Advanced metasearch engine technology /

Among the search tools currently on the Web, search engines are the most well known thanks to the popularity of major search engines such as Google and Yahoo! While extremely successful, these major search engines do have serious limitations. This book introduces large-scale metasearch engine techno...

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Bibliographic Details
Author / Creator:Meng, Weiyi.
Imprint:San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2011.
Description:1 electronic text (x, 117 p.) : ill., digital file.
Language:English
Series:Synthesis lectures on data management, 2153-5426 ; # 11
Synthesis digital library of engineering and computer science.
Synthesis lectures on data management, # 11.
Subject:Web search engines -- Mathematical models.
Federated searching -- Mathematical models.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/10510963
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Other authors / contributors:Yu, Clement T.
ISBN:9781608451937 (electronic bk.)
9781608451920 (pbk.)
Notes:Part of: Synthesis digital library of engineering and computer science.
Series from website.
Includes bibliographical references (p. 107-115).
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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Also available in print.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Summary:Among the search tools currently on the Web, search engines are the most well known thanks to the popularity of major search engines such as Google and Yahoo! While extremely successful, these major search engines do have serious limitations. This book introduces large-scale metasearch engine technology, which has the potential to overcome the limitations of the major search engines. Essentially, a metasearch engine is a search system that supports unified access to multiple existing search engines by passing the queries it receives to its component search engines and aggregating the returned results into a single ranked list. A large-scale metasearch engine has thousands or more component search engines. While metasearch engines were initially motivated by their ability to combine the search coverage of multiple search engines, there are also other benefits such as the potential to obtain better and fresher results and to reach the DeepWeb. The following major components of large-scale metasearch engines will be discussed in detail in this book: search engine selection, search engine incorporation,and result merging. Highly scalable and automated solutions for these components are emphasized. The authors make a strong case for the viability of the large-scale metasearch engine technology as a competitive technology for Web search.
Standard no.:10.2200/S00307ED1V01Y201011DTM011
Table of Contents:
  • 1. Introduction
  • Finding information on the web
  • Browsing
  • Searching
  • A brief overview of text retrieval
  • System architecture
  • Document representation
  • Document-query matching
  • Query evaluation
  • Retrieval effectiveness measures
  • A brief overview of search engine technology
  • Special characteristics of the web
  • Web crawler
  • Utilizing tag information
  • Utilizing link information
  • Result organization
  • Book overview
  • 2. Metasearch engine architecture
  • System architecture
  • Why metasearch engine technology
  • Challenging environment
  • Heterogeneities and their impact
  • Standardization efforts
  • 3. Search engine selection
  • Rough representative approaches
  • Learning-based approaches
  • Sample document-based approaches
  • Statistical representative approaches
  • D-WISE
  • CORI Net
  • gGLOSS
  • Number of potentially useful documents
  • Similarity of the most similar document
  • Search engine representative generation
  • 4. Search engine incorporation
  • Search engine connection
  • HTML form tag for search engines
  • Automatic search engine connection
  • Search result extraction
  • Semiautomatic wrapper generation
  • Automatic wrapper generation
  • 5. Result merging
  • Merging based on full document content
  • Merging based on search result records
  • Merging based on local ranks of results
  • Round-robin based methods
  • Similarity conversion based methods
  • Voting based methods
  • Machine learning based method
  • 6. Summary and future research
  • Bibliography
  • Authors' biographies.