Taxonomic and Uncertain Reasoning inObject-Oriented

We present a coherent modeling and reasoning methodology to extend object-oriented databases towards taxonomic and uncertain knowledge. Current ISA-hierarchies are enriched by more general t-classes to improve conceptual modeling. The t-classes themselves are then integrated with probabilistic constraints to express uncertainty. The information content of such taxonomic and probabilistic knowledge can be visualized naturally by special Hasse-diagrams. The second contribution concerns eecient tax-onomic and probabilistic deduction. Through a careful interaction between taxonomic and probabilistic knowledge much better inference methods can be devised.