Combining Declarative and Procedural Knowledge to Automate and Represent Ontology Mapping

  • Li Xu
  • David W. Embley
  • Yihong Ding
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4231)


Ontologies on the Semantic Web are by nature decentralized. From the body of ontology mapping approaches, we can draw a conclusion that an effective approach to automate ontology mapping requires both data and metadata in application domains. Most existing approaches usually represent data and metadata by ad-hoc data structures, which lack formalisms to capture the underlying semantics. Furthermore, to approach semantic interoperability, there is a need to represent mappings between ontologies with well-defined semantics that guarantee accurate exchange of information. To address these problems, we propose that domain ontologies attached with extraction procedures are capable of representing knowledge required to find direct and indirect matches between ontologies. And mapping ontologies attached with query procedures not only support equivalent inferences and computations on equivalent concepts and relations but also improve query performance by applying query procedures to derive target-specific views. We conclude that a combination of declarative and procedural representation based on ontologies favors the analysis and implementation for ontology mapping that promises accurate and efficient semantic interoperability.


Procedural Knowledge Domain Ontology Ontology Mapping Query Answer Vocabulary Term 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Menlo Park (1999)Google Scholar
  2. 2.
    Berlin, J., Motro, A.: Database schema matching using machine learning with feature selection. In: Pidduck, A.B., Mylopoulos, J., Woo, C.C., Ozsu, M.T. (eds.) CAiSE 2002. LNCS, vol. 2348, pp. 452–466. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Calvanese, D., De Giacomo, G., Lenzerini, M.: A framework for ontology integration. In: Proceedings of the 1st Internationally Semantic Web Working Symposium (SWWS), pp. 303–317 (2001)Google Scholar
  4. 4.
    Chaudhri, V.K., Farquhar, A., Fikes, R., Karp, P.D., Rice, J.P.: OKBC: a programmatic foundation for knowledge base interoperability. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI 1998), Madison, Wisconsin (1998)Google Scholar
  5. 5.
    Demos page for BYU data extraction group,
  6. 6.
    Dhamankar, R., Lee, Y., Doan, A., Halevy, A., Domingos, P.: iMAP: Discovering complex semantic matches between database schemas. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data (SIGMOD 2004), Paris, France, June 2004, pp. 283–294 (2004)Google Scholar
  7. 7.
    Do, H., Rahm, E.: COMA - a system for flexible combination of schema matching approaches. In: Proceedings of the 28th International Conference on Very Large Databases (VLDB), Hong Kong, China, August 2002, pp. 610–621 (2002)Google Scholar
  8. 8.
    Doan, A., Madhavan, J., Dhamankar, R., Domingos, P., Halevy, A.: Learning to match ontologies on the semantic web. VLDB Journal 12, 303–319 (2003)CrossRefGoogle Scholar
  9. 9.
    Embley, D.W., Campbell, D.M., Jiang, Y.S., Liddle, S.W., Lonsdale, D.W., Ng, Y.-K., Smith, R.D.: Conceptual-model-based data extraction from multiple-record Web pages. Data & Knowledge Engineering 31(3), 227–251 (1999)MATHCrossRefGoogle Scholar
  10. 10.
    Halevy, A.Y.: Answering queries using views: A survey. The VLDB Journal 10(4), 270–294 (2001)MATHCrossRefGoogle Scholar
  11. 11.
    Kalfoglou, Y., Schorlemmer, M.: Ontology mapping: the state of the art. The Knowledge Engineering Review 18(1), 1–31 (2003)CrossRefGoogle Scholar
  12. 12.
    Li, W., Clifton, C.: SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks. Data & Knowledge Engineering 33(1), 49–84 (2000)MATHCrossRefGoogle Scholar
  13. 13.
    Madhavan, J., Bernstein, P.A., Doan, A., Halevy, A.: Corpus-based schema matching. In: ICDT 2005 (2005)Google Scholar
  14. 14.
    Madhavan, J., Bernstein, P.A., Domingos, P., Halevy, A.: Representing and reasoning about mappings between domain models. In: Proceedings of the 18th National Conference on Artificial Intelligence (AAAI 2002) (2002)Google Scholar
  15. 15.
    Maedche, A., Motic, B., Silva, N., Volz, R.: Mafra - an ontology mapping framework in the semantic web. In: Proceedings of the ECAI Workshop on Knowledge Transformation, Lyon, France (July 2002)Google Scholar
  16. 16.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. The VLDB Journal 10(4), 334–350 (2001)MATHCrossRefGoogle Scholar
  17. 17.
    Russell, S., Norvig, P.: Artificial Intelligence: A Mordern Approach, 2nd edn. Pearson Education, Inc., London (2003)Google Scholar
  18. 18.
    Xu, L., Embley, D.W.: A composite approach to automating direct and indirect schema mappings. Information Systems, available online (April 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Li Xu
    • 1
  • David W. Embley
    • 2
  • Yihong Ding
    • 2
  1. 1.Department of Computer ScienceUniversity of Arizona SouthSierra VistaU.S.A.
  2. 2.Department of Computer ScienceBrigham Young UniversityProvoU.S.A.

Personalised recommendations