Taxonomic reasoning is a typical inference task performed by many AI knowledge representation systems. We illustrate the effectiveness of taxonomic reasoning techniques as an active support to knowledge acquisition and schemas design in the advanced database environment LOGIDATA+, supporting complex objects and a rule-based language. The developed idea is that, by extending complex object data models with defined classes, it is possible to infer ISA relationships (i.e. compute subsumption) between classes on the basis of their descriptions. From a theoretical point of view, this approach makes it possible to give a formal definition of consistency to a schema, while, from a pragmatic point of view, it is possible to automatically classify a new class in the correct position of a given taxonomy.