  Search engine logs store detailed information on Web users interactions.
Thus, as more and more people use search engines on a daily basis, important
trails of users common knowledge are being recorded in those files. Previous
research has shown that it is possible to extract concept taxonomies from full
text documents, while other scholars have proposed methods to obtain similar
queries from query logs. We propose a mixture of both lines of research, that
is, mining query logs not to find related queries nor query hierarchies, but
actual term taxonomies that could be used to improve search engine
effectiveness and efficiency. As a result, in this study we have developed a
method that combines lexical heuristics with a supervised classification model
to successfully extract hyponymy relations from specialization search patterns
revealed from log missions, with no additional sources of information, and in a
language independent way.
