TY  - CONF
AU  - Benz, Dominik
AU  - Hotho, Andreas
AU  - Stützer, Stefan
AU  - Stumme, Gerd
A2  - 
T1  - Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge
T2  - Proceedings of the 2nd Web Science Conference (WebSci10)
PB  - 
C1  - Raleigh, NC, USA
PY  - 2010/
CY  -  
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://www.kde.cs.uni-kassel.de/pub/pdf/benz2010semantics.pdf
DO  - 
KW  - 2010
KW  - ibm-kde-tagging
KW  - itegpub
KW  - myown
KW  - ol_web2.0
KW  - semantics
KW  - web_science
KW  - websci
KW  - websci10
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 
ER  -

TY  - CONF
AU  - Körner, Christian
AU  - Benz, Dominik
AU  - Strohmaier, Markus
AU  - Hotho, Andreas
AU  - Stumme, Gerd
A2  - 
T1  - Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity
T2  - Proceedings of the 19th International World Wide Web Conference (WWW 2010)
PB  - ACM
C1  - Raleigh, NC, USA
PY  - 2010/04
CY  -  
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://www.kde.cs.uni-kassel.de/pub/pdf/koerner2010stop.pdf
DO  - 
KW  - 2010
KW  - collaborative_verbosity
KW  - emergentsemantics_factors
KW  - ibm-kde-tagging
KW  - itegpub
KW  - myown
KW  - ol_web2.0
KW  - www
KW  - www2010
L1  - 
SN  - 
N1  - 
N1  - 
AB  - Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise??, and (iii) in learning ontologies.
ER  -

TY  - CONF
AU  - Benz, Dominik
AU  - Hotho, Andreas
A2  - Hinneburg, Alexander
T1  - Position Paper: Ontology Learning from Folksonomies
T2  - Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)
PB  - Martin-Luther-Universität Halle-Wittenberg
C1  - 
PY  - 2007/10
CY  -  
VL  - 
IS  - 
SP  - 109
EP  - 112
UR  - http://www.kde.cs.uni-kassel.de/pub/pdf/benz2007position.pdf
DO  - 
KW  - 2007
KW  - diploma_thesis
KW  - ibm-kde-tagging
KW  - itegpub
KW  - methods_concepthierarchy
KW  - myown
KW  - ol_web2.0
KW  - ontology_learning
KW  - rel1
KW  - rel2
KW  - tagorapub
L1  - 
SN  - 978-3-86010-907-6
N1  - 
N1  - 
AB  - The emergence of collaborative tagging systems with their underlying flat and uncontrolled resource organization paradigm has led to a large number of research activities focussing on a formal description and analysis of the resulting “folksonomies??. An interesting outcome is that the characteristic qualities of these systems seem to be inverse to more traditional knowledge structuring approaches like taxonomies or ontologies: The latter provide rich and precise semantics, but suffer - amongst others - from a knowledge acquisition bottleneck. An important step towards exploiting the possible synergies by bridging the gap between both paradigms is the automatic extraction of relations between tags in a folksonomy. This position paper presents preliminary results of ongoing work to induce hierarchical relationships among tags by analyzing the aggregated data of collaborative tagging systems as a basis for an ontology learning procedure.
ER  -