Advertisement

Getting the Meaning Right: A Complementary Distributional Layer for the Web Semantics

  • Vít Nováček
  • Siegfried Handschuh
  • Stefan Decker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7031)

Abstract

We aim at providing a complementary layer for the web semantics, catering for bottom-up phenomena that are empirically observable on the Semantic Web rather than being merely asserted by it. We focus on meaning that is not associated with particular semantic descriptions, but emerges from the multitude of explicit and implicit links on the web of data. We claim that the current approaches are mostly top-down and thus lack a proper mechanisms for capturing the emergent aspects of the web meaning. To fill this gap, we have proposed a framework based on distributional semantics (a successful bottom-up approach to meaning representation in computational linguistics) that is, however, still compatible with the top-down Semantic Web principles due to inherent support of rules. We evaluated our solution in a knowledge consolidation experiment, which confirmed the promising potential of our approach.

Keywords

Internal Tandem Duplication Ontology Match Distributional Semantic Link Open Data Cloud Corpus Representation 
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.

References

  1. 1.
    de Saussure, F.: Course in General Linguistics, Open Court, Illinois (1983)Google Scholar
  2. 2.
    Firth, J.: A synopsis of linguistic theory 1930-1955. Studies in Ling. Anal. (1957)Google Scholar
  3. 3.
    Baroni, M., Lenci, A.: Distributional memory: A general framework for corpus-based semantics. Computational Linguistics (2010)Google Scholar
  4. 4.
    Franz, T., Schultz, A., Sizov, S., Staab, S.: TripleRank: Ranking Semantic Web Data by Tensor Decomposition. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 213–228. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Goldman, R., Widom, J.: DataGuides: Enabling query formulation and optimization in semistructured databases. In: VLDB. Morgan Kaufmann (1997)Google Scholar
  6. 6.
    Chemudugunta, C., Holloway, A., Smyth, P., Steyvers, M.: Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 229–244. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI 2010. (2010)Google Scholar
  8. 8.
    Maedche, A.: Emergent semantics for ontologies. In: Emergent Semantics. IEEE Intelligent Systems, pp. 85–86. IEEE Press (2002)Google Scholar
  9. 9.
    Aberer, K., Cudré-Mauroux, P., Catarci, A.M.O(e.) T., Hacid, M.-S., Illarramendi, A., Kashyap, V., Mecella, M., Mena, E., Neuhold, E.J., De Troyer, O., Risse, T., Scannapieco, M., Saltor, F., Santis, L.d., Spaccapietra, S., Staab, S., Studer, R.: Emergent Semantics Principles and Issues. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 25–38. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  11. 11.
    Taubert, J., Hindle, M., Lysenko, A., Weile, J., Köhler, J., Rawlings, C.J.: Linking Life Sciences Data Using Graph-Based Mapping. In: Paton, N.W., Missier, P., Hedeler, C. (eds.) DILS 2009. LNCS, vol. 5647, pp. 16–30. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    ter Horst, H.J.: Completeness, decidability and complexity of entailment for RDF schema and a semantic extension involving the OWL vocabulary. Journal of Web Semantics, 79–115 (2005)Google Scholar
  13. 13.
    Doorenbos, R.B.: Production Matching for Large Learning Systems. PhD thesis (1995)Google Scholar
  14. 14.
    Pesquita, C., Faria, D., Falco, A.O., Lord, P., Couto, F.M.: Semantic similarity in biomedical ontologies. PLoS Computational Biololgy 5(7) (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vít Nováček
    • 1
  • Siegfried Handschuh
    • 1
  • Stefan Decker
    • 1
  1. 1.Digital Enterprise Research Institute (DERI)National University of Ireland Galway (NUIG)GalwayIreland

Personalised recommendations