Paper title: Modeling Techniques for Knowledge Representation of Expert System: A Survey
Published in: Issue 2, (Vol. 13) / 2019Download
Publishing date: 2019-12-16
Pages: 39-44
Author(s): MUHAMMAD L. J., GARBA E. J., OYE N. D., WAJIGA G. M.
Abstract. Knowledge representation is one of the most desirable things to make system intelligent. Every expert system may only be an intelligent if its intelligence is equivalent to the intelligence of human being for a particular domain. In expert system development, a good solution depends on a good knowledge representation modeling technique chosen. So, failure to choose appropriate technique can be a major problem in the later stages of an expert system development, because some critical information cannot be encoded within the chosen technique. This study reviewed some various modeling techniques for knowledge presentation of expert system, identified and discussed the pros and cons of each technique
Keywords: Artificial Intelligence, Expert System, Knowledge Base, Knowledge Transfer, Knowledge Inference

1. A. Fayyad, U.,Piatesky-Shapiro, G., Smyth, P., and Uthurusamy, R. (Eds.), Advances in Knowledge Discovery and Data Mining, Cambridge, 1996.

2. A. Goodall. Expert systems (Computer science). Learned Information, University of California, 1985,

3. C. Enrico, C. The Guide to Health Informatics, London, 3rd Edition, 2003.

4. E. C. Okafor and C. C. Osuagwu (2007), Issues in Structuring the Knowledge-base of Expert Systems, The Electronic Journal of Knowledge Management,Vol. 5 (3), pp. 313 – 322.

5. E. Turban and E. Jay. Decision Support Systems and Expert Systems”. 6th Edition, Prentice Hall, Upper Saddle River, NJ, 2002.

6. G. Hwang. Knowledge elicitation and integration from multiple experts (1194). Journal Information Science and Engineering, Vol. 10, pp. 99-109

7. K. C. Laudon, and J. P. Laudon. Essential of management information systems” (5th ed), Englewood cliffs, NJ: Prentice Hall, 2002.

8. K. L. McGraw & K. Harbison-Briggs, Knowledge acquisition: Principles and guidelines,NJ: Prentice-Hall, 1989.

9. L. J. Muhammad, E. J. Garba, N. D. Oye and G. M. Wajiga (2018), On the Problems of Knowledge Acquisition and Representation of Expert System for Diagnosis of Coronary Artery Disease (CAD), International Journal of u- and e- Service, Science and Technology, Vol. 11, No. 3, pp. 49-58

10. L. J. Muhammad (2019). Fuzzy Rule Driven Data Mining Framework to Knowledge Acquisition for Expert System, Modibbo Adama University of Technology, Yola, Unpublished PhD, thesis.

11. M. B. Wiga, A. S. Noor and A. Igi. Rule Extraction for Fuzzy Expert System to Diagnose Coronary Artery Disease, (Published Conference Proceedings style) in Proc of International Conference on Information Technology, Yogyakarta, Indonesia 2016, 1(1),136-141

12. M. R. Quillian. Semantic memory, In Semantic information processing”, ed., by M.Minsky, MIT Press, Cambridge, Mass, 1986, 216-270.

13. M. Minsky. Framework for representing knowledge, in the psychology of computer vision, ed., P. Winston, McGraw-Hill NY, pp. 211-277, 1975.

14. O. O. Oladipupo A fuzzy association rule mining expert-driven approach to knowledge acquisition. Ph.D. Thesis, Covenant University, 2012.

15. S. Kaisler (1986). Expert systems: An overview, IEEE Journal of Oceanic Engineering, Vol. OE-11(4).

16. S. Chen, A knowledge acquisition scheme for ruled-based systems (Published Conference Proceedings style) in Proc. IEEE Region Conference on Computer, Communication, Control and Power Engineering, 1993, Vol. 2, pp. 621-625.

17. S. Mehdi. Expert Systems Development: Some Problems, Motives and Issues in an Exploratory Study”, Dissertation submitted to Department of Informatics, Lund University, Lund/Sweden, 1993.

18. Y. I. Liou. Expert system technology: knowledge acquisition. Handbook of Applied Expert Systems, New York: CRC Press. 2.1 -2.11, 1999.

19. Y. D. Niranjana and S. Anto (2014). An Evolutionary-Fuzzy Expert System for the Diagnosis of Coronary Artery Disease” International Journal of Advanced Research in Compute Engineering & Technology Vol. 3 (4).

20. V. Marik, e. a. (2003). Umela Inteligence 4. ACADEMIA.

21. M. Petrik , Knowledge representation for expert systems, Published Conference Proceedings style) in Proc . at International Conference for Undergraduate and Graduate Students of Applied Mathematics 2004

22. P. N. Stuart Russel. Artificial Intelligence, Modern Approach, 2003.

23 R. Girratano. Expert Systems, Priciples and Programming. PWS, 1998.

24. R. Lopez de Mantaras (2001). Case-Based Reasoning, Machine Learning and Its Applications

25. M. Ensing, R. Paton, P. Speela, R. Radab (2004). An object-oriented approach to knowledge representation in a biomedical domain Artificial Intelligence in Medicine, 6: 6, pp. 459-482

26. M. A. Ali (2017). Fuzzy expert system for Coronary Artery Disease diagnosis in Jordan, Heath Technology, Springer

27. P. Tanwar, T. V. Prasad, K. Datta (2013) Hybrid Technique for Effective Knowledge Representation. In: Meghanathan N., Nagamalai D., Chaki N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol. 178. Springer, Berlin, Heidelberg

28. M. Malhotra and T. R. Gopalakrishnan Nai (2015).. Evolution of Knowledge Representation and Retrieval Techniques, International Journal of Intelligent Systems and Applications, Vol. 07, pp. 18-28

29. F. Hayes-Roth, and N. Jacobstein, (1994). The state of knowledge-based systems," Communications of the ACM, Vol. 37 No. 3, pp. 26-39

30. S. Russell, and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed., Prentice Hall, 2009.

31. L. A. Zadeh (1992) Knowledge Representation in Fuzzy Logic. In: Yager R.R., Zadeh L.A. (eds) An Introduction to Fuzzy Logic Applications in Intelligent Systems. The Springer International Series in Engineering and Computer Science (Knowledge Representation, Learning and Expert Systems), vol 165. Springer, Boston, MA

32. H. J. Levesque, and R. Brachman (1987). Expressiveness and tractability in knowledge representation and reasoning, Computational Intelligence, pp. 378–93

33. R. I. Brachman, and H. J. Levesque, Readings in Knowledge Representation Morgan Kaufmann Publishers, Inc., Los Altos, Calif., 1985

Back to the journal content
Creative Commons License
This article is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License.
Home | Editorial Board | Author info | Archive | Contact
Copyright JACSM 2007-2020