East of Neuchatel: a universal model for the representation of statistical taxonomy systems

Abstract

The Neuchatel data model for statistical classification systems can be regarded as a standard for classification management in official statistics. However, despite its unquestioned benefits, the model remains rather informal. For computational use, the semantics of model structures have to be specified more accurately. Focusing on formal underpinnings of the model required to support automated processing of classification structures, this paper proposes an alternative approach. The Neuchatel concepts are simplified and generalized, leading to a universal model of hierarchical-taxonomic classification structures featuring precise semantics and supporting typical algebraic transformations related to classification systems.