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A Legal Ontology Refinement Support Environment Using a Machine-Readable Dictionary

A Legal Ontology Refinement Support Environment Using a Machine-Readable Dictionary This paper discusses how to refine a given initial legal ontology using an existing MRD (Machine-Readable Dictionary). There are two hard issues in the refinement process. One is to find out those MRD concepts most related to given legal concepts. The other is to correct bugs in a given legal ontology, using the concepts extracted from an MRD. In order to resolve the issues, we present a method to find out the best MRD correspondences to given legal concepts, using two match algorithms. Moreover, another method called a static analysis is given to refine a given legal ontology, based on the comparison between the initial legal ontology and the best MRD correspondences to given legal concepts. We have implemented a software environment to help a user refine a given legal ontology based on these methods. The empirical results have shown that the environment works well in the field of Contracts for the International Sale of Goods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence and Law Springer Journals

A Legal Ontology Refinement Support Environment Using a Machine-Readable Dictionary

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Publisher
Springer Journals
Copyright
Copyright © 1997 by Kluwer Academic Publishers
Subject
Computer Science; Artificial Intelligence (incl. Robotics); International IT and Media Law, Intellectual Property Law; Philosophy of Law; Legal Aspects of Computing; Information Storage and Retrieval
ISSN
0924-8463
eISSN
1572-8382
DOI
10.1023/A:1008220029904
Publisher site
See Article on Publisher Site

Abstract

This paper discusses how to refine a given initial legal ontology using an existing MRD (Machine-Readable Dictionary). There are two hard issues in the refinement process. One is to find out those MRD concepts most related to given legal concepts. The other is to correct bugs in a given legal ontology, using the concepts extracted from an MRD. In order to resolve the issues, we present a method to find out the best MRD correspondences to given legal concepts, using two match algorithms. Moreover, another method called a static analysis is given to refine a given legal ontology, based on the comparison between the initial legal ontology and the best MRD correspondences to given legal concepts. We have implemented a software environment to help a user refine a given legal ontology based on these methods. The empirical results have shown that the environment works well in the field of Contracts for the International Sale of Goods.

Journal

Artificial Intelligence and Law – Springer Journals

Published: Sep 19, 2004

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