Identifying terms by their family and friends
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Understanding ontology evolution: A change detection approach
Web Semantics: Science, Services and Agents on the World Wide Web
Featureless similarities for terms clustering using tree-traversing ants
PCAR '06 Proceedings of the 2006 international symposium on Practical cognitive agents and robots
Tree-Traversing Ant Algorithm for term clustering based on featureless similarities
Data Mining and Knowledge Discovery
Determining termhood for learning domain ontologies in a probabilistic framework
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
SOFSEM'06 Proceedings of the 32nd conference on Current Trends in Theory and Practice of Computer Science
A framework for ontology evolution in collaborative environments
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Building a dynamic classifier for large text data collections
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
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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.