CUCS-004-00 Automatic Classification of Text Databases Through Query Probing

Many text databases on the web are \hidden" behind search interfaces, and their documents are only accessible through querying. Search engines typically ignore the contents of such search-only databases. Recently, Yahoo-like directories have started to manually organize these databases into categories that users can browse to flnd these valuable resources. We propose a novel strategy to automate the classiflcation of search-only text databases. Our technique starts by training a rule-based document classifler, and then uses the classifler's rules to generate probing queries. The queries are sent to the text databases, which are then classifled based on the number of matches that they produce for each query. We report some initial exploratory experiments that show that our approach is promising to automatically characterize the contents of text databases accessible on the web.