Improving Folksonomies Using Formal Knowledge: A Case Study on Search

  • Sofia Angeletou
  • Marta Sabou
  • Enrico Motta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5926)


Search in folksonomies is impeded by lack of machine understandable descriptions for the meaning of tags and their relations. One approach to addressing this problem is the use of formal knowledge resources (KS) to assign meaning to the tags, most notably WordNet and (online) ontologies. However, there is no insight of how the different characteristics of such KS can contribute to improving search in folksonomies. In this work we compare the two KS in the context of folksonomy search, first by evaluating the enriched structures and then by performing a user study on searching the folksonomy content through these structures. We also compare them to cluster-based folksonomy search. We show that the diversity of ontologies leads to more satisfactory results compared to WordNet although the latter provides richer structures. We also conclude that the idiosyncrasies of folksonomies can not be addressed by only using formal KS.


Knowledge Source Query Expansion Knowledge Source Semantic Structure Query Keyword 
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.


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  1. 1.
    Abbasi, R., Staab, S.: Richvsm: enriched vector space models for folksonomies. In: Proc. of the 20th ACM conf. on Hypertext and hypermedia (2009)Google Scholar
  2. 2.
    Angeletou, S., Sabou, M., Motta, E.: Semantically enriching folksonomies with FLOR. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021. Springer, Heidelberg (2008)Google Scholar
  3. 3.
    Golder, S., Huberman, B.: Usage patterns of collaborative tagging systems. Journal of Information Science 32, 198–208 (2006)CrossRefGoogle Scholar
  4. 4.
    Hotho, A., Jarschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: Search and ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Huiskes, M., Lew, M.: The mir flickr retrieval evaluation. In: Proc. of the ACM Int. Conf. on MIR (2008)Google Scholar
  6. 6.
    Laniado, D., Eynard, D., Colombetti, M.: Using WordNet to turn a folksonomy into a hierarchy of concepts. In: Proc.of 4th SWAP (2007)Google Scholar
  7. 7.
    Lee, S., Yong, H.: Tagplus: A retrieval system using synonym tag in folksonomy. In: Int. Conf. on Multimedia and Ubiquitous Engineering (2007)Google Scholar
  8. 8.
    Pan, J., Taylor, S., Thomas, E.: Reducing ambiguity in tagging systems with folksonomy search expansion. In: Aroyo, L., Traverso, P., Ciravegna, F., Cimiano, P., Heath, T., Hyvönen, E., Mizoguchi, R., Oren, E., Sabou, M., Simperl, E. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 669–683. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Sabou, M., Gracia, J., Angeletou, S., d’Aquin, M., Motta, E.: Evaluating the semantic web: A task-based approach. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 423–437. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    van Zwol, R., Murdock, V., Garcia Pueyo, L., Ramirez, G.: Diversifying image search with user generated content. In: Proc. of the ACM Int. Conf. on MIR (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sofia Angeletou
    • 1
  • Marta Sabou
    • 1
  • Enrico Motta
    • 1
  1. 1.Knowledge Media Institute (KMi)The Open UniversityMilton KeynesUnited Kingdom

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