Schema Evolution publication categorizer

Many approaches have been proposed for the
of mapping categories (classes) from a source
to classes in a master taxonomy. Most of techniques,
however, ignore the hierarchical structure
taxonomies. In this paper, we propose a maximum likelihood
based framework which exploits the hierarchical
structure to obtain a more natural mapping between
source classes and the master taxonomy. Furthermore,
unlike previous work, our technique also inserts
classes into appropriate places of the master
creating new categories if required. We evaluate approach
on text and hyperspectral datasets.