Applying Optimal Stopping Theory to Improve the Performance of Ontology Refinement Methods

A. Weichselbraun; G. Wohlgenannt; Arno Scharl

doi:10.1109/HICSS.2011.72

Applying Optimal Stopping Theory to Improve the Performance of Ontology Refinement Methods

A. Weichselbraun, G. Wohlgenannt, Arno Scharl

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	2011 44th Hawaii International Conference on System Sciences (HICSS)
Place of Publication	Kauai, HI
Publisher	IEEE Computer Society
Pages	1 - 10
ISBN (Print)	978-1-4244-9618-1
DOIs	https://doi.org/10.1109/HICSS.2011.72
Publication status	Published - Jan 2011
Event	44th Hawaii International Conference on System Sciences - Hawaii, Kauai, United States Duration: 4 Jan 2011 → 7 Jan 2011

Conference

Conference	44th Hawaii International Conference on System Sciences
Abbreviated title	HICSS
Country/Territory	United States
City	Kauai
Period	04/01/2011 → 07/01/2011

Access to Document

10.1109/HICSS.2011.72

Cite this

@inproceedings{338cea6ecad042e7952325c9eafd462e,

title = "Applying Optimal Stopping Theory to Improve the Performance of Ontology Refinement Methods",

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.",

author = "A. Weichselbraun and G. Wohlgenannt and Arno Scharl",

year = "2011",

month = jan,

doi = "10.1109/HICSS.2011.72",

language = "English",

isbn = "978-1-4244-9618-1",

pages = "1 -- 10",

booktitle = "2011 44th Hawaii International Conference on System Sciences (HICSS)",

publisher = "IEEE Computer Society",

address = "United States",

note = "44th Hawaii International Conference on System Sciences, HICSS ; Conference date: 04-01-2011 Through 07-01-2011",

}

Weichselbraun, A, Wohlgenannt, G & Scharl, A 2011, Applying Optimal Stopping Theory to Improve the Performance of Ontology Refinement Methods. in 2011 44th Hawaii International Conference on System Sciences (HICSS). IEEE Computer Society, Kauai, HI, pp. 1 - 10, 44th Hawaii International Conference on System Sciences, Kauai, United States, 04/01/2011. https://doi.org/10.1109/HICSS.2011.72

TY - GEN

T1 - Applying Optimal Stopping Theory to Improve the Performance of Ontology Refinement Methods

AU - Weichselbraun, A.

AU - Wohlgenannt, G.

AU - Scharl, Arno

PY - 2011/1

Y1 - 2011/1

N2 - 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.

AB - 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.

U2 - 10.1109/HICSS.2011.72

DO - 10.1109/HICSS.2011.72

M3 - Conference contribution

SN - 978-1-4244-9618-1

SP - 1

EP - 10

BT - 2011 44th Hawaii International Conference on System Sciences (HICSS)

PB - IEEE Computer Society

CY - Kauai, HI

T2 - 44th Hawaii International Conference on System Sciences

Y2 - 4 January 2011 through 7 January 2011

ER -

Applying Optimal Stopping Theory to Improve the Performance of Ontology Refinement Methods

Abstract

Conference

Access to Document

Fingerprint

Cite this