Predicting Category Additions in a Topic Hierarchy.

This paper discusses the problem of predicting the structural changes in an ontology. It addresses ontologies that contain instances in addition to concepts. The focus is on an ontology where the instances are textual documents, but the approach presented in this document is general enough to also work with other kinds of instances, as long as a similarity measure can be defined over them. We examine the changes in the Open Directory Project ontology of Web pages over a period of several years and analyze the most common types of structural changes that took place during that time. We then present an approach for predicting one of the more common types of structural changes, namely the addition of a new concept that becomes the subconcept of an existing parent concept and adopts a few instances of this existing parent concept. We describe how this task can be formulated as a machine-learning problem and present an experimental evaluation of this approach that shows promising results of the proposed approach. [ABSTRACT FROM AUTHOR]