Automatic Taxonomy Extraction from Query Logs with no Additional Sources of Information
This work addresses the need for improved search engine effectiveness and efficiency by automatically deriving taxonomies from user interactions, though it builds on existing research in query log mining and taxonomy extraction.
The authors tackled the problem of extracting term taxonomies directly from search engine query logs without using external data sources, achieving a method that successfully identifies hyponymy relations in a language-independent manner.
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.