Quotation Weichselbraun, Albert and Wohlgenannt, Gerhard and Scharl, Arno. Forthcoming. Applying Optimal Stopping Theory to Improve the Performance of Ontology Refinement Methods. In Proceedings of the 44th Hawaii International Conference on System Sciences (HICSS-44), Hrsg. IEEE Computer Society, 1-10. Maui, Hawaii, USA: IEEE Computer Society Press.


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Abstract

Recent research shows the potential of utilizing data collected through Web 2.0 applications to capture domain evolution. Relying on external data sources, however, often introduces delays due to the time spent retrieving data from these sources. The method introduced in this paper streamlines the data acquisition process by applying optimal stopping theory. An extensive evaluation demonstrates how such an optimization improves the processing speed of an ontology refinement component which uses Delicious to refine ontologies constructed from unstructured textual data while having no significant impact on the quality of the refinement process. Domain experts compare the results retrieved from optimal stopping with data obtained from standardized techniques to assess the effect of optimal stopping on data quality and the created domain ontology.

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Publication's profile

Affiliation WU
Type of publication Contribution to conference proceedings
Language English
Title Applying Optimal Stopping Theory to Improve the Performance of Ontology Refinement Methods
Title of whole publication Proceedings of the 44th Hawaii International Conference on System Sciences (HICSS-44)
Editor IEEE Computer Society
Page from 1
Page to 10
Location Maui, Hawaii, USA
Publisher IEEE Computer Society Press
Year 2011
ISBN 978-0-7695-4282-9

Associations

People
Weichselbraun, Albert (Former researcher)
Wohlgenannt, Gerhard (Former researcher)
Scharl, Arno (Former researcher)
Organization
Institute for Data, Process and Knowledge Management (AE Polleres) (Details)
Research Institute for Computational Methods FI (Details)
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