Taxonomic and Uncertain Integrity Constraints in Object−Oriented Databases − the TOP Approach

Abstract

We present a coherent modeling and reasoning methodology to extend object-oriented databases towards taxonomic and uncertain integrity constraints. Our so-called TOP database model enriches current ISA-hierarchies by more general t-classes to improve conceptual modeling. The t-classes themselves are then integrated with probabilistic constraints to express uncertainty. We give an efficient algorithm for checking the modeling-consistency of a probabilistic knowledge base. As a typical application domain for TOP, we exemplify how various aspects of a portfolio management system can be modeled. We also demonstrate that recent probabilistic inference methods, relying on a careful interaction between taxonomic and uncertain knowledge, can be applied in this context.