Using machine learning to support continuous ontology development
EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
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With the increasing popularity of Web 2.0, a general rise of user generated content there are more and more tagging systems that allow users to annotate digital resources with tags (keywords) and share their annotations with other users. Tagging is frequently seen in contrast to traditional knowledge organization systems or as something completely new. However, owing to tags are created manually by users in terms of their interest and ambiguity inherent in normal human languages, there is a problem that different tags may be relate to the same items or identical tags can represent the different meaning. So, this can cause the confusion of using the tags. Therefore this paper gives a point of view that tags should better be mapped to an ontology in order to improving search and manage of relevant items. Moreover, a simple ontology of learning resources is created in this paper that includes different classes, properties and relationships. Meanwhile, an example of paper tagged by several words is shown and be mapped to this ontology, and support the function of reasoning via OWL.