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Rapid Synthesis of Domain-Specific Web Search Engines Based on Semi-Automatic Training-Example Generation

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4 Author(s)
Hidetomo Nabeshima ; University of Yamanashi, Japan ; Reiko Miyagawa ; Yuki Suzuki ; Koji Iwanuma

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

Published in:

2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06)

Date of Conference:

18-22 Dec. 2006