Semantic Web Reasoning for Ontology-Based Integration of Resources

  • Liviu Badea
  • Doina Tilivea
  • Anca Hotaran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3208)


The Semantic Web should enhance the current World Wide Web with reasoning capabilities for enabling automated processing of possibly distributed information. In this paper we describe an architecture for Semantic Web reasoning and query answering in a very general setting involving several heterogeneous information sources, as well as domain ontologies needed for offering a uniform and source-independent view on the data. Since querying a Web source is very costly in terms of response time, we focus mainly on the query planner of such a system, as it may allow avoiding the access to query-irrelevant sources or combinations of sources based on knowledge about the domain and the sources.


Resource Description Framework Description Logic Integrity Constraint Domain Ontology Query Planning 
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.
    Aquilino, D., Asirelli, P., Renso, C., Turini, F.: MedLan: a Logic-based Mediator Language, IEI Technical Report B4-16 (November 1997)Google Scholar
  2. 2.
    Arens, Y., Knoblock, C.A., Hsu, C.-N.: Query Processing in the SIMS Information Mediator. In: Tate, A. (ed.) Advanced Planning Technology. AAAI Press, Menlo Park (1996)Google Scholar
  3. 3.
    Badea, L., Tilivea, D.: Intelligent Information Integration as a Constraint Handling Problem. In: Andreasen, T., Motro, A., Christiansen, H., Larsen, H.L. (eds.) FQAS 2002. LNCS (LNAI), vol. 2522, pp. 12–27. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Berger, S., Bry, F., Schaffert, S., Wieser, C.: Xcerpt and visXcerpt: From Pattern-Based to Visual Querying of XML and Semistructured Data. In: Proceedings VLDB 2003, Berlin (September 2003),
  5. 5.
    Bressan, S., Goh, C.H.: Answering queries in context. In: Andreasen, T., Christiansen, H., Larsen, H.L. (eds.) FQAS 1998. LNCS (LNAI), vol. 1495, p. 68. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Duschka, O.M., Genesereth, M.R.: Infomaster - An Information Integration Tool. In: Proc. International Workshop “Intelligent Information Integration”, KI 1997, Freiburg (1997)Google Scholar
  7. 7.
    Decker, S., Sintek, M.: Triple - an RDF query, inference, and transformation language. In: Proc. of the 2002 International Semantic Web Conference, ISWC-2002 (2002)Google Scholar
  8. 8.
    Decker, S., Brickley, D., Saarela, J., Angele, J.: A Query and Inference Service for RDF. In: QL 1998 – The Query Languages Workshop. World Wide Web Consortium (1998)Google Scholar
  9. 9.
    Fensel, D., Angele, J., Decker, S., Erdmann, M., Schnurr, H.P., Staab, S., Studer, R., Witt, A.: On2broker: Semantic-based Access to Information Sources at the WWW. In: Proceedings of WebNet, pp. 366–371 (1999)Google Scholar
  10. 10.
    Fruewirth, T.: Theory and Practice of Constraint Handling Rules. JLP 37, 95–138 (1998)CrossRefGoogle Scholar
  11. 11.
    Garcia-Molina, H., Papakonstantinou, Y., Quass, D., Rajaraman, A., Sagiv, Y., Ullman, J., Vassalos, V., Widom, J.: The TSIMMIS approach to mediation: Data models and Languages. Journal of Intelligent Information Systems (1997)Google Scholar
  12. 12.
    Kifer, M., Lausen, G., Wu, J.: Logical foundations of object-oriented and frame-based languages. Journal of the ACM 42(4), 741–843 (1995)CrossRefGoogle Scholar
  13. 13.
    Kambhampati, S., Lambrecht, E., Nambiar, U., Nie, Z., Senthil, G.: Optimizing Recursive Information Gathering Plans in EMERAC. Journal of Intelligent Information Systems 22(2), 119–153 (2004)CrossRefGoogle Scholar
  14. 14.
    Kakas, A., Kowalski, R., Toni, F.: The role of abduction in logic programming. In: Handbook of logic in AI and LP 5, OUP, pp. 235–324 (1998)Google Scholar
  15. 15.
    Knoblock, C., et al.: The ARIADNE Approach to Web-Based Information Integration. International Journal of Cooperative Information Systems 10(1-2), 145–169 (2001)CrossRefGoogle Scholar
  16. 16.
    Levy, A.Y.: Logic-Based Techniques. In: Minker, J. (ed.) Data Integration Logic Based Artificial Intelligence. Kluwer, Dordrecht (2000)Google Scholar
  17. 17.
    Levy, A.Y., Rajaraman, A., Ordille, J.J.: Querying Heterogeneous Information Sources Using Source. In: Proc. 22nd VLDB Conference, Bombay, India (1996)Google Scholar
  18. 18.
    Levy, A.Y., Rousset, M.C.: Combining Horn Rules and Description Logics in CARIN. Artificial Intelligence Journal 104 (September 1998)Google Scholar
  19. 19.
    Ludascher, B., Himmeroder, R., Lausen, G., May, W., Schlepphorst, C.: Managing Semistructured Data with FLORID: A Deductive Object-oriented Perspective. Information Systems 23(8), 589–613 (1998)CrossRefGoogle Scholar
  20. 20.
    Marchiori, M., Saarela, J.: Query+Metadata+Logic=Metalog,
  21. 21.
    Subrahmanian, V.S., et al.: HERMES: A heterogeneous reasoning and mediator system,
  22. 22.
    Wiederhold, G.: Mediators in the architecture of future information systems. IEEE Comp. 25(3), 38–49 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Liviu Badea
    • 1
  • Doina Tilivea
    • 1
  • Anca Hotaran
    • 1
  1. 1.AI Lab, National Institute for Research and Development in InformaticsBucharestRomania

Personalised recommendations