Knowledge acquisition for classification systems
Takahiro Miura, Isamu Shioya · 2005
We propose a new method to mine a type scheme semi-automatically from an initial database scheme and the instances. Our data model assumes that one entity may have more than one type and classification (or type scheme). It might be appropriate when each entity is classified into at most k (least general) classes with respect to the ISA hierarchy, to keep database processing efficient. Our method differs from others in evolving ISA hierarchy by introducing a semantical metric. We propose a sophisticated algorithm to simplify, evolve and generate type schemes.