We investigate machine learning methods for automatically integrating objects from different
taxonomies into a master taxonomy. This problem is not only currently pervasive on the
Web, but is also important to the emerging Semantic Web. A straightforward approach to
automating this process would be to build classifiers through machine learning and then use
these classifiers to classify objects from the source taxonomies into categories of the master
taxonomy. However, conventional machine learning algorithms totally ignore the availability …