A dataset of RDF licenses

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

Assigning Creative Commons Licenses to Research Metadata: Issues and Cases

This paper discusses the problem of lack of clear licensing and transparency of usage terms and conditions for research metadata. Making research data connected, discoverable and reusable are the key enablers of the new data revolution in research. We discuss how the lack of transparency hinders discovery of research data and make it disconnected from the publication and other trusted research outcomes. In addition, we discuss the application of Creative Commons licenses for research metadata, and provide some examples of the applicability of this approach to internationally known data infrastructures.Comment: 9 pages. Submitted to the 29th International Conference on Legal Knowledge and Information Systems (JURIX 2016), Nice (France) 14-16 December 201

A Linked term bank of copyright-related terms

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

These Are Your Rights - A Natural Language Processing Approach to Automated RDF Licenses Generation

International audienc

Improvements in Information Extraction in Legal Text by Active Learning

International audienceManaging licensing information and data rights is becoming a crucial issue in the Linked (Open) Data scenario. An open problem in this scenario is how to associate machine-readable licenses specifications to the data, so that automated approaches to treat such information can be fruitfully exploited to avoid data misuse. This means that we need a way to automatically extract from a natural language document specifying a certain license a machine-readable description of the terms of use and reuse identified in such license. Ontology-based Information Extraction is crucial to translate natural language documents into Linked Data. This connection supports consumers in navigating documents and semantically related data. However , the performances of automated information extraction systems are far from being perfect, and rely heavily on human intervention, either to create heuristics, to annotate examples for inferring models, or to interpret or validate patterns emerging from data. In this paper, we apply different Active Learning strategies to Information Extraction (IE) from licenses in English, with highly repetitive text, few annotated or unannotated examples available, and very fine precision needed. We show that the most popular approach to active learning, i.e., uncertainty sampling for instance selection, does not provide a good performance in this setting. We show that we can obtain a similar effect to that of density-based methods using uncertainty sampling , by just reversing the ranking criterion, and choosing the most certain instead of the most uncertain instances

YABAL-A : compatibilidad entre licencias Creative Commons y cálculo de licencias de obras derivadas

Esta tesis trata sobre la adopción del uso de licencias libres, más específicamente sobre las licencias Creative Commons. En particular aborda la reutilización de obras y la problemática subyacente que esto acarrea, entre las cuales se encuentra: la incompatibilidad entre distintas licencias Creative Commons, la posibilidad de determinar la licencia de una obra derivada y la referenciación de las obras insumos y créditos de las obras derivadas. En concreto se analiza el caso de la Universidad de la República (Uruguay), que ofrece recursos bajo licencias Creative Commons, por medio de sistemas montados sobre: DSpace, WordPress, Moodle, Moodle-Hub y Pumukit. Se realizaron evaluaciones de las aplicaciones que soportan estos sistemas y otras herramientas de software para ver las funcionalidades que brindan para el uso de licencias Creative Commons. Se realiza durante el trabajo un relevamiento de herramientas, en el cual no se detecta ninguna que permita resolver los problemas (de manera total y completa) que surgen del uso de licencias. Por lo cual es un aporte central de este trabajo la propuesta de una solución integral que permite determinar la compatibilidad entre licencias, el cálculo automático de la licencia y el despliegue de los créditos de una obra derivada. También se desarrolla una aplicación y un módulo en lenguaje PHP que brindan una solución concreta para estos problemas