Integration of Semantically Annotated Data by the KnoFuss Architecture

  • Andriy Nikolov
  • Victoria Uren
  • Enrico Motta
  • Anne de Roeck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5268)

Abstract

Most of the existing work on information integration in the Semantic Web concentrates on resolving schema-level problems. Specific issues of data-level integration (instance coreferencing, conflict resolution, handling uncertainty) are usually tackled by applying the same techniques as for ontology schema matching or by reusing the solutions produced in the database domain. However, data structured according to OWL ontologies has its specific features: e.g., the classes are organized into a hierarchy, the properties are inherited, data constraints differ from those defined by database schema. This paper describes how these features are exploited in our architecture KnoFuss, designed to support data-level integration of semantic annotations.

References

  1. 1.
    Kim, W., Seo, J.: Classifying schematic and data heterogeneity in multidatabase systems. IEEE Computer 24(12), 12–18 (1991)Google Scholar
  2. 2.
    Thor, A., Rahm, E.: MOMA - a mapping-based object matching system. In: 3rd Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA (2007)Google Scholar
  3. 3.
    Straccia, U., Troncy, R.: oMAP: Combining classifiers for aligning automatically OWL ontologies. In: Ngu, A.H.H., Kitsuregawa, M., Neuhold, E.J., Chung, J.-Y., Sheng, Q.Z. (eds.) WISE 2005. LNCS, vol. 3806. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg (2007)MATHGoogle Scholar
  5. 5.
    Fellegi, I.P., Sunter, A.B.: A theory for record linkage. Journal of American Statistical Association 64(328), 1183–1210 (1969)CrossRefGoogle Scholar
  6. 6.
    Elmagarmid, A.K., Ipeirotis, P.G., Verykios, V.S.: Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering 19(1), 1–16 (2007)CrossRefGoogle Scholar
  7. 7.
    Bilenko, M., Mooney, R.J.: Adaptive duplicate detection using learnable string similarity measures. In: 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003), Washington, DC, pp. 39–48 (2003)Google Scholar
  8. 8.
    Rendle, S., Schmidt-Thieme, L.: Object identification with constraints. In: 6th IEEE International Conference on Data Mining (ICDM) (2006)Google Scholar
  9. 9.
    Jian, N., Hu, W., Cheng, G., Qu, Y.: Falcon-AO: Aligning ontologies with Falcon. In: K-CAP Workshop on Integrating Ontologies, Banff, CA, pp. 87–93 (2005)Google Scholar
  10. 10.
    Ehrig, M.: Ontology Alignment: Bridging the Semantic Gap. Springer, New York (2007)Google Scholar
  11. 11.
    Ehrig, M., Staab, S., Sure, Y.: Bootstrapping ontology alignment methods with APFEL. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 186–200. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Lee, Y., Sayyadian, M., Doan, A., Rosenthal, A.S.: eTuner: Tuning schema matching software using synthetic scenarios. VLDB Journal 16, 97–122 (2007)Google Scholar
  13. 13.
    Nikolov, A., Uren, V., Motta, E., de Roeck, A.: Using the Dempster-Shafer theory of evidence to resolve ABox inconsistencies. In: Workshop on Uncertainty Reasoning for the Semantic Web, ISWC 2007, Busan, Korea (2007)Google Scholar
  14. 14.
    Motta, E.: Reusable Components for Knowledge Modelling. Frontiers in Artificial Intelligence and Applications, vol. 53. IOS Press, Amsterdam (1999)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Andriy Nikolov
    • 1
  • Victoria Uren
    • 1
  • Enrico Motta
    • 1
  • Anne de Roeck
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK

Personalised recommendations