Advanced metasearch engine technology /
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Author / Creator: | Meng, Weiyi. |
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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 |
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.