Modeling Consensus Semantics in Social Tagging Systems

  • Bin ZhangEmail author
  • Yin Zhang
  • Ke-Ning Gao
Regular Paper


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.


algorithms knowledge acquisition Markov processes 

Supplementary material

11390_2011_179_MOESM1_ESM.pdf (74 kb)
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  1. [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.Google Scholar
  2. [2]
    Robu V, Halpin H, Shepherd H. Emergence of consensus and shared vocabularies in collaborative tagging systems. TWEB, 2009, 3(4): Article No.14.Google Scholar
  3. [3]
    Gruber T. Ontology of Folksonomy: A mash-up of apples and oranges. Int. J. Semantic Web Inf. Syst., 2007, 3(2): 1–11.CrossRefGoogle Scholar
  4. [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.Google Scholar
  5. [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.Google Scholar
  6. [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.Google Scholar
  7. [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.Google Scholar
  8. [8]
    Zlatic V, Ghoshal G, Caldarelli G. Hypergraph topological quantities for tagged social networks. Physical Review E, 2009, 80(3): 036118.MathSciNetCrossRefGoogle Scholar
  9. [9]
    Zhang Z K, Liu C. A hypergraph model of social tagging networks. J. Stat. Mech., 2010: P10005.Google Scholar
  10. [10]
    Mika P. Ontologies are us: A unified model of social networks and semantics. J. Web Sem., 2007, 5(1): 5–15.MathSciNetCrossRefGoogle Scholar
  11. [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.Google Scholar
  12. [12]
    Shang M S, Zhang Z K. Diffusion-based recommendation in collaborative tagging system. Chin. Phys. Lett., 2009, 26(11): 118903.MathSciNetCrossRefGoogle Scholar
  13. [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.CrossRefGoogle Scholar
  14. [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.CrossRefGoogle Scholar
  15. [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.MathSciNetCrossRefGoogle Scholar
  16. [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.Google Scholar
  17. [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.Google Scholar
  18. [18]
    Shen K, Wu L. Folksonomy as a complex network. Cornell University Library e-prints arXiv:cs/0509072vl, 2006,
  19. [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.Google Scholar
  20. [20]
    Cattuto C, Loreto V, Pietronero L. Collaborative tagging and semiotic dynamics. Cornell University Library e-prints arXiv:cs/0605015VI, 2006,
  21. [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.Google Scholar
  22. [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.Google Scholar
  23. [23]
    Manning C D, Raghavan P, Schutze H. Introduction to Information Retrieval. Cambridge University Press, 2008.Google Scholar
  24. [24]
    Olson D L, Delen D. Advanced Data Mining Techniques. Springer, 2008.Google Scholar
  25. [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.CrossRefGoogle Scholar
  26. [26]
    R Development Core Team. R: A language and environment for statistical computing.
  27. [27]
    Karypis G. CLUTO: A Clustering Toolkit,

Copyright information

© Springer Science+Business Media, LLC & Science Press, China 2011

Authors and Affiliations

  1. 1.College of Information Science and TechnologyNortheastern UniversityShenyangChina
  2. 2.Computing CenterNortheastern UniversityShenyangChina

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