| Authors: |
Robert J\"{a}schke
and Andreas Hotho
and Christoph Schmitz
and Bernhard Ganter
and Gerd Stumme
|
| URL: |
http://portal.acm.org/citation.cfm?id=1346366.1346701&coll=Portal&dl=GUIDE&CFID=24525070&CFTOKEN=80643313 |
| Description: |
Discovering shared conceptualizations in folksonomies |
| Tags: |
folksonomy
machine_learning
|
| 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. |
@article{hotho_2008,
title = {Discovering shared conceptualizations in folksonomies},
address = {Amsterdam, The Netherlands, The Netherlands},
author = {Robert J\"{a}schke and Andreas Hotho and Christoph Schmitz and Bernhard Ganter and Gerd Stumme},
journal = {Web Semant.},
number = {1},
pages = {38--53},
publisher = {Elsevier Science Publishers B. V.},
url = {http://portal.acm.org/citation.cfm?id=1346366.1346701&coll=Portal&dl=GUIDE&CFID=24525070&CFTOKEN=80643313},
volume = {6},
year = {2008},
description = {Discovering shared conceptualizations in folksonomies},
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}, doi = {http://dx.doi.org/10.1016/j.websem.2007.11.004},
keywords = {folksonomy machine_learning }
}
::