ANGELIC II: An Improved Methodology for Representing Legal Domain Knowledge

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.