By Topic

Using Medical Test Collection Relevance Judgements to Identify Ontological Relationships Useful for Query Expansion

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wollersheim, D. ; La Trobe University,Melbourne, Australia ; Rahayu, W.J.

In this paper we describe an innovative query expansion evaluation framework (QEEF) which discovers the ontological and algorithmic characteristics that drive successful query expansion. The method consists of identifying UMLS (Unified Medical Language System) concepts in the Ohsumed corpus queries and documents, and then applying variety of query expansion algorithms to the query concepts, both individually and at the query level. We analyse the results, discovering the characteristics of high relevance medical query expansions. We directly evaluate query expansion success, and this enables discovery of the relationship between the UMLS facets and this success. The paper details the methods used, and then discusses the influence of both UMLS attributes, and choice of query expansion algorithm, on query expansion success.

Published in:

Data Engineering Workshops, 2005. 21st International Conference on

Date of Conference:

05-08 April 2005