of language resources have different needs in terms of format, language, topics, etc. for the data they need to use. Linghub (McCrae and Cimiano, 2015) was first developed for this purpose, using the capabilities of linked data to represent metadata, and tackling the heterogeneous metadata issue. Linghub aimed at helping language resources and technology users to easily find and retrieve relevant data, and identify important information on access, topics, etc. This work describes a rejuvenation and modernisation of the 2015 platform into using a popular open source data management system, DSpace, as foundation. The new platform, Linghub2, contains updated and extended resources, more languages offered, and continues the work towards homogenisation of metadata through conversions, through linkage to standardisation strategies and community groups, such as the Open Digital Rights Language (ODRL) community group.

The EU-funded project Lynx focuses on the creation of a knowledge graph for the legal domain (Legal Knowledge Graph, LKG) and its use for the semantic processing, analysis and enrichment of documents from the legal domain. This article describes the use cases covered in the project, the entire developed platform and the semantic analysis services that operate on the documents.

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.

This paper presents an architecture of a Personal Information Management System, in which individuals can define the access to their personal data by means of smart contracts. These smart contracts, running on the Ethereum blockchain, implement access control lists and grant immutability, traceability and verifiability of the references to personal data, which is stored itself in a (possibly distributed) file system. A distributed authorization mechanism is devised, where trust from multiple network nodes is necessary to grant the access to the data. To this aim, two possible alternatives are described: a Secret Sharing scheme and Threshold Proxy Re-Encryption scheme. The performance of these alternatives is experimentally compared in terms of execution time. Threshold Proxy Re- Encryption appears to be faster in different scenarios, in particular when increasing message size, number of nodes and the threshold value, i.e. number of nodes needed to grant the data disclosure.

A multi-lingual term bank of copyright-related terms has been published connecting WIPO definitions, IATE terms and definitions from Creative Commons licenses. These terms have been hierarchically arranged, spanning multiple languages and targeting different jurisdictions. The term bank has been published as a TBX dump file and is publicly accessible as linked data. Models for the RDF data structure are based on Lemon and W3C Recommendations. The term bank has been used to annotate common licenses in the RDFLicense dataset.

This paper describes a representation of the legal framework in the air transport passenger's rights domain and the foremost incidents that trigger the top of consumer complaints ranking in the EU. It comprises the development of a small network of three ontologies, formalisation of scenarios, specification of properties and identification of relations. The approach is illustrated by means of a case study based in the context of a real life cancelled flight incident. This is part of an intended support-system that aims to provide both consumers and companies with relevant legal information to enhance the decision-making process.

This paper describes a dataset of licenses expressed as RDF. The most important rights and conditions present in licenses for software, data and general works are expressed with the Open Digital Rights Language (ODRL) 2.0 vocabulary and extensions thereof. The dataset contains licenses identified by a dereferenceable URI, which are served with content negotiation providing a double representation for humans and machines alike. This feature enables a generalized machine-to-machine commerce if generally adopted.

In the entertainment domain users tweet about their expectations and opinions regarding upcoming, current and past experiences, while companies advertise and promote the shows. This characterization, important for customers and companies, goes beyond traditional sentiment analysis where the polarity of the sentiments expressed in opinions is usually identified as positive, negative or neutral. The authors investigate different tweet representation models, including bags of words and probabilistic topic models, to shed light on the semantics of the messages. Their experiments show that topic-based models generated with Latent Dirichlet Allocation (LDA) yield, most of the times, better categorizations when compared to TF-IDF based features, particularly when these models are enriched with natural language features and specific Twitter slang.

Abstract-Polygonal lines constitute a key graphical primitive in 2D vector graphics data. Thus, the ability to apply a digital watermark to such an entity would enable the watermarking of cartoons, drawings, and Geographical Information Systems (GIS) data in vector graphics format. This paper builds on and extends an existing algorithm that achieves polygonal line watermarking by modifying the Fourier descriptors magnitude in an imperceptible way. Watermarks embedded by this technique can be detected in rotated, translated, scaled, or reflected polygonal lines. The detection of such watermarks had been previously carried out through a correlator detector. In this paper, analysis of the statistics of the Fourier descriptors is exploited to devise an optimal blind detector. Furthermore, the problem of watermarking multiple lines, as well as other implementation issues are being addressed. Experimental results verify the imperceptibility and robustness of the proposed method.