Skip to main content
SpringerLink
Log in

Modeling Consensus Semantics in Social Tagging Systems

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

In social tagging systems, people can annotate arbitrary tags to online data to categorize and index them. However, the lack of the “a priori” set of words makes it difficult for people to reach consensus about the semantics of tags and how to categorize data. Ontologies based approaches can help reaching such consensus, but they are still facing problems such as inability of model ambiguous and new concepts properly. For tags that are used very few times, since they can only be used in very specific contexts, their semantics are very clear and detailed. Although people have no consensus on these tags, it is still possible to leverage these detailed semantics to model the other tags. In this paper we introduce a random walk and spreading activation like model to represent the semantics of tags using semantics of unpopular tags. By comparing the proposed model to the classic Latent Semantic Analysis approach in a concept clustering task, we show that the proposed model can properly capture the semantics of tags.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Halpin H, Robu V, Shepherd H. The complex dynamics of collaborative tagging. In Proc. the 16th International Conference on World Wide Web (WWW 2007), Banff, Canada, May 8–12, 2007, pp.211-220.

  2. Robu V, Halpin H, Shepherd H. Emergence of consensus and shared vocabularies in collaborative tagging systems. TWEB, 2009, 3(4): Article No.14.

  3. Gruber T. Ontology of Folksonomy: A mash-up of apples and oranges. Int. J. Semantic Web Inf. Syst., 2007, 3(2): 1–11.

    Article  Google Scholar 

  4. Schmitz C, Hotho A, Jaschke R, Stumme G. Mining association rules in folksonomies. In Proc. IFCS 2006, Ljubljana, Slovenia, Jul. 25–29, 2006, pp.261-270.

  5. Hotho A, Jaschke R, Schmitz C, Stumme G. Information retrieval in folksonomies: Search and ranking. In Proc. the 3rd European Semantic Web Conference on the Semantic Web: Research and Applications (ESWC 2006), Budva, Montenegro, Jun. 11–14, 2006, pp.411-426.

  6. Wu X, Zhang L, Yu Y. Exploring social annotations for the semantic web. In Proc. the 15th International Conference on World Wide Web (WWW 2006), Edinburgh, UK, May 23–26, 2006, pp.417-426.

  7. Lambiotte R, Ausloos M. Collaborative tagging as a tripartite network. In Proc. the 14th International Conference on Conceptual Structures: Inspiration and Application (ICCS 2006), Aalborg, Denmark, Jul. 16–21, 2006, pp.1114-1117.

  8. Zlatic V, Ghoshal G, Caldarelli G. Hypergraph topological quantities for tagged social networks. Physical Review E, 2009, 80(3): 036118.

    Article  MathSciNet  Google Scholar 

  9. Zhang Z K, Liu C. A hypergraph model of social tagging networks. J. Stat. Mech., 2010: P10005.

  10. Mika P. Ontologies are us: A unified model of social networks and semantics. J. Web Sem., 2007, 5(1): 5–15.

    Article  MathSciNet  Google Scholar 

  11. Yeung C A, Gibbins N, Shadbolt N. Tag meaning disambiguation through analysis of tripartite structure of Folksonomies. In Proc. Web Intelligence/IAT Workshops 2007, Silicon Valley, USA, Nov. 2–5, 2007, pp.3–6.

  12. Shang M S, Zhang Z K. Diffusion-based recommendation in collaborative tagging system. Chin. Phys. Lett., 2009, 26(11): 118903.

    Article  MathSciNet  Google Scholar 

  13. Zhang Z K, Zhou T, Zhang Y C. Personalized recommendation via integrated diffusion on user-tem-tag tripartite graphs. Physica A, 2010, 389(1): 179–186.

    Article  Google Scholar 

  14. Shang M S, Zhang Z K, Zhou T, Zhang Y C. Collaborative filtering with diffusion-based similarity on tripartite graphs. Physica A, 2010, 389(6): 1259–1264.

    Article  Google Scholar 

  15. Zhang Z K, Liu C, Zhang Y C, Zhou T. Solving the cold-start problem in recommender systems with social tags. Europhysics Letters, 2010, 92(2): 28002.

    Article  MathSciNet  Google Scholar 

  16. Tso-Sutter K H L, Marinho L B, Schmidt-Thieme L. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proc. SAC 2008, Fortaleza, Brazil, Mar. 16–20, pp.1995–1999.

  17. Wetzker R, Umbrath W, Said A. A hybrid approach to item recommendation in folksonomies. In Proc. ESAIR 2009, Barcelona, Spain, Feb. 9–11, 2009, pp.25–29.

  18. Shen K, Wu L. Folksonomy as a complex network. Cornell University Library e-prints arXiv:cs/0509072vl, 2006, http://arxiv.org/abs/cs/0509072.

  19. Veres C. Concept modeling by the masses: Folksonomy structure and interoperability. In Proc. the 25th International Conference on Conceptual Modeling, Tucson, USA, Nov. 6–9, 2006, pp.325–338.

  20. Cattuto C, Loreto V, Pietronero L. Collaborative tagging and semiotic dynamics. Cornell University Library e-prints arXiv:cs/0605015VI, 2006, http://arxiv.org/abs/cs/0605015.

  21. Fuxman A, Tsaparas P, Achan K, Agrawal R. Using the wisdom of the crowds for keyword generation. In Proc. the 17th International Conference on World Wide Web (WWW 2008), Beijing, China, Apr. 21–25, 2008, pp.61–70.

  22. Fu W, Kannampallil T G, Kang R, He J. Semantic imitation in social tagging. ACM Trans. Comput.-Hum. Interact., 2010, 17(3): Article No.12.

  23. Manning C D, Raghavan P, Schutze H. Introduction to Information Retrieval. Cambridge University Press, 2008.

  24. Olson D L, Delen D. Advanced Data Mining Techniques. Springer, 2008.

  25. Deerwester S C, Dumais S T, Landauer T K, Furnas G W, Harshman R A. Indexing by latent semantic analysis. JASIS, 1990, 41(6): 391–407.

    Article  Google Scholar 

  26. R Development Core Team. R: A language and environment for statistical computing. http://www.R-project.org.

  27. Karypis G. CLUTO: A Clustering Toolkit, http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview.

Download references

Author information

Authors and Affiliations

  1. College of Information Science and Technology, Northeastern University, Shenyang, 110004, China

    Bin Zhang (Senior Member, CCF, Member, ACM) & Yin Zhang

  2. Computing Center, Northeastern University, Shenyang, 110004, China

    Ke-Ning Gao

Authors
  1. Bin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ke-Ning Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Zhang.

Additional information

Supported by the National Natural Science Foundation of China under Grant No. 61073062, the Natural Science Foundation of Liaoning Province of China under Grant No. 20102060 and the Fundamental Research Funds for the Central Universities under Grant No. N090604010.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

(PDF 73.8 KB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, B., Zhang, Y. & Gao, KN. Modeling Consensus Semantics in Social Tagging Systems. J. Comput. Sci. Technol. 26, 806–815 (2011). https://doi.org/10.1007/s11390-011-0179-y

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-011-0179-y

Keywords

Search

Navigation