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Domain Analysis and Queries in Context

  • Alfs T. Berztiss
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1959)

Abstract

We are formulating design guidelines for a knowledge system that is to provide answers to natural language queries in context. A query that starts out being very vague is to be sharpened with the assistance of the system. Also, the response to a query is more meaningful when presented in context. We recognize three types of context: essential, reference, and source. Essential context associates the response to a query with a time and place. Reference context provides reference values that help the user determine whether the response to a fuzzy query is true or false. Source context relates to the dependability of the response.

Keywords

IEEE Transaction Domain Analysis Data Engineer Concept Space Attribute Grammar 
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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References

  1. 1.
    Cohen, P., Schrag, R., Jones, E., Pease, A., Lin, A., Starr, B., Gunning, D., and Burke, M., The DARPA High Performance Knowledge Bases project. AI Magazine 18:4 (Fall 1998), 25–49.Google Scholar
  2. 2.
    Wartik, S. and Prieto-Diaz, R., Criteria for comparing reuse-oriented domain analysis approaches. International Journal of Software Engineering and Knowledge Engineering 2 (1992), 403–431.CrossRefGoogle Scholar
  3. 3.
    Rolling, W.A., A preliminary annotated bibliography on domain engineering. ACM SIGSOFT Software Engineering Notes 19:3 (1994), 82–84.CrossRefGoogle Scholar
  4. 4.
    Glass, R.L. and Vessey, I., Contemporary application-domain taxonomies. IEEE Software 12:4 (1995), 63–76.CrossRefGoogle Scholar
  5. 5.
    Klir, G.J., Developments in uncertainty-based information. Advances in Computers 36 (1993), 255–332.Google Scholar
  6. 6.
    Parsons, S., Current approaches to handling imperfect information in data and knowledge bases. IEEE Transactions on Knowledge and Data Engineering 8 (1996), 353–372.CrossRefGoogle Scholar
  7. 7.
    Balzer, R., Tolerating inconsistency. In Proceedings of the 13th International Conference on Software Engineering, 158–165. IEEE CS Press, 1991.Google Scholar
  8. 8.
    Finkelstein, A.C.W., Gabbay, D., Hunter, A., Kramer, J. and Nuseibeh, B., Inconsistency handling in multiperspective requirements. IEEE Transactions on Software Engineering 20 (1994), 569–578.CrossRefGoogle Scholar
  9. 9.
    Liu, X.F. and Yen, J., An analytic framework for specifying and analyzing imprecise requirements. In Proceedings of the 18th International Conference on Software Engineering, 60–69. IEEE CS Press, 1996.Google Scholar
  10. 10.
    Burg, J.F.M. and van de Riet, R.P., Analyzing informal requirements specifications: a first step towards conceptual modeling. In Applications of Natural Language to Information Systems, 15–27. IOS Press, 1996.Google Scholar
  11. 11.
    Kao, M., Cercone, N., and Luk, W.-S., Providing quality responses with natural language interfaces: the null value problem. IEEE Transactions on Software Engineering 14 (1988), 959–984.CrossRefGoogle Scholar
  12. 12.
    Liu, K.-C. and Sunderraman, R., On representing indefinite and maybe information in relational databases. In Proceedings of the Fourth International Conference on Data Engineering. IEEE CS Press, 1988.Google Scholar
  13. 13.
    Liu, K.-C. and Sunderraman, R., Indefinite and maybe information in relational databases. ACM Transactions on Database Systems 15 (1990), 1–39.CrossRefMathSciNetGoogle Scholar
  14. 14.
    Kong, Q. and Chen, G., On deductive databases with incomplete information. ACM Transactions on Information Systems 13 (1995), 354–369.CrossRefGoogle Scholar
  15. 15.
    Ichikawa, T. and Hirakawa, M., ARES: a relational database with the capability of performing flexible interpretation of queries. IEEE Transactions on Software Engineering SE-12 (1986), 624–634.Google Scholar
  16. 16.
    Motro, A., FLEX: a tolerant and cooperative user interface to databases. IEEE Transactions on Knowledge and Data Engineering 2 (1990), 231–246.CrossRefGoogle Scholar
  17. 17.
    Pirotte, A., Roelants, D., and Zimanyi, E., Controlled generation of intensional queries. IEEE Transactions on Knowledge and Data Engineering 3 (1991), 221–236.CrossRefGoogle Scholar
  18. 18.
    Motro, A., Intensional answers to database queries. IEEE Transactions on Knowledge and Data Engineering 6 (1994), 444–454.CrossRefGoogle Scholar
  19. 19.
    Yager, R.R., Using approximate reasoning to represent default knowledge. Artificial Intelligence 31 (1987), 99–112.zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Kalagnanam, J., Simon, H.A., and Iwasaki, Y., The mathematical bases for qualitative reasoning. IEEE Expert 6:2 (1991), 11–19.CrossRefGoogle Scholar
  21. 21.
    Klir, G.J. and Yuan, B., Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, 1995.Google Scholar
  22. 22.
    Bosc, P. and Pivert, O., SQLf: a relational database language for fuzzy querying. IEEE Transactions on Fuzzy Systems 3 (1995), 1–17.CrossRefGoogle Scholar
  23. 23.
    Han, J., Huang, Y., Cercone, N., and Fu, Y., Intelligent query answering by knowledge discovery techniques. IEEE Transactions on Knowledge and Data Engineering 8 (1996), 373–390.CrossRefGoogle Scholar
  24. 24.
    Berztiss, A.T., Imprecise queries and the quality of conceptual models. In Information Modelling and Knowledge Bases V, 174–185. IOS Press, 1994.Google Scholar
  25. 25.
    Berztiss, A.T., The query language Vizla. IEEE Transactions on Knowledge and Data Engineering 5 (1993) 813–825.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Alfs T. Berztiss
    • 1
    • 2
  1. 1.University of PittsburghUSA
  2. 2.SYSLABUniversity of StockholmSweden

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