On taxonomic reasoning in conceptual design

Abstract

Taxonomic reasoning is a typical task performed by many AI knowledge representation systems. In this paper, the effectiveness of taxonomic reasoning techniques as an active support to knowledge acquisition and conceptual schema design is shown. The idea developed is that by extending conceptual models with defined concepts and giving them rigorous logic semantics, it is possible to infer isa relationships between concepts on the basis of their descriptions. From a theoretical point of view, this approach makes it possible to give a formal definition for consistency and minimality of a conceptual schema. From a pragmatic point of view it is possible to develop an active environment that allows automatic classification of a new concept in the right position of a given taxonomy, ensuring the consistency and minimality of a conceptual schema. A formalism that includes the data semantics of models giving prominence to type constructors (E/R, TAXIS, GALILEO) and algorithms for taxonomic inferences are presented: their soundness, completeness, and tractability properties are proved. Finally, an extended formalism and taxonomic inference algorithms for models giving prominence to attributes (FDM, IFO) are given.