Measuring Data-Driven Ontology Changes using Text Mining

Enkhsaikhan, M., Wong, W., Liu, W. and Reynolds, M.

    Most current ontology management systems concentrate on detecting usage-driven changes and representing changes formally in order to maintain the consistency. In this paper, we present a semi-automatic approach for measuring and visualising data-driven changes through ontology learning. Terms are first generated using text mining techniques using an ontology learning module, and then classified automatically into clusters. The clusters are then manually named, which is the only manual process in this system. Each cluster is considered as a sub-concept of the root concept, and thus one dimension of the feature space describing the root concept. The changes of terms in each cluster contributes to the change of the root concept. Using our system, Web documents are collected at different time periods and fed into the system to generate different versions of the same ontology for each time period. The paper presents several ways of visualising and analysing the changes. Initial experiments on online media data have demonstrated the promising capabilities of our system.
Cite as: Enkhsaikhan, M., Wong, W., Liu, W. and Reynolds, M. (2007). Measuring Data-Driven Ontology Changes using Text Mining. In Proc. Sixth Australasian Data Mining Conference (AusDM 2007), Gold Coast, Australia. CRPIT, 70. Christen, P., Kennedy, P. J., Li, J., Kolyshkina, I. and Williams, G. J., Eds. ACS. 39-46.
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