Applying Optimal Stopping for Optimizing Queries to External Semantic Web Resources

Abstract

The rapid increase in the amount of available information from various online sources poses
new challenges for programs that endeavor to process these sources automatically and identify
the most relevant material for a given application.

This paper introduces an approach for optimizing queries to Semantic Web resources based on ideas originally proposed by MacQueen for optimal stopping in business economics.
Modeling applications as decision makers looking for optimal action/answer sets, facing search costs for acquiring information, test costs for checking these information, and receiving a reward depending on the usefulness of the proposed solution, yields strategies for optimizing queries to external services.
An extensive evaluation compares these strategies to a conventional coverage based approach, based on real world response times taken from popular Web services.