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.