Exploiting Structure, Annotation, and Ontological Knowledge for Automatic Classification of XML Data

This paper investigates how to automatically classify non-schematic XML data into a user-defined topic directory. The main focus is on constructing appropriate feature spaces on which a classifier operates. In addition to the usual text-based term frequency vectors, we study XML twigs and tag paths as extended features that can be combined with text term occurrences in XML elements. Moreover, we show how to leverage ontological background information, more specifically, the WordNet thesaurus, for the construction of more expressive feature spaces. For efficiency our implementation computes features incrementally and caches ontology entries. Our experiments demonstrate the improved accuracy of automatic classification based on the enhanced feature spaces.