Architecture of a metasearch engine that supports user information needs

When a query is submitted to a metasearch engine, decisions are made with respect to the underlying search engines to be used, what modifications will be made to the query, and how to score the results. These decisions are typically made by considering only the user's keyword query, neglecting the larger information need. Users with specific needs, such as “research papers” or “homepages,” are not able to express these needs in a way that affects the decisions made by the metasearch engine. In this paper, we describe a metasearch engine architecture that considers the user's information need for each decision. Users with different needs, but the same keyword query, may search different sub-search engines, have different modifications made to their query, and have results ordered differently. Our architecture combines several powerful approaches together in a single general purpose metasearch engine.

[1]  Peter Haddawy,et al.  The Decision-Theoretic Video Advisor* , 1998 .

[2]  Oren Etzioni,et al.  The MetaCrawler architecture for resource aggregation on the Web , 1997 .

[3]  H. Raiffa,et al.  Decisions with Multiple Objectives , 1993 .

[4]  Adele E. Howe,et al.  SAVVYSEARCH: A Metasearch Engine That Learns Which Search Engines to Query , 1997, AI Mag..

[5]  Craig A. Knoblock,et al.  Lycos : Design choices in an Internet search service , 1997 .

[6]  Shih-Fu Chang,et al.  Using Relevance Feedback in Content-Based Image Metasearch , 1998, IEEE Internet Comput..

[7]  Michael B. Eisenberg,et al.  A re-examination of relevance: toward a dynamic, situational definition , 1990, Inf. Process. Manag..

[8]  Guijun Wang,et al.  ProFusion*: Intelligent Fusion from Multiple, Distributed Search Engines , 1996, J. Univers. Comput. Sci..

[9]  C. Lee Giles,et al.  Accessibility of information on the web , 1999, Nature.

[10]  C. Lee Giles,et al.  Inquirus, the NECI Meta Search Engine , 1998, Comput. Networks.

[11]  Michael D. Gordon,et al.  Improving Web Search Using Utility Theory , 1998, Workshop on Web Information and Data Management.

[12]  Carol L. Barry.  The Identification of User Criteria of Relevance and Document Characteristics: Beyond the Topical Ap , 1993 .

[13]  C. Lee Giles,et al.  Context and Page Analysis for Improved Web Search , 1998, IEEE Internet Comput..

[14]  William P. Birmingham,et al.  Using decision theory to order documents , 1998, DL '98.