Abstract
Law is an explicit system of rules to govern the behaviour of people. Legal practitioners must learn to apply legal knowledge to the facts at hand. The United States Multistate Bar Exam (MBE) is a professional test of legal knowledge, where passing indicates that the examinee understands how to apply the law. This paper describes an initial attempt to model and implement the automatic application of legal knowledge using a rule-based approach. An NLP tool extracts information (e.g. named entities and syntactic triples) to instantiate an ontology relative to concepts and relations; ontological elements are associated with legal rules written in SWRL to draw inferences to an exam question. The preliminary results on a small sample are promising. However, the main development is the methodology and identification of key issues for future analysis.
Original language | English |
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Title of host publication | AI Approaches to the Complexity of Legal Systems |
Subtitle of host publication | AICOL VI-X 2015–2017 |
Editors | Ugo Pagallo, Monica Palmirani, Pompeu Casanovas, Giovanni Sartor, Serena Villata |
Place of Publication | Cham |
Publisher | Springer |
Pages | 317-324 |
Number of pages | 8 |
ISBN (Electronic) | 978-3-030-00178-0 |
ISBN (Print) | 978-3-030-00177-3 |
DOIs | |
Publication status | Published - 23 Oct 2018 |
Event | VIII Workshop on Artificial Intelligence and the Complexity of Legal Systems - King's College London, London, United Kingdom Duration: 12 Jun 2017 → 12 Jun 2017 http://www.aicol.eu/?page_id=247 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer, Cham |
Volume | 10791 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Workshop
Workshop | VIII Workshop on Artificial Intelligence and the Complexity of Legal Systems |
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Abbreviated title | AICOL 2017 |
Country/Territory | United Kingdom |
City | London |
Period | 12/06/17 → 12/06/17 |
Internet address |