Knowledge Engineering and Traditional Information System

Abstract

The discipline of knowledge engineering grew out of the early work on expert systems in the seventies. The purpose is not necessarily to develop systems that replace humans, but to allow the use of systems that increase human effectiveness and efficiency. Another issue that frequently comes up in discussions about problem-solving methods is their correspondence with human reasoning. Invocation of the propose task produces one new parameter assignment, the smallest possible extension of an existing design. Domain-specific, search-control knowledge guides the order of parameter selection, based on the components they belong to. Ontology is specified describing the categories of the domain knowledge and the relationships between these categories. Knowledge roles link the components of the method to elements of the application domain; ontologies provide guidelines for building domain conceptualizations, such as the construction of subsumption hierarchies. The traditional methodologies such as System Development Life Cycle (SDLC), Object oriented analysis and design (OOAD), Waterfall and Rapid Application Development (RAD) have struggled to accommodate the web-specific aspects into their method and work practices. Most of the websites are based on graphical hypermedia systems, database-driven information system, and also security systems.

Country : Nigeria

1 Dr. Oye N. D.2 Jemimah Nathaniel

  1. Department of Computer Science, MAUTECH- Yola, Nigeria
  2. Postgraduate Student of Computer Science (MSc), University of Jos, Plateau State, Nigeria

IRJIET, Volume 4, Issue 2, February 2020 pp. 13-23

References

  1. Allen J., (2016). Maintaining knowledge about temporal intervals. Communications of the ACM, 26:832–843.
  2. Artale E., Franconi A., & Pazzi L. (2017). Part-whole relations in object-centered systems: An overview. Data and Knowledge Engineering, 20 (347–383).
  3. Avison, D. & Fitzgerald, G. (2006). Information Systems Development: Methodologies, Techniques, and Tools. Fourth Edition. UK: McGraw Hill Education.
  4. Boose. A. (2017). Survey of knowledge acquisition techniques and tools. Knowledge Acquisition, 1(1):3–37.
  5. Brachman R. J.  & Levesque H. J. (2018). The tractability of sub sumption in frame-based description languages. In AAAI 84.
  6. Brachman R. J & Schmolze J. G. (2017). An overview of the KL-ONE knowledge representation system. Cognitive Science, 9: 171–216.
  7. Britton, C. & Doake, J. (2003). Software System Development. Third Edition. USA: McGraw-Hill Education.
  8. Chandrasekaran B. (2012). Generic tasks in knowledge based reasoning: High level building blocks for expert system design. IEEE Expert, 1(3):23–30.
  9. Chandrasekaran B. & Hamid (2010). Design problem solving: A task analysis. AI Magazine, 11:59–71.
  10. Clancey W. J. (2017). The epistemology of a rule based system -a framework for explanation. Artificial Intelligence, 20:215–251.
  11. Davis R., Shrobe H., & Szolovits P. (2011). What is a knowledge representation? AI 16 1, Knowledge Engineering Magazine, Spring:17–33.
  12. Dix et al,(2003) “Human Computer Interaction” 2nd Edition, Prentice Hall.
  13. Fitzgerald B. (1997). Time to turn update the clock, in Wojtkowski G, Wojtkowski W, Wrycza S and Zupancic J (eds.) Systems Development Methods for the Next Century, Plenum Press, New York.
  14. Gamma E., Helm R., Johnson R., & Vlissides J. (1995). Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley, Reading, MA.
  15. Gruber T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5:199–220.
  16. Gangemi A. (2005). Ontology design patterns for semantic web content. In International Semantic Web Conference ISWC’05, Galway, Ireland, LNCS, pages 262–276. Springer-Verlag.
  17. Klein, F. J. (2008). The Waterfall Model of Software Development [Online].
  18. Marcus S., editor (1988). Automatic knowledge acquisition for expert systems. Kluwer, Boston.
  19. McDermott J.  (2012). Preliminary steps towards a taxonomy of problem-solving methods. In S. Marcus, editor, Automating Knowledge Acquisition for Expert Systems, pages 225–255. Kluwer, Boston.
  20. Motta  E., Stutt A., Zdrahal Z., O’Hara K., & Shadbolt N. R. (1996). Solving VT in VITAL: a study in model construction and reuse. Int. J. Human-Computer Studies, 44(3/4):333–372.
  21. Powell, T A. (1998), Web Site Engineering. New Jersey. Prentice Hall.
  22. Preece et al (2002). “Interaction Design - Beyond Human-Computer Interaction” Wiley.
  23. Puerta A. R., Egar J., Tu S., & Musen M. (1992). A multiple-method shell for the automatic generation of knowledge acquisition tools. Knowledge Acquisition, 4:171–196.
  24. Richard Vidgen (2008), “Constructing a web information system development methodology” P 247-261, Blackwell Science Ltd.
  25. RomiSatriaWahono. (2000). Object-Oriented Analysis and Design Methodology. [Online].
  26. Schreiber A. Th., Akkermans J. M., Anjewierden A. A., de Hoog R., Shadbolt N. R., Van de Velde W., and Wielinga B. J. (2004). Knowledge Engineering and Management: The Common KADS Methodology. MIT Press, Cambridge, MA, December.
  27. Schreiber A. Th & Birmingham W. P. (1996). The Sisyphus-VT initiative. Int. J. Human-Computer Studies, 43(3/4):275–280. Editorial special issue.
  28. Steels L. (1990). Components of expertise. AI Magazine, summer.
  29. Stefik M.  (2012). Introduction to Knowledge Systems. Los Altos, CA. Morgan Kaufmann.
  30. Vidgen, R., Avison, D., Wood, B. & Wood-Harper, T. (2002). Developing Web Information Systems. Oxford: Butterworth Heinemann.
  31. Van Harmelen F. & Aben M. (1996). Structure preserving specification languages for knowledge-based systems. International Journal of Human Computer Studies, 44:187–212.
  32. Van Harmelen F. & Balder J. R. 1993). (ML)2: a formal language for KADS models of expertise. Knowledge Acquisition, 4(1). Special issue: ‘The KADS approach to knowledge engineering’, reprinted in KADS: A Principled Approach to Knowledge-Based System Development, Schreiber, A. Th. et al. (eds.).
  33. Valente A.  & L¨ockenhoff C. (1993). Organization as guidance: A library of assessment models. In Proceedings of the Seventh European Knowledge Acquisition Workshop (EKAW’93), pages 243–262.
  34. Weaver, P. (2004). Success in Your Project: A Guide to Student System Development Projects. USA: Prentice Hall.
  35. Wielinga B. J. & Breuker J. A. (1992). Models of expertise. In Proceedings ECAI–86, 18 1. Knowledge Engineering.
  36. Wielinga B. J. & Breuker J. A. (1984). Interpretation of verbal data for knowledge acquisition. In T. O’Shea, editor, Advances in Artificial Intelligence, pages 41–50, Amsterdam, The Netherlands,. ECAI, Elsevier Science. Also as: Report 1.4, ESPRIT Project 12, University of Amsterdam.
  37. Yang et al., (2002). Heuristic classification. Artificial Intelligence, 27:289–350.