Adopting relevance feature to learn personalized ontologies


Autoria(s): Shen, Yan; Li, Yuefeng; Xu, Yue
Contribuinte(s)

Thielscher, Michael

Zhang, Dongmo

Data(s)

2012

Resumo

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.

Identificador

http://eprints.qut.edu.au/58120/

Publicador

Springer

Relação

DOI:10.1007/978-3-642-35101-3_39

Shen, Yan, Li, Yuefeng, & Xu, Yue (2012) Adopting relevance feature to learn personalized ontologies. Lecture Notes in Computer Science, 7691, pp. 457-468.

http://purl.org/au-research/grants/ARC/DP0988007

Direitos

Copyright 2012 Springer-Verlag

Fonte

School of Electrical Engineering & Computer Science; Science & Engineering Faculty

Palavras-Chave #Relevance feature #Specificity term #Ontology #Local instance #Global Knowledge base #Concept matching
Tipo

Journal Article