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

Albert Weichselbraun, Gerhard Wohlgenannt, Arno Scharl
2011 2011 44th Hawaii International Conference on System Sciences  
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
more » ... 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.
doi:10.1109/hicss.2011.72 dblp:conf/hicss/WeichselbraunWS11 fatcat:ocqyifc3xvhzjfat3l7hsc4dky