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A Hybrid Concept Similarity Measure Model for Ontology Environment
Hai Dong, Farookh Khadeer Hussain, and Elizabeth Chang
Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, GPO Box U1987 Perth, Western Australia 6845, Australiahai.dong@cbs.curtin.edu.au
farookh.hussain@cbs.curtin.edu.au
elizabeth.chang@cbs.curtin.edu.au
Abstract. In this paper, we present a hybrid concept similarity measure model for the ontology environment. Whilst to date many similar technologies have been developed for semantic networks, few of them can be directly applied to the semantic-rich ontology environment. Before the measure model is adopted, an ontology is required to be converted into a lightweight ontology space, and within it all the ontology concepts need to be transformed into the pseudo-concepts. By means of this model, ontology concept similarities are measured respectively based on the content of pseudo-concepts and the structure of the lightweight ontology space. Afterwards, the two aspects of concept similarity are leveraged as the eventual product. In addition, an experiment is conducted to evaluate the measure model based on a small ontology. Conclusions are drawn and future works are planned in the final section.
Keywords: concept similarity measure, latent semantic indexing, ontology, semantic similarity models
LNCS 5872, p. 848 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2009