SAVVYSEARCH: A Metasearch Engine That Learns Which Search Engines to Query

  • Adele E. Howe
  • Daniel Dreilinger

Abstract

Search engines are among the most successful applications on the web today. So many search engines have been created that it is difficult for users to know where they are, how to use them, and what topics they best address. Metasearch engines reduce the user burden by dispatching queries to multiple search engines in parallel. The SAVVYSEARCH metasearch engine is designed to efficiently query other search engines by carefully selecting those search engines likely to return useful results and responding to fluctuating load demands on the web. SAVVYSEARCH learns to identify which search engines are most appropriate for particular queries, reasons about resource demands, and represents an iterative parallel search strategy as a simple plan.
Published
1997-06-15
How to Cite
Howe, A. E., & Dreilinger, D. (1997). SAVVYSEARCH: A Metasearch Engine That Learns Which Search Engines to Query. AI Magazine, 18(2), 19. https://doi.org/10.1609/aimag.v18i2.1290
Section
Articles