Authors:
Miguel Fernández-Fernández
1
and
Daniel Gayo-Avello
2
Affiliations:
1
MVConsultoría, Spain
;
2
University of Oviedo, Spain
Keyword(s):
Web search, Query log, Hyponymy relations, Query reformulation, Automatic taxonomy extraction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Structured Data Analysis and Statistical Methods
;
Symbolic Systems
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. In this first approach we have researched the feasibility of finding hyponymy relations between terms or noun-phrases by exploiting specialization search patterns in topical sessions, obtaining encouraging preliminary results.