Jäschke, R.; Hotho, A.; Schmitz, C.; Ganter, B. & Stumme, G.: Discovering Shared Conceptualizations in Folksonomies. In: Web Semantics: Science, Services and Agents on the World Wide Web 6 (2008), Nr. 1, S. 38-53
[Volltext]
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.
@article{jaeschke2008discovering,
author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd},
title = {Discovering Shared Conceptualizations in Folksonomies},
booktitle = {Semantic Web and Web 2.0},
journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
year = {2008},
volume = {6},
number = {1},
pages = {38--53},
url = {http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b},
keywords = {discovering, 2008, tagging, concept, formal, taggingsurvey, analysis, folksonomy, fca, myown, bibsonomy},
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.}
}