TY  - CONF
AU  - Schmitz, Christoph
AU  - Hotho, Andreas
AU  - Jäschke, Robert
AU  - Stumme, Gerd
A2  - Batagelj, V.
A2  - Bock, H.-H.
A2  - Ferligoj, A.
A2  - �iberna, A.
T1  - Mining Association Rules in Folksonomies
T2  - Data Science and Classification. Proceedings of the 10th IFCS Conf.
PB  - Springer
CY  - Heidelberg
PY  - 2006/07
M2  - 
VL  - 
IS  - 
SP  - 261
EP  - 270
UR  - 
M3  - 
KW  - analysis
KW  - closely_related
KW  - diploma_thesis
KW  - folksonomy
KW  - nepomuk
KW  - network
KW  - semantic
KW  - ol_web2.0
KW  - methods_concepts
KW  - methods_concepthierarchy
L1  - schmitz06-mining.pdf
SN  - 
N1  - 
N1  - 
AB  - 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.
ER  -

TY  - CONF
AU  - Schmitz, Christoph
AU  - Hotho, Andreas
AU  - J�schke, Robert
AU  - Stumme, Gerd
A2  - Batagelj, V.
A2  - Bock, H.-H.
A2  - Ferligoj, A.
A2  - �iberna, A.
T1  - Mining Association Rules in Folksonomies
T2  - Data Science and Classification. Proceedings of the 10th IFCS Conf.
PB  - Springer
CY  - Heidelberg
PY  - 2006/07
M2  - 
VL  - 
IS  - 
SP  - 261
EP  - 270
UR  - 
M3  - 
KW  - ol_web2.0
KW  - nepomuk
KW  - closely_related
KW  - semantic
KW  - folksonomy
KW  - diploma_thesis
KW  - analysis
KW  - network
L1  - schmitz06-mining.pdf
SN  - 
N1  - 
N1  - 
AB  - 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.
ER  -