PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

PREDICTING THE ADDITION OF NEW CONCEPTS IN A TOPIC HIERARCHY
Janez Brank, Dunja Mladenić and Marko Grobelnik
In: IS 2007, 8-12 October 2007, Ljubljana, Slovenia.

Abstract

Ontologies 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

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:3759
Deposited By:Dunja Mladenić
Deposited On:16 February 2008