Adopting relevance feature to learn personalized ontologies
Relevance feature and ontology are two core components to learn personalized ontologies for concept-based retrievals. However, how to associate user native information with common knowledge is an urgent issue. This paper proposes a sound solution by matching relevance feature mined from local instances with concepts existing in a global knowledge base. The matched concepts and their relations are used to learn personalized ontologies. The proposed method is evaluated elaborately by comparing it against three benchmark models. The evaluation demonstrates the matching is successful by achieving remarkable improvements in information filtering measurements.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Journal Article|
|Additional Information:||Paper presented in The 25th Australasian Joint Conference on Advances in Artificial Intelligence, 4-7 December 2012
Sydney Harbour Marriott Hotel
|Keywords:||Relevance feature, Specificity term, Ontology, Local instance, Global Knowledge base, Concept matching|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 Springer-Verlag|
|Deposited On:||13 Mar 2013 03:02|
|Last Modified:||19 Jan 2015 01:48|
Repository Staff Only: item control page