The Use of Machine-Generated Ontologies in Dynamic Information Seeking

  • Giovanni Modica
  • Avigdor Gal
  • Hasan M. Jamil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2172)


Information seeking is the process in which human beings recourse to information resources in order to increase their level of knowledge with respect to their goals. In this paper we offer a methodology for automating the evolution of ontologies and share the results of our experiments in supporting a user in seeking information using interactive systems. The main conclusion of our experiments is that if one narrows down the scope of the domain, ontologies can be extracted with a very high level of precision (more than 90% in some cases). The paper is a step in providing theoretical, as well as practical, foundation for automatic ontology generation. It is our belief that such a process would allow the creation of flexible tools to manage metadata, either as an aid to a designer or as an independent system (“smart agent”) for time critical missions.


Information Seek Semistructured Data Ontology Module Navigation Module Target Ontology 
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.
    Concise Oxford Dictionary. Oxford Univ. Press, 8 edition, 1991. 438Google Scholar
  2. 2.
    S. Abiteboul, D. Quass, J. McHugh, J. Widom, and J. L. Wiener. The LOREL query language for semistructured data. International Journal on Digital Libraries, 1(1), 1997. 435Google Scholar
  3. 3.
    J. Aitchison, A. Gilchrist, and D. Bawden. Thesaurus construction and use: A practical manual. Aslib, London, third edition, 1997. 435Google Scholar
  4. 4.
    Y. Arens, C. A. Knoblock, and W. Shen. Query reformulation for dynamic information integration. In G. Wiederhold, editor, Intelligent Integration of Information, pages 11–42. Kluwer Academic Publishers, 1996. 435Google Scholar
  5. 5.
    A. Borgida. Knowledge representation, semantic data modelling: What’s the difference? In Proceedings of the 9th International Conference on Entity-Relationship Approach (ER’90), pages 1–2, Lausanne, Switzerland, 1990. 437Google Scholar
  6. 6.
    M. Bunge. Treatise on Basic Philosophy: Vol. 3: Ontology I: The Furniture of the World. D. Reidel Publishing Co., Inc., New York, NY, 1977. 437zbMATHGoogle Scholar
  7. 7.
    M. Bunge. Treatise on Basic Philosophy: Vol. 4: Ontology II: A World of Systems. D. Reidel Publishing Co., Inc., New York, NY, 1979. 437zbMATHGoogle Scholar
  8. 8.
    M. J. Carey et al. Towards heterogeneous multimedia information systems: The Garlic approach. In Proceedings of the RIDE-DOM workshop, pages 124–131, 1995. 435Google Scholar
  9. 9.
    S. Castano, V. De Antonellis, M. G. Fugini, and B. Pernici. Conceptual schema analysis: Techniques and applications. ACM Transactions on Database Systems (TODS), 23(3):286–332, 1998. 436CrossRefGoogle Scholar
  10. 10.
    C. Fox. Lexical analysis and stoplists. In W. B. Frakes and R. Baeza-Yates, editors, Information Retrieval: Data Structures & Algorithms, pages 102–130. Prentice Hall, Englewood Cliffs, NJ 07632, 1992. 442Google Scholar
  11. 11.
    W. B. Frakes and R. Baeza-Yates, editors. Information Retrieval: Data Structures & Algorithms. Prentice Hall, Englewood Cliffs, NJ 07632, 1992. 439Google Scholar
  12. 12.
    W. Francis and H. Kucera, editors. Frequency Analysis of English Usage. Houghton Mifflin, New York, 1982. 442Google Scholar
  13. 13.
    A. Gal. Semantic interoperability in information services: Experiencing with Coop-WARE. SIGMOD Record, 28(1):68–75, 1999. 435CrossRefMathSciNetGoogle Scholar
  14. 14.
    J. Kahng and D. McLeod. Dynamic classification ontologies for discovery in cooperative federated databases. In Proceedings of the First IFCIS International Conference on Cooperative Information Systems (CoopIS’96), pages 26–35, Brussels, Belgium, June 1996. 435Google Scholar
  15. 15.
    T. D. Millstein, A. Y. Levy, and M. Friedman. Query containment for data integration systems. In Proceedings of the Nineteenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Dallas, Texas, May 2000. ACM Press. 435Google Scholar
  16. 16.
    A. Moulton, S. E. Madnick, and M. Siegel. Context mediation on Wall Street. In Proceedings of the 3rd IFCIS International Conference on Cooperative Information Systems (CoopIS’98), pages 271–279, New York City, New York, August 1998. IEEE-CS Press. 435Google Scholar
  17. 17.
    S. Nestorov, S. Abiteboul, and R. Motwani. Extracting schema from semistructured data. In L. M. Haas and A. Tiwary, editors, SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, June 2-4, 1998, Seattle, Washington, USA, pages 295–306. ACM Press, 1998. 436Google Scholar
  18. 18.
    A. M. Ouksel and C. F. Naiman. Coordinating context building in heterogeneous information systems. Journal of Intelligent Information Systems (JIIS), 3(2):151–183, April 1994. 435CrossRefGoogle Scholar
  19. 19.
    A. M. Ouksel and A. P. Sheth. Semantic interoperability in global information systems: A brief introduction to the research area and the special section. SIGMOD Record, 28(1):5–12, March 1999. 436CrossRefGoogle Scholar
  20. 20.
    C. J. Van Rijsbergen, editor. Information Retrieval. Butterworths, London, 1979. 439Google Scholar
  21. 21.
    G. Salton and M. McGill. Modern Information Retrieval. McGraw-Hill, New York, 1983. 433zbMATHGoogle Scholar
  22. 22.
    P. L. Schuyler, W. T. Hole, and M. S. Tuttle. The UMLS (Unified Medical Language System) metathesaurus: representing different views of biomedical concepts. Bulletin of the Medical Library Association, 81:217–222, 1993. 435Google Scholar
  23. 23.
    A. P. Sheth, S. K. Gala, and S. B. Navathe. On automatic reasoning for schema integration. Intenational Journal on Intelligent Cooperative Information Systems (IJICIS), 2(1):23–50, June 1993. 436CrossRefGoogle Scholar
  24. 24.
    P. Simon. Parts: A Study in Ontology. Clarendon Press, New York, NY, 1987. 438Google Scholar
  25. 25.
    H. Smith and K. Poulter. Share the ontology in XML-based trading architectures. Communications of the ACM, 42(3):110–111, 1999. 433CrossRefGoogle Scholar
  26. 26.
    D. Soergel. Organizing information: principles of data base and retrieval systems. Academic Press, Orlando, FA, 1985. 435Google Scholar
  27. 27.
    A. Varzi. On the boundary between mereology and topology. In R. Casati, B. Smith, and G. White, editors, Philosophy and the Cognitive Sciences. Hoelder-Pichler-Tempsky, Vienna, Austria, 1994. 438Google Scholar
  28. 28.
    B. C. Vickery. Faceted classification schemes. Graduate School of Library Service, Rutgers, the State University, New Brunswick, N. J., 1966. 435Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Giovanni Modica
    • 1
  • Avigdor Gal
    • 2
    • 3
  • Hasan M. Jamil
    • 1
  1. 1.Mississippi State University, Mississippi State UniversityUSA
  2. 2.Technion, Israel Institute of TechnologyTechnion City, HaifaIsrael
  3. 3.Rutgers UniversityPiscatawayUSA

Personalised recommendations