SEAL — A Framework for Developing SEmantic Web PortALs

  • Alexander Maedche
  • Steffen Staab
  • Nenad Stojanovic
  • Rudi Studer
  • York Sure
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2097)


The core idea of the Semantic Web is to make information accessible to human and software agents on a semantic basis. Hence, web sites may feed directly from the Semantic Web exploiting the underlying structures for human and machine access. We have developed a generic approach for developing semantic portals, viz. SEAL (SEmantic portAL), that exploits semantics for providing and accessing information at a portal as well as constructing and maintaining the portal.

In this paper, we discuss the role that semantic structures make for establishing communication between different agents in general. We elaborate on a number of intelligent means that make semantic web sites accessible from the outside, viz. semantics-based browsing, semantic querying and querying with semantic similarity, and machine access to semantic information at a semantic portal. As a case study we refer to the AIFB web site — a place that is increasingly driven by Semantic Web technologies.


Knowledge Base Semantic Similarity Software Agent Lexical Entry Object Match 
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.
    J. Angele, H.-P. Schnurr, S. Staab, and R. Studer. The times they are a-changin’ — the corporate history analyzer. In D. Mahling and U. Reimer, editors, Proceedings of the Third International Conference on Practical Aspects of Knowledge Management. Basel, Switzerland, October 30–31, 2000, 2000.
  2. 2.
    V. Richard Benjamins and Dieter Fensel. Community is knowledge! (KA)2. In Proceedings of the 11th Workshop on Knowledge Acquisition, Modeling, and Management (KAW’ 98), Banff, Canada, April 1998, 1998.Google Scholar
  3. 3.
    S. Chakrabarti, M. van den Berg, and B. Dom. Focused crawling: a new approach to topic-specific web resource discovery. In Proceedings of WWW-8, 1999.Google Scholar
  4. 4.
    W. Dalitz, M. Grötschel, and J. Lügger. Information Services for Mathematics in the Internet (Math-Net). In A. Sydow, editor, Proceedings of the 15th IMACS World Congress on Scientific Computation: Modelling and Applied Mathematics, volume 4 of Artificial Intelligence and Computer Science, pages 773–778. Wissenschaft und Technik Verlag, 1997.Google Scholar
  5. 5.
    S. Decker, M. Erdmann, D. Fensel, and R. Studer. Ontobroker: Ontology Based Access to Distributed and Semi-Structured Information. In R. Meersman et al., editors, Database Semantics: Semantic Issues in Multimedia Systems, pages 351–369. Kluwer Academic Publisher, 1999.Google Scholar
  6. 6.
    D. Fensel, S. Decker, M. Erdmann, and R. Studer. Ontobroker: The Very High Idea. In Proceedings of the 11th International Flairs Conference (FLAIRS-98), Sanibel Island, Florida, May, 1998.Google Scholar
  7. 7.
    J. Heflin and J. Hendler. Searching the web with shoe. In Artificial Intelligence for Web Search. Papers from the AAAI Workshop. WS-00-01, pages 35–40. AAAI Press, 2000.Google Scholar
  8. 8.
    A. Hotho and G. Stumme, editors. Semantic Web Mining — Workshop at ECML-2001 / PKDD-2001, Freiburg, Germany, 2001.Google Scholar
  9. 9.
    E. Hovy. Combining and standardizing large-scale, practical ontologies for machine translation and other uses. In Proc. of the First Int. Conf. on Language Resources and Evaluation (LREC), 1998.Google Scholar
  10. 10.
    M. Kifer, G. Lausen, and J. Wu. Logical Foundations of Object-Oriented and Frame-Based Languages. Journal of the ACM, 42:741–843, 1995.zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    A. Maedche and S. Staab. Discovering conceptual relations from text. In Proceedings of ECAI-2000. IOS Press, Amsterdam, 2000.Google Scholar
  12. 12.
    A. Maedche and S. Staab. Ontology learning for the semantic web. IEEE Intelligent Systems, 16(2), 2001.Google Scholar
  13. 13.
    T.J. Menzis. Knowledge maintenance: The state of the art. The Knowledge Engineering Review, 10(2), 1998.Google Scholar
  14. 14.
    G. Miller. Wordnet: A lexical database for English. CACM, 38(11):39–41, 1995.Google Scholar
  15. 15.
    C.K. Odgen and I.A. Richards. The Meaning of Meaning: A Study of the Influence of Language upon Thought and of the Science of Symbolism. Routledge & Kegan Paul Ltd., London, 10 edition, 1923.Google Scholar
  16. 16.
    R. Rada, H. Mili, E. Bicknell, and M. Blettner. Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics, 19(1), 1989.Google Scholar
  17. 17.
    P. Resnik. Knowledge maintenance: The state of the art. In Proceedings of IJCAI-95, pages 448–453, Montreal, Canada, 1995.Google Scholar
  18. 18.
    R. Richardson, A. F. Smeaton, and J. Murphy. Using wordnet as knowledge base for measuring semantic similarity between words. Technical Report CA-1294, Dublin City University, School of Computer Applications, 1994.Google Scholar
  19. 19.
    S. Staab, J. Angele, S. Decker, M. Erdmann, A. Hotho, A. Maedche, H.-P. Schnurr, R. Studer, and Y. Sure. Semantic community web portals. Proc. of WWW9 / Computer Networks, 33(1-6):473–491, 2000.CrossRefGoogle Scholar
  20. 20.
    S. Staab, M. Erdmann, and A. Maedche. Engineering ontologies using semantic patterns. In A. Preece, editor, Proc. of the IJCAI-01 Workshop on E-Business & the Intelligent Web, 2001.Google Scholar
  21. 21.
    S. Staab and A. Maedche. Knowledge portals — ontologies at work. AI Magazine, 21(2), Summer 2001.Google Scholar
  22. 22.
    S. Staab, A. Maedche, and S. Handschuh. An annotation framework for the semantic web. In Proceedings of the First Workshop on Multimedia Annotation, Tokyo, Japan, January 30–31, 2001, 2001.Google Scholar
  23. 23.
    S. Staab, A. Maedche, and S. Handschuh. Creating metadata for the semantic web: An annotation framework and the human factor. Technical Report 412, Institute AIFB, University of Karlsruhe, 2001.Google Scholar
  24. 24.
    Y. Sure, A. Maedche, and S. Staab. Leveraging corporate skill knowledge–From ProPer to OntoProper. In D. Mahling and U. Reimer, editors, Proceedings of the Third International Conference on Practical Aspects of Knowledge Management. Basel, Switzerland, October 30–31, 2000, 2000.
  25. 25.
    S. Weibel, J. Kunze, C. Lagoze, and M. Wolf. Dublin Core Metadata for Resource Discovery. Number 2413 in IETF. The Internet Society, September 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Alexander Maedche
    • 1
    • 3
  • Steffen Staab
    • 1
    • 2
  • Nenad Stojanovic
    • 1
  • Rudi Studer
    • 1
    • 2
    • 3
  • York Sure
    • 1
    • 2
  1. 1.Institute AIFBUniversity of KarlsruheKarlsruheGermany
  2. 2.Ontoprise GmbHKarlsruheGermany
  3. 3.FZI Research Center for Information TechnologiesKarlsruheGermany

Personalised recommendations