Building efficient and effective metasearch engines

  • Authors:

  • Weiyi Meng;Clement Yu;King-Lup Liu

  • Affiliations:

  • State University of New York at Binghamton, Binghamton, NY;University of Illinois at Chicago, Chicago, IL;DePaul University, Chicago, IL

  • Venue:
  • ACM Computing Surveys (CSUR)
  • Year:
  • 2002

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Abstract

Frequently a user's information needs are stored in the databases of multiple search engines. It is inconvenient and inefficient for an ordinary user to invoke multiple search engines and identify useful documents from the returned results. To support unified access to multiple search engines, a metasearch engine can be constructed. When a metasearch engine receives a query from a user, it invokes the underlying search engines to retrieve useful information for the user. Metasearch engines have other benefits as a search tool such as increasing the search coverage of the Web and improving the scalability of the search. In this article, we survey techniques that have been proposed to tackle several underlying challenges for building a good metasearch engine. Among the main challenges, the database selection problem is to identify search engines that are likely to return useful documents to a given query. The document selection problem is to determine what documents to retrieve from each identified search engine. The result merging problem is to combine the documents returned from multiple search engines. We will also point out some problems that need to be further researched.