Abstract
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.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
AGRAWAL, R., IMIELINSKI, T. and SWAMI, A. (1993): Mining association rules between sets of items in large databases. In: Proc. of SIGMOD 1993, pp. 207–216. ACM Press.
CONNOTEA (2005): Connotea Mailing List. https://lists.sourceforge.net/lists/listinfo/connotea-discuss.
GANTER, B. and WILLE, R. (1999): Formal Concept Analysis: Mathematical foundations. Springer.
HAMMOND, T., HANNAY, T., LUND, B. and SCOTT, J. (2005): Social Bookmarking Tools (I): A General Review. D-Lib Magazine, 11(4).
HOTHO, A., JÄSCHKE, R., SCHMITZ, C. and STUMME, G. (2006): Information Retrieval in Folksonomies: Search and Ranking. In: submitted for publication at ESWC 2006.
LEHMANN, F. and WILLE, R. (1995): A triadic approach to Formal Concept Analysis. In: G. Ellis, R. Levinson, W. Rich and J. F. Sowa (Eds.), Conceptual Structures: Applications, Implementation and Theory, vol. 954 of Lecture Notes in Computer Science. Springer. ISBN 3-540-60161-9.
HANNAY, T. (2005): Social Bookmarking Tools (II): A Case Study-Connotea. D-Lib Magazine, 11(4).
MATHES, A. (2004): Folksonomies — Cooperative Classification and Communication Through Shared Metadata. http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html.
MIKA, P. (2005): Ontologies Are Us: A Unified Model of Social Networks and Semantics. In: Y. Gil, E. Motta, V. R. Benjamins and M. A. Musen (Eds.), ISWC 2005, vol. 3729 of LNCS, pp. 522–536. Springer-Verlag, Berlin Heidelberg.
PASQUIER, N., BASTIDE, Y., TAOUIL, R. and LAKHAL, L. (1999): Closed set based discovery of small covers for association rules. In: Actes des 15mes journes Bases de Donnes Avances (BDA’99), pp. 361–381.
STAAB, S., SANTINI, S., NACK, F., STEELS, L. and MAEDCHE, A. (2002): Emergent semantics. Intelligent Systems, IEEE, 17(1):78.
STEELS, L. (1998): The Origins of Ontologies and Communication Conventions in Multi-Agent Systems. Autonomous Agents and Multi-Agent Systems, 1(2):169.
STUMME, G. (1999): Conceptual Knowledge Discovery with Frequent Concept Lattices. FB4-Preprint 2043, TU Darmstadt.
STUMME, G. (2002): Efficient Data Mining Based on Formal Concept Analysis. In: A. Hameurlain, R. Cicchetti and R. Traunmller (Eds.), Proc. DEXA 2002, vol. 2453 of LNCS, pp. 534–546. Springer, Heidelberg.
STUMME, G. (2005): A Finite State Model for On-Line Analytical Processing in Triadic Contexts. In: B. Ganter and R. Godin (Eds.), ICFCA, vol. 3403 of Lecture Notes in Computer Science, pp. 315–328. Springer. ISBN 3-540-24525-1.
WILLE, R. (1982): Restructuring lattices theory: An approach based on hierarchies of concepts. In: I. Rival (Ed.), Ordered Sets, pp. 445–470. Reidel, Dordrecht-Boston.
ZAKI, M. J. and HSIAO, C.-J. (1999): ChARM: An efficient algorithm for closed association rule mining. Technical Report 99-10. Tech. rep., Computer Science Dept., Rensselaer Polytechnic.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin · Heidelberg
About this paper
Cite this paper
Schmitz, C., Hotho, A., Jäschke, R., Stumme, G. (2006). Mining Association Rules in Folksonomies. In: Batagelj, V., Bock, HH., Ferligoj, A., Žiberna, A. (eds) Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-34416-0_28
Download citation
DOI: https://doi.org/10.1007/3-540-34416-0_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34415-5
Online ISBN: 978-3-540-34416-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)