@article{keyhere,
	title = {Learning Disjointness},
	author = {Johanna Volker and Denny Vrandečić and York Sure and Andreas Hotho},
	journal = {The Semantic Web: Research and Applications},
	pages = {175--189},
	url = {http://dx.doi.org/10.1007/978-3-540-72667-8_14},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/227942b25dc07980380a8af65024ada99/thau},
	description = {SpringerLink - Book Chapter},
	abstract = {An increasing number of applications benefits from light-weight ontologies, or to put it differently “a little semantics goes a long way”. However, our experience indicates that more expressiveness can offer significant advantages. Introducing disjointness axioms,
for instance, greatly facilitates consistency checking and the automatic evaluation of ontologies. In an extensive user studywe discovered that proper modeling of disjointness is a difficult and very time-consuming task. We therefore developed anapproach to automatically enrich learned or manually engineered ontologies with disjointness axioms. This approach relieson several methods for obtaining syntactic and semantic evidence from different sources which we believe to provide a solidbase for learning disjointness. After thoroughly evaluating the implementation of our approach we think that in future ontologyengineering environments the automatic discovery of disjointness axioms may help to increase the richness, quality and usefulnessof any given ontology.},
	keywords = {kr learning ontology }
}