Applying Optimal Stopping Theory to Improve the Performance of Ontology
Refinement Methods
A. Weichselbraun, G. Wohlgenannt, and A. Scharl. Proceedings of the 44th Hawaii International Conference on System
Sciences (HICSS-44), page 1--10. Maui, Hawaii, IEEE Computer Society Press, (January 2011)
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.
%0 Conference Paper
%1 weichselbraun2011
%A Weichselbraun, Albert
%A Wohlgenannt, Gerhard
%A Scharl, Arno
%B Proceedings of the 44th Hawaii International Conference on System
Sciences (HICSS-44)
%C Maui, Hawaii
%D 2011
%I IEEE Computer Society Press
%K imported
%P 1--10
%T Applying Optimal Stopping Theory to Improve the Performance of Ontology
Refinement Methods
%X 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.
%@ 978-0-7695-4282-9
@inproceedings{weichselbraun2011,
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.},
added-at = {2012-04-16T19:17:24.000+0200},
address = {Maui, Hawaii},
author = {Weichselbraun, Albert and Wohlgenannt, Gerhard and Scharl, Arno},
biburl = {https://www.bibsonomy.org/bibtex/24776588c141eb82af0c2df0e47786b80/albert.weichselbraun},
booktitle = {Proceedings of the 44th Hawaii International Conference on System
Sciences (HICSS-44)},
eprint = {http://eprints.weblyzard.com/31/1/weichselbraun2011%2DperformanceOfOntologyRefinement.pdf},
interhash = {ad3fdd33355106a8ee51aad33f982226},
intrahash = {4776588c141eb82af0c2df0e47786b80},
isbn = {978-0-7695-4282-9},
keywords = {imported},
month = {January},
owner = {albert},
pages = {1--10},
publisher = {IEEE Computer Society Press},
timestamp = {2012-04-16T19:17:28.000+0200},
title = {Applying Optimal Stopping Theory to Improve the Performance of Ontology
Refinement Methods},
year = 2011
}