Advertisement

Integrating Requirements Specifications and Web Services Using Cognitive Models1

  • Joe Geldart
  • William Song
Chapter

Abstract

Modern telecommunication providers are under increasing competition in their markets due to deregulation and merging of previously separate domains. In order to maintain a competitive edge, providers are looking to allow an increase in the flexibility of their offerings without substantially increasing costs. This position chapter sets out a scheme for allowing the automation of the processing of customer requirements to give an executable service specification. This scheme involves the recognition of requirements as cognitive artifacts and the containment of the uncertain reasoning; this entails to a single level in a three-tier processing model.

Keywords

Abstract Domain Conceptual Metaphor Cognitive Artifact Concrete Domain Cognitive Account 
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.

References

  1. Bansal, S. and Vidal, J. (2003) Matchmaking of Web-Services Based on the DAML-S Service Model. In: Proceedings of the AAMAS Workshop on Web Services and Agent Based Engineering.Google Scholar
  2. Broy, M., Meisinger, M. and Kr ü ger, I. (2007) A Formal Model of Services. ACM Transactions on Software Engineering and Methodology, 16(1).Google Scholar
  3. Ebert, C. (1997) Dealing With Non-Functional Requirements in Large Software Systems. Annals of Software Engineering, 3, 367–395.CrossRefGoogle Scholar
  4. Englmeier, K., Pereira, J. and Mothe, J. (2006) Choreography of Web-Services Based On Natural Language Storybooks. In: Proceedings of the 8th International Conference on Electronic Commerce: The New E-commerce: Innovations for Conquering Current Barriers, Obstacles and Limitations to Conducting Successful Business on the Internet.Google Scholar
  5. Gillet, P., Scherl, R. and Shafer, G. (2007) A Probabilistic Logic Based on the Acceptability of Gambles. International Journal of Approximate Reasoning, 44, 281–300.MathSciNetCrossRefGoogle Scholar
  6. Giuglea, A.-M. and Moschitti, A. (2006) Semantic Role Labelling Via FrameNet, VerbNet and PropBank. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the ACL, pp. 929–936.Google Scholar
  7. Hayzelden, A. and Bigham, J. (1999) Agent Technology in Communications Systems: An Overview. Knowledge Engineering Review Journal, 14(3), 1–35.Google Scholar
  8. Kohonen, T. (2001) Self Organizing Maps, 3rd ed. Springer, Berlin.MATHGoogle Scholar
  9. Lakoff, G. (1987) Women, Fire and Dangerous Things: What Categories Reveal About the Human Mind. University of Chicago Press, Chicago, IL.Google Scholar
  10. Lakoff, G. and Johnson, M. (1981) Metaphors We Live By. University of Chicago Press, Chicago, IL.Google Scholar
  11. Nguyen, H., Kreinovich, V. and Longpre, L. (2001) Second-order Uncertainty as a Bridge Between Probabilistic and Fuzzy Approaches. In: Proceedings of the 2nd Conference of the European Society for Fuzzy Logic and Technology EUSFLAT'01, England, pp. 410–413.Google Scholar
  12. Rasmussen, T. (2001) Labelled Natural Deduction for Interval Logics. In: CSL'01, 2142 of LNCS. Springer, Berlin, pp. 308–323.Google Scholar
  13. Rosch, E. (1973) Natural Categories. Cognitive Psychology, 4, 328–350.CrossRefGoogle Scholar
  14. Rosch, E. (1975) Cognitive Representation of Semantic Categories. Journal of Experimental Psychology, 104, 573–605.Google Scholar
  15. Sheshagiri, M., des Jardins, M. and Finin, T. (2003) A Planner for Composing Services Described in DAML-S. In: Proceedings of the AAMAS Workshop on Web Services and Agent Based Engineering.Google Scholar
  16. Simonov, M., Gangemi, A. and Soroldoni, M. (2004) Ontology-Driven Natural Language Access to Legacy and Web Services in the Insurance Domain. In: BIS 2004: Proceedings of the 7th Business Information Systems Conference.Google Scholar
  17. Sommerville, I. (2001) Software Engineering, 6th ed. Addison-Wesley, Reading, MA.Google Scholar
  18. Straccia, U. (2005) A Fuzzy Description Logic for the Semantic Web. In: Sanchez, E., ed., Capturing Intelligence: Fuzzy Logic and the Semantic Web. Elsevier, Amsterdam.Google Scholar
  19. Sun, R. (1992) Fuzzy Evidential Logic: A Model of Causality for Common-sense Reasoning. In: Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society.Google Scholar
  20. Turner, M. (1996) The Literary Mind: The Origins of Thought and Language. Oxford University Press, Oxford.Google Scholar
  21. Wang, P. (2006) Rigid Flexibility: The Logic of Intelligence. Springer, Berlin.MATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Joe Geldart
  • William Song
    • 1
  1. 1.Department of Computer ScienceUniversity of DurhamUK

Personalised recommendations