A Method for Conceptualising Legal Domains. An Example from the Dutch Unemployment Benefits Act

Authors: Visser, P.1; Bench-Capon, T.1; van den Herik, J.2

Source: Artificial Intelligence and Law, Volume 5, Number 3, September 1997 , pp. 207-242(36)

Publisher: Springer

Buy & download fulltext article:


Price: $47.00 plus tax (Refund Policy)


There has been much talk of the need to build intermediate models of the expertise required preparatory to constructing a knowledge-based system in the legal domain. Such models offer advantages for verification, validation, maintenance and reuse. As yet, however, few such models have been reported at a useful level of detail. In this paper we describe a method for conceptualising legal domains as well as its application to a substantial fragment of the Dutch Unemployment Benefits Act (DUBA).

We first discuss the intermediate models (called expertise models), then present a three-stage method for their construction, drawing on the CommonKADS work in knowledge acquisition, conceptual models of statute law, and the KANT method of knowledge analysis. Subsequently, we describe how these techniques were applied to the DUBA, and provide detailed examples of the resulting model. Finally, conclusions on the framework and guidelines are given as well as means of recording and presenting the various design choices.

Keywords: conceptual models; ontologies; system design

Document Type: Regular Paper

Affiliations: 1: Department of Computer Science, University of Liverpool, P.O. Box 147, Liverpool L69 7ZF, United Kingdom (E-mail: {pepijn, tbc}@csc.liv.ac.uk) 2: Department of Law and Computer Science, University of Leiden, P.O. Box 9521, 2300 RA Leiden, The Netherlands (E-mail: jfricd@ruljur.leidenuniv.nl)

Publication date: September 1, 1997

Related content


Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content

Text size:

A | A | A | A
Share this item with others: These icons link to social bookmarking sites where readers can share and discover new web pages. print icon Print this page