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A method for the classification of folksonomy resources

Published: 21 March 2011 Publication History

Abstract

The paper presents a method for the automatic classification of the resources of collaborative tagging systems, also called folksonomies. Folksonomies are an easy way of representing knowledge in Web 2.0 because of its simplicity. However, due to their characteristics, the information retrieval in these systems is more difficult than in classical knowledge representation systems. This method has a high degree of precision and recall, and may adjust these values with the use of different modes of classification and thresholds, improving the performances of other resource classification methods.

References

[1]
R. Abbasi, S. Staab, and P. Cimiano. Organizing resources on tagging systems using t-org. In Bridging the Gap between Semantic Web and Web 2.0, 2007.
[2]
E. H. Chi and T. Mytkowicz. Understanding the efficiency of social tagging systems using information theory. In Proceedings of the 9th ACM Conference on Hypertext and hypermedia, pages 81--88. ACM, 2008.
[3]
S. A. Golder and B. A. Huberman. Usage patterns of collaborative tagging systems. Journal of Information Science, 32(2): 198--208, April 2006.
[4]
I. Peters. Folksonomies: indexing and retrieval in Web 2.0. Knowledge & information: studies in information science, 1868--842X. De Gruyter/Saur, 2009.
[5]
V. Robu, H. Halpin, and H. Shepherd. Emergence of consensus and shared vocabularies in collaborative tagging systems. ACM Trans. Web, 3(4): 1--34, 2009.
[6]
S. Siersdorfer and S. Sizov. Social recommender systems for web 2.0 folksonomies. In 20th ACM Conf. Hypertext and hypermedia, pages 261--270. ACM, 2009.
[7]
A. Singhal. Modern information retrieval: a brief overview. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 24, 2001.
[8]
S. Staab. Emergent semantics. IEEE Intelligent Systems, 17(1): 78--86, 2002.
[9]
L. Steels. The origins of ontologies and communication conventions in multi-agent systems. Autonomous Agents and Multi-Agent Systems, 1(2): 169--194, 1998.
[10]
T. Vander Wal. Folksonomy coinage and definition. Website, February 2007.
[11]
Z. Yin, R. Li, Q. Mei, and J. Han. Exploring social tagging graph for web object classification. In Proceedings of the 15th ACM SIGKDD, pages 957--966, New York, NY, USA, 2009. ACM.

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cover image ACM Conferences
SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing
March 2011
1868 pages
ISBN:9781450301138
DOI:10.1145/1982185
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 March 2011

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Author Tags

  1. clustering
  2. collaborative tagging systems
  3. information retrieval
  4. knowledge discovery

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  • Research-article

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  • Spanish Research grants

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SAC'11
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SAC'11: The 2011 ACM Symposium on Applied Computing
March 21 - 24, 2011
TaiChung, Taiwan

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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