Strategies for optimizing querying third party resources in Semantic Web applications

One key property of the Semantic Web is its support for interoperability. Combining knowledge Sources from different authors and locations yields refined and better results. Current Semantic Web applications only use a limited amount of particularly useful and Popular information providers like Swoogle, geonames, etc. for their queries. As more and more applications facilitating Semantic Web technologies emerge, the load caused by these applications is expected to grow, requiring more efficient ways for querying external resources. This research suggests an approach for query optimization based on ideas originally proposed by McQueen for optimal stopping in business economics. Applications querying external resources are modeled as decision makers looking for optimal action/answer sets, facing search costs for acquiring information, test costs for checking the acquired information, and receiving a reward depending on the usefulness of the proposed solution. Applying these concepts to the information system domain yields strategies for optimizing queries to external services. Ail extensive evaluation compares these strategies to a conventional coverage based approach, based on real world response times taken from three different Popular Web services.