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.

Extracting Opinion Targets from Environmental Web Coverage and Social Media Streams

“…Wong et al [9] use the mutual information between constituents of terms to guide term simplifications for ontology learning. Weichselbraun et al [10] query term statistics from Delicious and Yahoo! for refining domain ontologies.…”

Knowledge capture from multiple online sources with the extensible web retrieval toolkit (eWRT)

“…; and  components based on the TagInfoServices interface that are limited to providing data on the distribution of query terms in Web sources. These data is then used by high-level modules that compute association metrics such as Web distance for ontology learning, ontology enrichment and refinement [14].…”