By Topic

An Information Retrieval Model Based on Automatically Learnt Concept Hierarchies

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

3 Author(s)
Goyal, P. ; Intell. Syst. Res. Centre, Univ. of Ulster, Coleraine, UK ; Behera, L. ; McGinnity, T.M.

The paper investigates the application of fuzzy logic based concept summarization and formal concept analysis in automatically building concept hierarchies from a text corpora. The context of a term has been modeled using its syntactic relations with the most frequent verbs, which act as attributes. This context information has been used to produce a concept lattice, which retains the concept hierarchies as well as the membership weights of the objects. The concepts within each hierarchy have been summarized using a fuzzy logic based soft least upper bound approach. An information retrieval model is proposed, which uses fuzzy formal concepts to get the relevance degree between the document and the query. Results for ontology evaluation are shown on two domain ontologies.

Published in:

Semantic Computing, 2009. ICSC '09. IEEE International Conference on

Date of Conference:

14-16 Sept. 2009