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ANGELIC II: An Improved Methodology for Representing Legal Domain Knowledge

Authors:

Katie Atkinson,

Trevor Bench-CaponAuthors Info & Claims

ICAIL '23: Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law

Pages 12 - 21

https://doi.org/10.1145/3594536.3595137

Published: 07 September 2023 Publication History

Abstract

The purpose of this paper is to provide a definitive, up-to-date account of a methodology has that been proven successful for representing and reasoning about legal domains. The ANGELIC (ADF for kNowledGe Encapsulation of Legal Information for Cases) methodology was originally developed to exploit then recent developments in knowledge representation techniques that lend themselves well to capturing factor-based reasoning about legal cases. The methodology is situated firmly within the tradition of research in AI and Law that aims to build systems that are knowledge rich in terms of the domain expertise that is emulated within the systems. When the methodology was first introduced, it was demonstrated on academic examples, but it was subsequently used in and evaluated on a variety of real world domains for external clients. This set of evaluation exercises yielded a variety of learning points as the methodology was applied to different legal domains with their own particular features. These learning points, and the extensions to the methodology that follow from them, urge a consolidation exercise to provide an updated version of the methodology that reflects how it has matured over time. This paper represents a milestone in the development of the methodology in that it presents the ANGELIC II Domain Model, along with a description of its constituent parts, and demonstrates its application through a case study in a key evaluation domain.

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Cited By

Mumford JAtkinson KBench-Capon T(2024)Annotated insights into legal reasoning: A dataset of Article 6 ECHR casesArgument & Computation10.3233/AAC-24000215:2(113-119)Online publication date: 4-Jun-2024
https://doi.org/10.3233/AAC-240002
Bench-Capon T(2024)Intermediate factors and precedential constraintArtificial Intelligence and Law10.1007/s10506-024-09405-xOnline publication date: 17-May-2024
https://doi.org/10.1007/s10506-024-09405-x

Index Terms

ANGELIC II: An Improved Methodology for Representing Legal Domain Knowledge
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning

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cover image ACM Other conferences

ICAIL '23: Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law

June 2023

499 pages

ISBN:9798400701979

DOI:10.1145/3594536

Conference Chair:
Francisco Andrade
University of Minho, Portugal
,
Program Chair:
Matthias Grabmair
Technical University of Munich, Germany

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGAI: ACM Special Interest Group on Artificial Intelligence
AAAI: Am Assoc for Artifical Intelligence

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Association for Computing Machinery

New York, NY, United States

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Published: 07 September 2023

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ICAIL 2023: Nineteenth International Conference on Artificial Intelligence and Law

June 19 - 23, 2023

Braga, Portugal

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Cited By

Mumford JAtkinson KBench-Capon T(2024)Annotated insights into legal reasoning: A dataset of Article 6 ECHR casesArgument & Computation10.3233/AAC-24000215:2(113-119)Online publication date: 4-Jun-2024
https://doi.org/10.3233/AAC-240002
Bench-Capon T(2024)Intermediate factors and precedential constraintArtificial Intelligence and Law10.1007/s10506-024-09405-xOnline publication date: 17-May-2024
https://doi.org/10.1007/s10506-024-09405-x

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