One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata

  • Dominik Benz
  • Christian Körner
  • Andreas Hotho
  • Gerd Stumme
  • Markus Strohmaier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6644)


Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and WikiTaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.


tagging generality measures emergent semantics folksonomies 


  1. 1.
    Allen, R., Wu, Y.: Generality of texts. In: Digital Libraries: People, Knowledge, and Technology. LNCS, Springer, Heidelberg (2010)Google Scholar
  2. 2.
    Benz, D., Hotho, A., Stumme, G.: Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge. In: Proc. of WebSci 2010, Raleigh, NC, USA (2010)Google Scholar
  3. 3.
    Bozsak, E., Ehrig, M., Handschuh, S., Hotho, A., Maedche, A., Motik, B., Oberle, D., Schmitz, C., Staab, S., Stojanovic, L., Stojanovic, N., Studer, R., Stumme, G., Sure, Y., Tane, J., Volz, R., Zacharias, V.: KAON - Towards a Large Scale Semantic Web. In: Bauknecht, K., Tjoa, A.M., Quirchmayr, G. (eds.) EC-Web 2002. LNCS, vol. 2455, pp. 304–313. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Brandes, U., Pich, C.: Centrality estimation in large networks. I. J. Bifurcation and Chaos 17(7), 2303–2318 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Cattuto, C., Benz, D., Hotho, A., Stumme, G.: Semantic analysis of tag similarity measures in collaborative tagging systems. In: Proc. of the 3rd Workshop on Ontology Learning and Population (OLP3), Patras, Greece, pp. 39–43 (July 2008)Google Scholar
  6. 6.
    Cheng, W., Rademaker, M., De Baets, B., Hüllermeier, E.: Predicting partial orders: Ranking with abstention. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS, vol. 6321, pp. 215–230. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Cimiano, P., Hotho, A., Staab, S.: Learning concept hierarchies from text corpora using formal concept analysis. Journal of Artificial Intelligence Research (JAIR) 24, 305–339 (2005)zbMATHGoogle Scholar
  8. 8.
    Clark, J., Paivio, A.: Extensions of the Paivio, Yuille, and Madigan 1968 norms. Behavior Research Methods, Instruments, & Computers 36(3), 371 (2004)CrossRefGoogle Scholar
  9. 9.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  10. 10.
    Fleiss, J., et al.: Measuring nominal scale agreement among many raters. Psychological Bulletin 76(5), 378–382 (1971)CrossRefGoogle Scholar
  11. 11.
    Golder, S., Huberman, B.A.: Usage patterns of collaborative tagging systems. Journal of Information Science 32(2), 198–208 (2006)CrossRefGoogle Scholar
  12. 12.
    Henschel, A., Woon, W.L., Wächter, T., Madnick, S.: Comparison of generality based algorithm variants for automatic taxonomy generation. In: Proc. of IIT 2009, pp. 206–210. IEEE Press, Piscataway (2009)Google Scholar
  13. 13.
    Heymann, P., Garcia-Molina, H.: Collaborative creation of communal hierarchical taxonomies in social tagging systems. Tech. Rep. 2006-10, CS dep. (April 2006)Google Scholar
  14. 14.
    Heymann, P., Ramage, D., Garcia-Molina, H.: Social tag prediction. In: SIGIR 2008: Proc. of the 31st Annual Int’l ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 531–538. ACM, New York (2008)Google Scholar
  15. 15.
    Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: Search and ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS(LNAI), vol. 4011, pp. 411–426. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Kammann, R., Streeter, L.: Two meanings of word abstractness. Journal of Verbal Learning and Verbal Behavior 10(3), 303–306 (1971)CrossRefGoogle Scholar
  17. 17.
    Körner, C., Benz, D., Hotho, A., Strohmaier, M., Stumme, G.: Stop thinking, start tagging: tag semantics emerge from collaborative verbosity. In: Proc. of WWW 2010, pp. 521–530. ACM, New York (2010)Google Scholar
  18. 18.
    Maedche, A.: Ontology Learning for the Semantic Web. Kluwer Academic Publishing, Boston (2002)CrossRefzbMATHGoogle Scholar
  19. 19.
    Mika, P.: Ontologies are us: A unified model of social networks and semantics. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 522–536. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  20. 20.
    Paivio, A., Yuille, J.C., Madigan, S.A.: Concreteness, imagery, and meaningfulness values for 925 nouns. Journal of Experimental Psychology 76 (1968)Google Scholar
  21. 21.
    Ponzetto, S.P., Strube, M.: Deriving a large-scale taxonomy from wikipedia. In: AAAI, pp. 1440–1445. AAAI Press, Menlo Park (2007)Google Scholar
  22. 22.
    Schmitz, P.: Inducing ontology from flickr tags (2006)Google Scholar
  23. 23.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: A Core of Semantic Knowledge. In: 16th international World Wide Web conference, WWW 2007 (2007)Google Scholar
  24. 24.
    Wasserman, S., Faust, K.: Social network analysis: Methods and applications. Cambridge Univ. Pr, Cambridge (1994)CrossRefzbMATHGoogle Scholar
  25. 25.
    Zhang, Q.: Fuzziness - vagueness - generality - ambiguity. Journal of Pragmatics 29(1), 13–31 (1998)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dominik Benz
    • 1
  • Christian Körner
    • 2
  • Andreas Hotho
    • 3
  • Gerd Stumme
    • 1
  • Markus Strohmaier
    • 2
  1. 1.Knowledge and Data Engineering Group (KDE)University of KasselGermany
  2. 2.Knowledge Management InstituteGraz University of TechnologyAustria
  3. 3.Data Mining and Information Retrieval GroupUniversity of WürzburgGermany

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