Today the major web search engines answer queries by showing ten result snippets, which need to be inspected by users for identifying relevant results. In this paper we investigate how to extract structured information from the web, in order to directly answer queries by showing the contents being searched for. We treat users’ search trails (i.e., post-search browsing behaviors) as implicit labels on the relevance between web contents and user queries. Based on such labels we use information extraction approach to build wrappers and extract structured information. An important observation is that many web sites contain pages for name entities of certain categories (e.g., AOL Music contains a page for each musician), and these pages have the same format. This makes it possible to build wrappers from a small amount of implicit labels, and use them to extract structured information from many web pages for different name entities. We propose STRUCLICK, a fully automated system for extracting structured information for queries containing name entities of certain categories. It can identify important web sites from web search logs, build wrappers from users’ search trails, filter out bad wrappers built from random user clicks, and combine structured information from different web sites for each query. Comparing with existing approaches on information extraction, STRUCLICK can assign semantics to extracted data without any human labeling or supervision. We perform comprehensive experiments, which show STRUCLICK achieves high accuracy and good scalability.