Automatic Taxonomy Extraction from Query Logs with no Additional Sources of Information

TL;DR

This paper introduces a language-independent method to extract term taxonomies from search engine query logs by combining lexical heuristics with supervised classification, enhancing search engine performance without extra data sources.

Contribution

It presents a novel approach that mines query logs to directly extract hyponymy relations, unlike previous methods focused on related queries or full text documents.

Findings

Successfully extracts hyponymy relations from query logs

Operates in a language-independent manner

Improves search engine effectiveness

Abstract

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.