Artificial Intelligence Lab, Department of MIS, Univeristy of Arizona / It has become increasingly difficult to locate relevant
information on the Web, even with the help of Web
search engines. Two approaches to addressing the low
precision and poor presentation of search results of
current search tools are studied: meta-search and document
categorization. Meta-search engines improve
precision by selecting and integrating search results
fromgeneric or domain-specific Web search engines or
other resources. Document categorization promises
better organization and presentation of retrieved results.
This article introduces MetaSpider, a meta-search engine
that has real-time indexing and categorizing functions.
We report in this paper the major components of
MetaSpider and discuss related technical approaches.
Initial results of a user evaluation study comparing Meta-
Spider, NorthernLight, and MetaCrawler in terms of
clustering performance and of time and effort expended
show that MetaSpider performed best in precision rate,
but disclose no statistically significant differences in
recall rate and time requirements. Our experimental
study also reveals that MetaSpider exhibited a higher
level of automation than the other two systems and
facilitated efficient searching by providing the user with
an organized, comprehensive view of the retrieved documents.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105331 |
| Date | January 2001 |
| Creators | Chen, Hsinchun, Fan, Haiyan, Chau, Michael, Zeng, Daniel |
| Publisher | Wiley Periodicals, Inc |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
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