Combining Semantic Web Search with the Power of Inductive Reasoning

  • Claudia d’Amato
  • Nicola Fanizzi
  • Bettina Fazzinga
  • Georg Gottlob
  • Thomas Lukasiewicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6379)

Abstract

With the introduction of the Semantic Web as a future substitute of the Web, the key task for the Web, namely, Web Search, is evolving towards some novel form of Semantic Web search. A very promising recent approach to Semantic Web search is based on combining standard Web pages and search queries with ontological background knowledge, and using standard Web search engines as the main inference motor of Semantic Web search. In this paper, we continue this line of research. We propose to further enhance this approach by the use of inductive reasoning. This increases the robustness of Semantic Web search, as it adds the important ability to handle inconsistencies, noise, and incompleteness, which are all very likely to occur in distributed and heterogeneous environments such as the Web. In particular, inductive reasoning allows to infer (from training individuals) new knowledge, which is not logically deducible. We also report on a prototype implementation of the new approach and its experimental evaluations.

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References

  1. 1.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)MATHGoogle Scholar
  2. 2.
    Bao, J., Kendall, E.F., McGuinness, D.L., Wallace, E.K.: OWL2 Web ontology language: Quick reference guide (2008), http://www.w3.org/TR/owl2-quick-reference/
  3. 3.
    Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Sci. Am. 284, 34–43 (2001)CrossRefGoogle Scholar
  4. 4.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 30(1-7), 107–117 (1998)Google Scholar
  5. 5.
    Buitelaar, P., Cimiano, P.: Ontology Learning and Population: Bridging the Gap Between Text and Knowledge. IOS Press, Amsterdam (2008)MATHGoogle Scholar
  6. 6.
    Chirita, P.-A., Costache, S., Nejdl, W., Handschuh, S.: P-TAG: Large scale automatic generation of personalized annotation TAGs for the Web. In: Proc. WWW 2007, pp. 845–854. ACM Press, New York (2007)CrossRefGoogle Scholar
  7. 7.
    d’Amato, C., Fanizzi, N., Esposito, F.: Query answering and ontology population: An inductive approach. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 288–302. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Ding, L., Finin, T.W., Joshi, A., Peng, Y., Pan, R., Reddivari, P.: Search on the Semantic Web. IEEE Computer 38(10), 62–69 (2005)Google Scholar
  9. 9.
    Ding, L., Pan, R., Finin, T.W., Joshi, A., Peng, Y., Kolari, P.: Finding and ranking knowledge on the Semantic Web. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 156–170. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Fanizzi, N., d’Amato, C., Esposito, F.: Induction of classifiers through non-parametric methods for approximate classification and retrieval with ontologies. International Journal of Semantic Computing 2(3), 403–423 (2008)MATHCrossRefGoogle Scholar
  11. 11.
    Fanizzi, N., d’Amato, C., Esposito, F.: Metric-based stochastic conceptual clustering for ontologies. Inform. Syst. 34(8), 725–739 (2009)CrossRefGoogle Scholar
  12. 12.
    Fazzinga, B., Gianforme, G., Gottlob, G., Lukasiewicz, T.: Semantic Web search based on ontological conjunctive queries. In: Link, S., Prade, H. (eds.) FoIKS 2010. LNCS, vol. 5956, pp. 153–172. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Fazzinga, B., Lukasiewicz, T.: Semantic search on the Web. Semantic Web — Interoperability, Usability, Applicability (forthcoming)Google Scholar
  14. 14.
    Guha, R.V., McCool, R., Miller, E.: Semantic search. In: Proc. WWW 2003, pp. 700–709. ACM Press, New York (2003)CrossRefGoogle Scholar
  15. 15.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning – Data Mining, Inference, and Prediction. Springer, Heidelberg (2001)MATHGoogle Scholar
  16. 16.
    Horrocks, I., Patel-Schneider, P.F., van Harmelen, F.: From \(\mathcal{SHIQ}\) and RDF to OWL: The making of a Web ontology language. J. Web. Sem. 1(1), 7–26 (2003)Google Scholar
  17. 17.
    Lei, Y., Uren, V.S., Motta, E.: SemSearch: A search engine for the Semantic Web. In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS (LNAI), vol. 4248, pp. 238–245. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search — The Metric Space Approach. In: Advances in Database Systems, vol. 32. Springer, Heidelberg (2006)Google Scholar
  19. 19.
    W3C. OWL Web ontology language overview, 2004. W3C Recommendation (February 10, 2004), http://www.w3.org/TR/2004/REC-owl-features-20040210/

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Claudia d’Amato
    • 1
  • Nicola Fanizzi
    • 1
  • Bettina Fazzinga
    • 2
  • Georg Gottlob
    • 3
    • 4
  • Thomas Lukasiewicz
    • 3
    • 5
  1. 1.Dipartimento di InformaticaUniversità degli Studi di BariItaly
  2. 2.Dipartimento di Elettronica, Informatica e SistemisticaUniversità della CalabriaItaly
  3. 3.Computing LaboratoryUniversity of OxfordUK
  4. 4.Oxford-Man Institute of Quantitative FinanceUniversity of OxfordUK
  5. 5.Institut für InformationssystemeTU WienAustria

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