Using query extension and user feedback to improve pubmed search

Viktoria Dorfer, Sophie Anna Blank, Stephan Winkler, Thomas Kern, Gerald Petz, Patrizia Faschang

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

PubMed is a search engine that is widely used to search for medical publications. A common challenge in information retrieval, and thus also when using PubMed, is that broad search queries often result in lists of thousands of papers that are presented to the user, too narrow ones often yield small or even empty lists. To address this problem we here present a new PubMed search interface with query extension using keyword clusters generated with evolutionary algorithms to obtain more specific search results. Users can choose to add various words to their query and then rate search results; this scoring is stored in a database to enable learning from user feedback to improve keyword cluster optimization as well as query extensions. We show how users can extend PubMed queries using previously generated keyword clusters, rate query results, and use these ratings for optimizing parameters of the keyword clustering algorithms.

Original languageEnglish
Title of host publication23rd European Modeling and Simulation Symposium, EMSS 2011
Pages433-438
Number of pages6
Publication statusPublished - 2011
Event23rd European Modeling and Simulation Symposium, EMSS 2011 - Rome, Italy
Duration: 12 Sep 201114 Sep 2011

Publication series

Name23rd European Modeling and Simulation Symposium, EMSS 2011

Conference

Conference23rd European Modeling and Simulation Symposium, EMSS 2011
CountryItaly
CityRome
Period12.09.201114.09.2011

Keywords

  • Bioinformatical Information Retrieval
  • Keyword Clustering
  • PubMed
  • Query Extension

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