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PREDICTING THE ADDITION OF NEW CONCEPTS
IN A TOPIC HIERARCHY
AbstractOntologies often change through time, a process largely done manually by human editors. We discuss the task of automatically predicting when structural changes will occur in a given ontology. We first analyze the frequency of different types of structural changes in a large real-world ontology and then focus on the problem of predicting one specific type of structural change, namely the addition of a new category as a subcategory of an existing category, from which some of the existing instances are transferred into the new category. We show how the prediction of this type of structural change can be seen as a machine learning problem; the main challenge is to define a useful set of features. Experimental evaluation on a subset of the Open Directory Project hierarchy is provided
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