Web Search Model for Dynamic and Fuzzy Directory Search

In web search engines, index search used to be evaluated at a high recall rate. However, the pitfall is that users have to work hard to select relevant documents from too many search results. Skillful surfers tend to prefer the index searching method, while on the other hand, those who are not accustomed to web searching generally use the directory search method. Therefore, the directory searching method is needed as a complementary way of web searching. However, in the case that target documents for searching are obscurely categorized or users have no exact knowledge about the appropriate categories of target documents, occasionally directory search will fail to come up with satisfactory results. That is, the directory search method has a high precision and low recall rate. With this motive, we propose a novel model in which a category hierarchy is dynamically constructed. To do this, a category is regarded as a fuzzy set which includes keywords. Similarly extensible subcategories of a category can be found using fuzzy relational products. The merit of this method is to enhance the recall rate of directory search by reconstructing subcategories on the basis of similarity.