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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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