Datum
2006Metadata
Zur Langanzeige
Preprint
Mining association rules in folksonomies
Zusammenfassung
Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.
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@article{urn:nbn:de:hebis:34-2009040826905,
author={Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
title={Mining association rules in folksonomies},
year={2006}
}
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<resource xsi:schemaLocation="http://datacite.org/schema/kernel-2.2 http://schema.datacite.org/meta/kernel-2.2/metadata.xsd"> 2009-04-08T13:35:29Z 2009-04-08T13:35:29Z 2006 urn:nbn:de:hebis:34-2009040826905 http://hdl.handle.net/123456789/2009040826905 568596 bytes application/pdf eng Urheberrechtlich geschützt https://rightsstatements.org/page/InC/1.0/ 004 Mining association rules in folksonomies Preprint Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system. open access Schmitz, Christoph Hotho, Andreas Jäschke, Robert Stumme, Gerd Auch erschienen in: Batagelj, Vladimir u.a. (Hrsg.): Data science and classification. (Studies in classification, data analysis, and knowledge organization). Berlin u.a. : Springer, 2006. S. 261-270. ISBN 3-540-34415-2 - 978-3-540-34415-5(The original publication is available at www.springerlink.com) Ontologie <Wissensverarbeitung> Wissensextraktion </resource>
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