document: Automatic extraction of clickable structured web contents for name entity queries 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 .
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