D2.4 Ontology population: connecting legal text to ontology concepts and instances Grant Agreement nº: 690974 Project Acronym: MIREL Project Title: MIning and REasoning with Legal texts Website: http://www.mirelproject.eu/ Contractual delivery date: 31/12/2018 Actual delivery date: 31/12/2018 Contributing WP 2 Dissemination level: Public Deliverable leader: UL Contributors: UNITO, CORDOBA, UNIBO, INRIA D2.4 Ontology population: connecting legal text to ontology concepts and instances This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 690974 MIREL- 690974 Page 2 of 55 09/03/2019 D2.4 Ontology population: connecting legal text to ontology concepts and instances Document History Version Date Author Partner Description 0.1 30/11/2010 Livio Robaldo UL Initial draft 1.0 30/12/2017 Livio Robaldo UL Final Version Contributors Partner Name Role Contribution UL Livio Robaldo Editor Main editor of the document UNITO Luigi Di Caro Contributor Writing of specific sections Cordoba Laura Alonso Alemany Contributor Writing of specific sections UNIBO Monica Palmirani Contributor Writing of specific sections INRIA Serena Villata Contributor Writing of specific sections Disclaimer: The information in this document is provided “as is”, and no guarantee or warranty is given that the information is fit for any particular purpose. MIREL consortium members shall have no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may result from the use of these materials subject to any liability which is mandatory due to applicable law. MIREL- 690974 Page 3 of 55 09/03/2019 D2.4 Ontology population: connecting legal text to ontology concepts and instances Table of Contents Executive Summary ............................................................................................................................. 5 1 Introduction ................................................................................................................................ 6 2 The DAPRECO knowledge base and the Privacy Ontology (PrOnto) ........................................... 7 2.1 The General Data Protection Regulation (GDPR) .................................................................... 8 2.2 The Privacy Ontology (PrOnto) ................................................................................................ 9 2.3 The DAPRECO knowledge base ............................................................................................. 11 3 The Eunomos/MenslegiS system and the European Legal Taxonomy Syllabus ....................... 13 3.1 The Eunomos System ............................................................................................................ 13 3.1.1 Statistical NLP procedures used in Eunomos ............................................................ 16 3.1.2 Rule-based NLP procedures used in Eunomos .......................................................... 20 3.2 The European Legal Taxonomy Syllabus ............................................................................... 22 3.2.1 The European Legal Taxonomy Syllabus Schema ...................................................... 24 3.2.2 The European Legal Taxonomy Syllabus ontology .................................................... 28 4 Legal NERC with ontologies ....................................................................................................... 33 4.1 Establish a mapping between LKIF and YAGO....................................................................... 33 4.2 Domain and classes to be learned ........................................................................................ 34 4.3 Named entity recognition, classification, and linking trained on the LKIF+YAGO ontology . 35 4.3.1 Legal NERC via curriculum learning ........................................................................... 36 4.3.2 Legal NERC via support vector machines .................................................................. 41 5 Conclusions ............................................................................................................................... 50 References ......................................................................................................................................... 50 MIREL- 690974 Page 4 of 55 09/03/2019 D2.4 Ontology population: connecting legal text to ontology concepts and instances Executive Summary The goal of this deliverable is to report the achieved results and the ongoing work of the MIREL project devoted to connect legal text to concepts and instances in legal ontologies. The connection may be done manually or via NLP procedures for mining named entities and recurring linguistic patterns from legal documents and connect them to legal ontologies. In fact, recurring patterns usually correspond to relevant concepts, which may be associated with computational constructs such as classes, individuals, or relations belonging to legal ontologies. For this reason, ontologies may be also created (and not only populated) via semi-automatic ontology learning techniques, i.e., by applying automated ontology inference and population techniques, and combining automated methods and human assessment with, e.g., curriculum learning techniques. MIREL- 690974 Page 5 of 55 09/03/2019 D2.4 Ontology population: connecting legal text to ontology concepts and instances 1 Introduction Ontology as a branch of philosophy is described by [Smith, 2008] as “the science of what is, of the kinds of objects, properties, events, processes and relations in every area of reality”. Ontologies that explicitly describe reality, or provide “an explicit specification of a conceptualization” [Gruber, 1993], are also an important area of computer science. The objective in computer science is to provide people, or more typically artificial agents, with structured and navigable knowledge about entities and their inter-relations. Ontologies can help people share and reference knowledge about concepts in general or specialist areas, in one or more languages. Ontologies can also be used for information technology tasks such as semantic searches, interoperability between systems, or to facilitate reasoning and problem solving in artificial intelligence. The peculiarities of the legal domain are many: laws are written in “legalese” - a domain-specific sublanguage that inherits all the expressivity and ambiguity of natural language with additional terms of its own that are often obscure, subject to changes over time, contextually defined, ill- defined, subject to interpretation to deal with their vagueness, defined in incompatible ways in different legal sources, or difficult to translate into different languages. As [Peller, 1985] stated: “legal discourse can never escape its own textuality”. For this reason, legal ontologies are an active field of research because they could be useful in a range of different scenarios. Legal ontologies could help legal practitioners and scholars keep up to date with continuous changes in the law and understand legal sub-languages outside their own areas of expertise or jurisdiction. They could help legislators draft legislation with clarity and consistency. Moreover, a multi-jurisdictional legal ontology could help show the inter-relationship between, for instance, national and European Union terms and foster harmonization, a need recognized, e.g., by the Mandelkern Group Report [Mandelkern, 2001], for the EU Commission. The group stressed the need for internal coherence and consistency in the use of EU legal terms, as well as external coherence - consistency in the transposition of legal concepts into national law. Legal ontologies could potentially help translators of EU legislation become aware of how their choice of terms will be interpreted in different jurisdictions and help drafters of national legislation ensure greater consistency in their use of terminology when transposing European legislation. They could also serve as a useful tool in legal search, information retrieval, automatic translation, automated reasoning and regulatory compliance verification. Finally, legal ontologies could help users find similar or related legislation, or compare the transposition of laws in national jurisdiction - useful for legal scholars in comparative law or lawyers who deal with cross-border issues and international financial institutions. In light of this, concepts in legal ontologies need to be connected with textual spans referring to them, from legislation, case law, or similar documents. The connection may be done manually, as in the DAPRECO knowledge base, illustrated below in section 2, or via NLP procedures for mining named entities and recurring linguistic patterns from legal documents, as in the Eunomos system, illustrated in section 3, which defines a suite of both statistical and rule-based NLP procedures to connect the legal texts in its knowledge base to the concept of the European Legal Taxonomy MIREL- 690974 Page 6 of 55 09/03/2019 D2.4 Ontology population: connecting legal text to ontology concepts and instances Syllabus Ontology. Finally, since legal texts are plenty of recurring patterns, which usually correspond to relevant (legal) concepts, legal ontologies may be also created (and not only populated) via semi-automatic ontology learning techniques, i.e., by applying automated ontology inference and population techniques,