Using Legal Ontologies with Rules for Legal Textual Entailment

Biralatei Fawei; Adam Wyner; Jeff Z. Pan; Martin Kollingbaum

doi:10.1007/978-3-030-00178-0_21

Using Legal Ontologies with Rules for Legal Textual Entailment

Biralatei Fawei, Adam Wyner, Jeff Z. Pan, Martin Kollingbaum

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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
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 International Publishing
Pages	317-324
Number of pages	8
ISBN (Electronic)	978-3-030-00178-0
ISBN (Print)	978-3-030-00177-3
DOIs	https://doi.org/10.1007/978-3-030-00178-0_21
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
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
Abbreviated title	AICOL 2017
Country	United Kingdom
City	London
Period	12/06/17 → 12/06/17
Internet address	http://www.aicol.eu/?page_id=247

Access to Document

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https://link.springer.com/chapter/10.1007/978-3-030-00178-0_21Licence: All Rights Reserved

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Fawei, B., Wyner, A., Pan, J. Z., & Kollingbaum, M. (2018). Using Legal Ontologies with Rules for Legal Textual Entailment. In U. Pagallo, M. Palmirani, P. Casanovas, G. Sartor, & S. Villata (Eds.), AI Approaches to the Complexity of Legal Systems: AICOL VI-X 2015–2017 (pp. 317-324). ( Lecture Notes in Computer Science; Vol. 10791). Springer International Publishing. https://doi.org/10.1007/978-3-030-00178-0_21

Fawei, Biralatei ; Wyner, Adam ; Pan, Jeff Z. ; Kollingbaum, Martin. / Using Legal Ontologies with Rules for Legal Textual Entailment. AI Approaches to the Complexity of Legal Systems: AICOL VI-X 2015–2017. editor / Ugo Pagallo ; Monica Palmirani ; Pompeu Casanovas ; Giovanni Sartor ; Serena Villata. Cham : Springer International Publishing, 2018. pp. 317-324 ( Lecture Notes in Computer Science).

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title = "Using Legal Ontologies with Rules for Legal Textual Entailment",

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.",

author = "Biralatei Fawei and Adam Wyner and Pan, {Jeff Z.} and Martin Kollingbaum",

year = "2018",

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editor = "Ugo Pagallo and Monica Palmirani and Pompeu Casanovas and Giovanni Sartor and Serena Villata",

booktitle = "AI Approaches to the Complexity of Legal Systems",

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Fawei, B, Wyner, A, Pan, JZ & Kollingbaum, M 2018, Using Legal Ontologies with Rules for Legal Textual Entailment. in U Pagallo, M Palmirani, P Casanovas, G Sartor & S Villata (eds), AI Approaches to the Complexity of Legal Systems: AICOL VI-X 2015–2017. Lecture Notes in Computer Science, vol. 10791, Springer International Publishing, Cham, pp. 317-324, VIII Workshop on Artificial Intelligence and the Complexity of Legal Systems, London, United Kingdom, 12/06/17. https://doi.org/10.1007/978-3-030-00178-0_21

Using Legal Ontologies with Rules for Legal Textual Entailment. / Fawei, Biralatei; Wyner, Adam; Pan, Jeff Z.; Kollingbaum, Martin.

AI Approaches to the Complexity of Legal Systems: AICOL VI-X 2015–2017. ed. / Ugo Pagallo; Monica Palmirani; Pompeu Casanovas; Giovanni Sartor; Serena Villata. Cham : Springer International Publishing, 2018. p. 317-324 ( Lecture Notes in Computer Science; Vol. 10791).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Using Legal Ontologies with Rules for Legal Textual Entailment

AU - Fawei, Biralatei

AU - Wyner, Adam

AU - Pan, Jeff Z.

AU - Kollingbaum, Martin

PY - 2018/10/23

Y1 - 2018/10/23

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-030-00178-0_21

DO - 10.1007/978-3-030-00178-0_21

M3 - Conference contribution

SN - 978-3-030-00177-3

T3 - Lecture Notes in Computer Science

SP - 317

EP - 324

BT - AI Approaches to the Complexity of Legal Systems

A2 - Pagallo, Ugo

A2 - Palmirani, Monica

A2 - Casanovas, Pompeu

A2 - Sartor, Giovanni

A2 - Villata, Serena

PB - Springer International Publishing

CY - Cham

T2 - VIII Workshop on Artificial Intelligence and the Complexity of Legal Systems

Y2 - 12 June 2017 through 12 June 2017

ER -

Fawei B, Wyner A, Pan JZ, Kollingbaum M. Using Legal Ontologies with Rules for Legal Textual Entailment. In Pagallo U, Palmirani M, Casanovas P, Sartor G, Villata S, editors, AI Approaches to the Complexity of Legal Systems: AICOL VI-X 2015–2017. Cham: Springer International Publishing. 2018. p. 317-324. ( Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-00178-0_21