An Architecture for Emergent Semantics

  • Sven Herschel
  • Ralf Heese
  • Jens Bleiholder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4231)


Emergent Semantics is a new paradigm for inferring semantic meaning from implicit feedback by a sufficiently large number of users of an object retrieval system. In this paper, we introduce a universal architecture for emergent semantics using a central repository within a multi-user environment, based on solid linguistic theories.

Based on this architecture, we have implemented an information retrieval system supporting keyword queries on standard information retrieval corpora. Contrary to existing query refinement strategies, feedback on the retrieval results is incorporated directly into the actual document representations improving future retrievals.

An evaluation yields higher precision values at the standard recall levels and thus demonstrates the effectiveness of the emergent semantics approach for typical information retrieval problems.


Query Processing Object Representation Query Term Query Expansion Vector Space Model 
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.
    Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill Book Company, New York (1984)Google Scholar
  2. 2.
    Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by Latent Semantic Analysis. Journal of the American Society of Information Science 41(6), 391–407 (1990)CrossRefGoogle Scholar
  3. 3.
    Morris, C.W.: Foundations of the Theory of Signs. Chicago University Press, Chicago (1938)Google Scholar
  4. 4.
    Jacob, C., Radusch, I., Steglich, S.: Enhancing legacy services through context-enriched sensor data. In: Proceedings of the International Conference on Internet Computing (2006)Google Scholar
  5. 5.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, New York (1999)Google Scholar
  6. 6.
    Salton, G., Lesk, M.E.: Computer evaluation of indexing and text processing. Journal of the ACM 15(1), 8–36 (1968)MATHCrossRefGoogle Scholar
  7. 7.
    Robertson, S.E., Jones, K.S.: Relevance weighting of search terms. Journal of the American Society for Information Science 27(3), 129–146 (1976)CrossRefGoogle Scholar
  8. 8.
    The Apache Software Foundation (2006),
  9. 9.
    Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 210–217. ACM Press, New York (1995)Google Scholar
  10. 10.
    Oard, D.W.: The state of the art in text filtering. User Modeling and User-Adapted Interaction 7(3), 141–178 (1997)CrossRefGoogle Scholar
  11. 11.
    Heflin, J., Hendler, J., Luke, S.: SHOE: A knowledge representation language for internet applications. Technical Report CS-TR-4078 (UMIACS TR-99-71), University of Maryland (1999)Google Scholar
  12. 12.
    Grosky, W.I., Sreenath, D.V., Fotouhi, F.: Emergent semantics and the multimedia semantic web. SIGMOD Rec. 31(4), 54–58 (2002)CrossRefGoogle Scholar
  13. 13.
    Aberer, K., Cudré-Mauroux, P., Hauswirth, M.: The chatty web: emergent semantics through gossiping. In: Proceedings of the Twelfth International Conference on World Wide Web, pp. 197–206. ACM Press, New York (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sven Herschel
    • 1
  • Ralf Heese
    • 1
  • Jens Bleiholder
    • 1
  1. 1.Humboldt-Universität zu BerlinBerlinGermany

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