Knowledge Modelling for Deductive Web Mining

  • Vojtěch Svátek
  • Martin Labský
  • Miroslav Vacura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3257)


Knowledge-intensive methods that can altogether be characterised as deductive web mining (DWM) already act as supporting technology for building the semantic web. Reusable knowledge-level descriptions may further ease the deployment of DWM tools. We developed a multi-dimensional, ontology-based framework, and a collection of problem-solving methods, which enable to characterise DWM applications at an abstract level. We show that the heterogeneity and unboundedness of the web demands for some modifications of the problem-solving method paradigm used in the context of traditional artificial intelligence.


Knowledge Modelling Class Constraint Image Gallery Direct Retrieval Structural Extraction 
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.


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  1. 1.
  2. 2.
    Abasolo, C., Arcos, J.-L., Armengol, E., Gómez, M., López-Cobo, J.-M., López- Sánchez, M., López de Mantaras, R., Plaza, E., van Aart, C., Wielinga, B.: Libraries for Information Agents. IBROW Deliverable D4, online at
  3. 3.
    Anjewierden, A.: A library of document analysis components, IBrow deliverable D2b, Online at
  4. 4.
    Broekstra, J., Kampman, A., van Harmelen, F.: Sesame: An Architecture for Storing and Querying RDF and RDF Schema. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, p. 54. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Ciravegna, F., Dingli, A., Guthrie, D., Wilks, Y.: Integrating Information to Bootstrap Information Extraction fromWeb Sites. In: IJCAI 2003 Workshop on Intelligent Information Integration (2003)Google Scholar
  6. 6.
    Clancey, W.J.: Heuristic Classification. Artificial Intelligence 27-3, 289–350 (1985)CrossRefGoogle Scholar
  7. 7.
    Crubézy, M., Lu, W., Motta, E., Musen, M.A.: Configuring Configuring Online Problem-Solving Resources with the Internet Reasoning Service. IEEE Intelligent Systems 2, 34–42 (2003)CrossRefGoogle Scholar
  8. 8.
    Dill, S., Eiron, N., Gibson, D., Gruhl, D., Guha, R., Jhingran, A., Kanungo, T., Rajagopalan, S., Tomkins, A., Tomlin, J., Zien, J.: SemTag and Seeker: Bootstrapping the semantic web via automated semantic annotation. In: Proc. WWW 2003, Budapest (2003)Google Scholar
  9. 9.
    Ester, M., Kriegel, H.P., Schubert, M.: Web Site Mining: a new way to spot Competitors, Customers and Suppliers in the World Wide Web. In: Proc. KDD 2002 (2002)Google Scholar
  10. 10.
    Handschuh, S., Staab, S., Ciravegna, F.: S-CREAM – semi-automatic cREAtion of metadata. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, p. 358. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Jin, Y., Decker, S., Wiederhold, G.: OntoWebber: Model-Driven Ontology-Based Web Site Management. In: 1st International Semantic Web Working Symposium (SWWS 2001), Stanford University, Stanford, CA, July 29-Aug 1 (2001)Google Scholar
  12. 12.
    Kodratoff, Y.: Rating the Interest of Rules Induced from Data and within Texts. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 265–269. Springer, Heidelberg (2001)Google Scholar
  13. 13.
    Krátký, M., Pokorný, J., Snášel, V.: Indexing XML Data with UB-trees. In: Manolopoulos, Y., Návrat, P. (eds.) ADBIS 2002. LNCS, vol. 2435. Springer, Heidelberg (2002)Google Scholar
  14. 14.
    Krótzch, S., Rósner, D.: Ontology based Extraction of Company Profiles. In: Workshop DBFusion, Karlsruhe (2002)Google Scholar
  15. 15.
    Labský, M., Svátek, V.: Ontology Merging in Context of Web Analysis. In: Workshop DATESO 2003, TU Ostrava (2003)Google Scholar
  16. 16.
    Motta, E., Lu, W.: A Library of Components for Classification Problem Solving. In: Proceedings of PKAW 2000: The 2000 Pacific Rim Knowledge Acquisition, Workshop, Sydney, Australia, December 11-13 (2000)Google Scholar
  17. 17.
    Schreiber, G., et al.: Knowledge Engineering and Management. The CommonKADS Methodology. MIT Press, Cambridge (1999)Google Scholar
  18. 18.
    Svátek, V., Berka, P., Kavalec, M., Kosek, J., Vávra, V.: Discovering company descriptions on the web by multiway analysis. In: New Trends in Intelligent Information Processing and Web Mining (IIPWM 2003), Zakopane 2003. Advances in Soft Computing series. Springer, Heidelberg (2003)Google Scholar
  19. 19.
    Svátek, V., Kosek, J., Labský, M., Bráza, J., Kavalec, M., Vacura, M., Vávra, V., Snášel, V.: Rainbow - Multiway Semantic Analysis of Websites. In: 2nd International DEXA Workshop on Web Semantics (WebS 2003), Prague 2003. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  20. 20.
    Šváb, O., Svátek, V., Kavalec, M., Labský, M.: Querying the RDF: Small Case Study in the Bicycle Sale Domain. In: Workshop on Databases, Texts, Specifications and Objects (DATESO 2004), online at
  21. 21.
    Tansley, D.S.W., Hayball, C.C.: KBS Analysis and Design. In: A KADS Developer’s Handbook. Prentice-Hall, Englewood Cliffs (1993)Google Scholar
  22. 22.
    Vacura, M.: Recognition of pornographic WWW documents on the Internet (in Czech), PhD Thesis, University of Economics, Prague (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Vojtěch Svátek
    • 1
  • Martin Labský
    • 1
  • Miroslav Vacura
    • 1
  1. 1.Department of Information and Knowledge EngineeringUniversity of EconomicsPraha 3Czech Republic

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