TY  - CONF
AU  - Plangprasopchok, Anon
AU  - Lerman, Kristina
A2  - Quemada, Juan
A2  - León, Gonzalo
A2  - Maarek, Yoëlle S.
A2  - Nejdl, Wolfgang
T1  - Constructing folksonomies from user-specified relations on flickr.
T2  - WWW
PB  - ACM
C1  - 
PY  - 2009/
CY  -  
VL  - 
IS  - 
SP  - 781
EP  - 790
UR  - 
DO  - 
KW  - folksonomies
KW  - ol_web2.0
KW  - ontology_learning
KW  - methods_concepthierarchy
L1  - 
SN  - 978-1-60558-487-4
N1  - dblp
N1  - 
AB  - Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use userspecified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies.
ER  -

TY  - JOUR
AU  - Garcia-Silva, Andres
AU  - Corcho, Oscar
AU  - Alani, Harith
AU  - Gomez-Perez, Asuncion
T1  - Review of the state of the art: Discovering and Associating Semantics to Tags in Folksonomies
JO  - Knowledge Engineering Review
PY  - 2011/12
VL  - 26
IS  - 4
SP  - 
EP  - 
UR  - 
DO  - 
KW  - folksonomies
KW  - ol_web2.0
KW  - ontology_learning
KW  - overview
L1  - 
SN  - 
N1  - 
N1  - 
AB  - This paper describes and compares the most relevant approaches for associating tags with semantics in order to make explicit the meaning of those tags. We identify a common set of steps that are usually considered across all these approaches and frame our descriptions according to them, providing a unified view of how each approach tackles the different problems that appear during the semantic association process. Furthermore, we provide some recommendations on (a) how and when to use each of the approaches according to the characteristics of the data source, and (b) how to improve results by leveraging the strengths of the different approaches.
ER  -

TY  - THES
AU  - Keller, Christine
T1  - Theoretical and Practical Perspectives on Ontology Learning from Folksonomies
PY  - 2010/
PB  - Universität Stuttgart
SP  - 
EP  - 
UR  - 
DO  - 
KW  - ontology_learning
KW  - folksonomies
L1  - 
N1  - 
N1  - 
AB  - 
ER  -

TY  - JOUR
AU  - Strohmaier, Markus
AU  - Helic, Denis
AU  - Benz, Dominik
AU  - Körner, Christian
AU  - Kern, Roman
T1  - Evaluation of Folksonomy Induction Algorithms
JO  - Transactions on Intelligent Systems and Technology
PY  - 2012/
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://tist.acm.org/index.html
DO  - 
KW  - 2012
KW  - evaluation
KW  - folksonomies
KW  - myown
KW  - ontology_learning
L1  - 
SN  - 
N1  - to appear
N1  - 
AB  - 
ER  -

TY  - JOUR
AU  - Strohmaier, Markus
AU  - Helic, Denis
AU  - Benz, Dominik
AU  - Körner, Christian
AU  - Kern, Roman
T1  - Evaluation of Folksonomy Induction Algorithms
JO  - Transactions on Intelligent Systems and Technology
PY  - 2012/
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://tist.acm.org/index.html
DO  - 
KW  - evaluation
KW  - folksonomies
KW  - itegpub
KW  - ontology_learning
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 
ER  -