Revisable knowledge discovery in databases

This paper introduces the idea of using nonmonotonic inheritance networks for the storage and maintenance of knowledge discovered in data (revisable knowledge discovery in databases | RKDD). While existing data mining strategies for knowledge discovery in databases (KDD) typically involve initial structuring through the use of identiication trees and the subsequent extraction of rules from these trees for use in rule-based expert systems, such strategies have diiculty in coping with additional information which may connict with that already used for the automatic generation of rules. In the worst case, the entire automatic sequence may have to be repeated. If nonmonotonic inheritance networks are used instead of rules for storing knowledge discovered in databases, additional connicting information can be inserted directly into such structures, thereby bypassing the need for recompilation.