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Published Articles >> Table of Contents >> Abstract
2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06)
pp. 769-772
Rapid Synthesis of Domain-Specific Web Search Engines Based on Semi-Automatic Training-Example Generation
Hidetomo Nabeshima, University of Yamanashi, Japan
Reiko Miyagawa, University of Yamanashi, Japan
Yuki Suzuki, University of Yamanashi, Japan
Koji Iwanuma, University of Yamanashi, Japan
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WI.2006.143
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| Abstract |
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In this paper, we propose two kinds of semi-automatic
training-example generation algorithms for rapidly synthesizing
a domain-specific Web search engine. We use the
keyword spice model, as a basic framework, which is an
excellent approach for building a domain-specific search
engine with high precision and high recall. The keyword
spice model, however, requires a huge amount of training
examples which should be classified by hand. For overcoming
this problem, we propose two kinds of refinement
algorithms based on semi-automatic training-example generation:
(i) the sample decision tree based approach, and
(ii) the similarity based approach. These approaches make
it possible to build a highly accurate domain-specific search
engine with a little time and effort. The experimental results
show that our approaches are very effective and practical
for the personalization of a general-purpose search engine.
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 Additional Information
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Citation:
Hidetomo Nabeshima, Reiko Miyagawa, Yuki Suzuki, Koji Iwanuma,
"Rapid Synthesis of Domain-Specific Web Search Engines Based on Semi-Automatic Training-Example Generation,"
wi,
pp. 769-772,
2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06),
2006
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