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Discovering Shared Conceptualizations in Folksonomies

Web Semantics, 6(1): 38-53, 2008.
Authors: Robert Jäschke and Andreas Hotho and Christoph Schmitz and Bernhard Ganter and Gerd Stumme
URL: http://dx.doi.org/10.1016/j.websem.2007.11.004
Tags: ai bookmark community ontology paper semantic software tagging v0805 web
Abstract: Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.
| URL | BibTeX  
@article{JaeschkeHothoEtAl08jws,
title = {Discovering Shared Conceptualizations in Folksonomies},
author = {Robert Jäschke and Andreas Hotho and Christoph Schmitz and Bernhard Ganter and Gerd Stumme},
journal = {Web Semantics},
number = {1},
pages = {38-53},
url = {http://dx.doi.org/10.1016/j.websem.2007.11.004},
volume = {6},
year = {2008},
abstract = {Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.},
issn = {1570-8268}, timestamp = {2008.05.03}, file = {ScienceDirect:2008/JaeschkeHothoEtAl08jws.pdf:PDF}, owner = {flint},
keywords = {ai bookmark community ontology paper semantic software tagging v0805 web }
}