Improving Accuracy of Tagging Systems Using Tag Qualifiers and Tagraph Vocabulary System

  • Syavash Nobarany
  • Mona Haraty

This short paper addresses the lack of accuracy in social tagging systems, as information retrieval systems, in comparison with traditional search-engines. The lack of accuracy is caused by the vocabulary problems and the nature of tagging systems which rely on lower number of index terms for each resource. Tagraph vocabulary system which is based on a weighted directed graph of tags, and Tag Qualifiers, are proposed to mitigate these problems and increase the precision and the recall of social tagging systems. Both solutions are based on community contributions, therefore specific procedures such as task routing should be used to increase the number of contributions and therefore to achieve a more accurate system.


Information Retrieval System Index Term Relevance Qualifier Weighted Directed Graph Information Retrieval Method 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sen S, Lam S K, Cosley D, Rashid A M, Frankowski D, Harper F, Osterhouse J, Riedl J (2006) tagging, community, vocabulary, evolution. 20th anniversary conference on Computer supported cooperative work, pp. 181–190. ACM PressGoogle Scholar
  2. 2.
    Marlow C, Naaman M, Boyd D, Davis M (2006) HT06, Tagging Paper, Taxonomy, Flickr, Academic Article, ToRead., seventeenth ACM conference on Hypertext and hypermedia, pp. 31–40. ACM PressGoogle Scholar
  3. 3.
    Golber S, Huberman B A, The Structure of Collaborative Tagging System, Information Dynamics Lab: HP Labs, Palo Alto, USA,
  4. 4.
    Cosley D, Frankowski D, Terveen L, Riedl J (2007) SuggestBot: using intelligent task routing to help people find work in wikipedia. 12th international Conference on intelligent User interfaces, pp. 181–190. ACM Press.Google Scholar
  5. 5.
    Korfiatis N (2007) Social and Economic Incentives in Online Social Interactions: A Model and Typology. 30th Information Systems Research Seminar in Scandinavia IRIS.Google Scholar
  6. 6.
    Ludford P, Cosley D, Frankowski D, Terveen L (2004). Think Different: Increasing Online Community Participation Using Uniqueness and Group Dissimilarity. CHI 2004, pp. 631–638. ACM PressGoogle Scholar
  7. 7.
    [7] Furnas G W, Landauer T K, Gomez L M, Dumais S T (1987) The vocabulary problem in human-system communication. Communications, 30 (11), 964 – 971. ACM Press.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.School of ECEUniversity of TehranTehranIran

Personalised recommendations