Research in progress: report on the ICAIL 2017 doctoral consortium

This paper arose out of the 2017 international conference on AI and law doctoral consortium. There were five students who presented their Ph.D. work, and each of them has contributed a section to this paper. The paper offers a view of what topics are currently engaging students, and shows the diversity of their interests and influences

D10.1.1. Before analysis

WP Case study - Intelligent integrated decision support for legal professionalsThe objective of this document is to study the determining factors that exist in thelegal domain in Spain that can affect the achievement of a successful application in the Legal Case Study in the SEKT project. To do this,several surveys are presented, such as a user analysis, a domain analysis,a requirements analysis,a state of the art on legal applications anda state of the art on legal ontologie

Towards a linked information architecture for integrated law enforcement

Ponència presentada al Workshop on Linked Democracy: Artificial Intelligence for Democratic Innovation co-located with the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017) celebrat el 19 d'agost de 2017 a Melbourne, AustraliaLaw enforcement agencies are facing an ever-increasing flood of data to be acquired, stored, assessed and used. Automation and advanced data analy-sis capabilities are required to supersede traditional manual work processes and legacy information silos by automatically acquiring information from a range of sources, analyzing it in the context of on-going investigations, and linking it to other pieces of knowledge pertaining to the investigation. This paper outlines a modular architecture for management of linked data in the law enforcement domain and discusses legal and policy issues related to workflows and infor-mation sharing in this context

Modelling legal knowledge for GDPR compliance checking

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

Legal Knowledge Extraction for Knowledge Graph Based Question-Answering

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

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)