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MIREL 2018 : MIREL 2018 workshop on MIning and REasoning with Legal texts | |||||||||||||||
Link: https://sites.google.com/view/mirelworkshop2018/ | |||||||||||||||
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Call For Papers | |||||||||||||||
Call for Papers
MIREL workshop MIning and REasoning with Legal texts in conjunction with LuxLogAI September 17th - Luxembourg https://sites.google.com/view/mirelworkshop2018/ Deadline: July 2, 2018 The aim of MIREL-2018 workshop is to bridge the gap between the community working on legal ontologies and NLP parsers and the community working on reasoning methods and formal logic, in line with the objectives of the MIREL (MIning and REasoning with Legal texts) project. The workshop aims at fostering the scientific discussion between approaches based on language technologies applied to the legal domain (representing legal knowledge) and those based on legal reasoning (using the legal knowledge to build specialized services and applications). Background Legal scholars and practitioners are feeling increasingly overwhelmed with the expanding set of legislation and case law available these days, which is assuming more and more of an international character. For example, European legislation is estimated to be 170,000 pages long, of which over 100,000 pages have been produced in the last ten years. Furthermore, legislation is available in unstructured formats, which makes it difficult for users to cut through the information overload. As the law gets more complex, conflicting, and ever changing, more advanced methodologies are required for analyzing, representing and reasoning on legal knowledge. The management of large repositories of norms, and the semantic access and reasoning to these norms are key challenges in Legal Informatics, which is experiencing growth in activity, also at the industrial level. Specifically, it is necessary to address both conceptual challenges, such as the role of legal interpretation in mining and reasoning, and computational challenges, such as the handling of big legal data, and the complexity of regulatory compliance. Legal domain has always been attractive to language and semantic technology because of its importance for the society with respect to globalization and common markets as well as for its challenges for formalization and specific language use. For this reason, a series of workshops centered in legal informatics and related topics have been organized recently; examples are ICAIL, JURIX, and JURISIN. Furthermore, several research projects in the legal domain have been recently funded by the EU and similar institutions, among which ``MIREL: MIning and REasoning with Legal texts''. http://www.mirelproject.eu/ Objective The development of NLP techniques and semantic technologies for automatic analysis and indexing of big data freely available on the web has created opportunities for building new approaches to improve the efficiency, comprehensibility, and consistency of legal systems. Semantic analysis aims at relating syntactic elements – which could be phrases, clauses, sentences, paragraphs, and whole documents - to their meanings in a given domain, including meanings specific to legal information. On the one hand, in recent years the EU has delivered huge amounts of resources on EU law in many languages (such as, EuroParl, JRC, etc.). On the other hand, the matured NLP and Semantic Web technology provides a good inventory: for formalizing the law data in the form of domain ontologies; for automating the process of relevant knowledge extraction from legal documents; and for representing it in form of Linked Data in RDF. This will support legal reasoning tasks such as better search possibilities, compliance checking and decision support, as well as a better presentation of the legal information to professional and non-professional stakeholders. The aim of MIREL-2018 workshop is to bridge the gap between the community working on legal ontologies and NLP parsers and the community working on reasoning methods and formal logic, towards these objectives described above. Topics Language technologies for processing of legal texts Legal reasoning (searching, compliance checking, decision support) Ontology design patterns for the legal domain Ontological modeling of legal data Core and domain ontologies for the legal domain Legal knowledge on the Web Legal Linked Open Data Machine learning and data mining for legal applications Adaptation of language processing modules to legal domain Large-scale normative reasoning Computational methods for legal reasoning Extraction of legal Named entities - legal citations, etc. Legal search engines - requirements, implementations, etc. Semantic annotations for legal texts Formal analysis of normative concepts and normative systems Formal analysis of the semantics/pragmatics of deontic and normative expressions in natural language Expressive vs. lightweight representations of legal knowledge Legislation and case law corpora in Linked Open Data Applications in the legal domain Submission We invite submissions up to 12 pages plus 3 additional pages for bibliography and appendix, in LNCS format. Papers will be published at the IfCoLoG Journal of Logics and their Applications. Authors shall submit their papers electronically via EasyChair before the due date in PDF format. https://sites.google.com/view/mirelworkshop2018/ Organizers Laura Alonso Alemany, Universidad Nacional de Córdoba (Argentina) Guillermo Simari, Universidad Nacional del Sur (Argentina) Leon van der Torre, University of Luxembourg (Luxembourg) LuxLogAI The MIREL 2018 workshop is the start of the Luxembourg Logic for AI Summit, which takes place 17-26 September 2018. https://luxlogai.uni.lu/ The Luxembourg Logic for AI Summit (LuxLogAI 2018) brings together RuleML+RR 2018, DecisionCAMP 2018, the Reasoning Web Summer School (RW 2018), the Global Conference on Artificial Intelligence (GCAI 2018), the MIREL 2018 workshop, and the Deduktionstreffen 2018. With its special focus theme on “methods and tools for responsible AI”, a core objective of LuxLogAI is to present the latest developments and progress made on the crucial question of how to make AI more transparent, responsible and accountable. |
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