Top > JACIII > Capturing Evolutional Knowledge Using Time Interval Tracing

single-jc.php

JACIII Vol.11 No.4 pp. 373-380
doi: 10.20965/jaciii.2007.p0373
(2007)

Paper:

Views over last 60 days: 646

Capturing Evolutional Knowledge Using Time Interval Tracing

Shun-Chieh Lin*, Chia-Wen Teng*, and Shian-Shyong Tseng*,**

*Department of Computer Science, National Chiao Tung University, 1001, Ta-hsueh Rd., Hsinchu, Taiwan

**Department of Info. Sci. & App., Asia University, 500, Liu-Feng Rd., Wufeng, Taichung, Taiwan

Received:
April 4, 2006
Accepted:
August 8, 2006
Published:
April 20, 2007
Creative Commons License
Keywords:
expert system, knowledge acquisition, EMCUD, VODKA, grid evolution
Abstract
Knowledge acquisition is a critical bottleneck in building a knowledge-based system. Much research and many tools have been developed to acquire domain knowledge with embedded rules that may be ignored in constructing the initial prototype. Due to different backgrounds and dynamic knowledge changing over time, domain knowledge constructed at one time may be degraded at any time thereafter. Here, we propose knowledge acquisition, called enhanced embedded meaning capturing under uncertainty deciding (enhanced EMCUD), which constructs a domain ontology and traces information over time to efficiently update time-related domain knowledge based on the current environment. We enrich the knowledge base and ease the construction of domain knowledge that changes with times and the environment.
Cite this article as:
S. Lin, C. Teng, and S. Tseng, “Capturing Evolutional Knowledge Using Time Interval Tracing,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.4, pp. 373-380, 2007.
Data files:
References
  1. [1] J. Adams, “Probabilistic and certainty factors,” In: B. Buchanan and E. Shortliffe (Eds.), Rule-Base Expert Systems: The MYCIN Experiments of the Standford Heuristic Programming Project. Reading, MA: Addison-Wesley, 1985.
  2. [2] J. H. Boose and J. M. Bradshaw, “Expertise transfer and complex problems: using AQUINAS as a knowledge-acquisition workbench for knowledge-based systems,” International Journal of Man Machine Studies, Vol.26, pp. 29-40, 1987.
  3. [3] M. Boicu, “Automatic Knowledge Acquisition from Subject Matter Expert,” IEEE International Conference on Tool with Artificial Intelligent, 2001.
  4. [4] O. Cairo, “KAMET: A comprehensive methodology for knowledge acquisition from multiple knowledge sources,” Expert Systems with Applications, Vol.14, No.1, pp. 1-16, 1998.
  5. [5] J. J. Castro-Schez, N. R. Jennings, X. D. Luo, and N. R. Shadbolt, “Acquiring domain knowledge for negotiating agents: a case of study,” International Journal of Human-Computer Studies, Vol.61, No.1, pp. 3-31, 2004.
  6. [6] P. Crowther and J. Hartnett, “Using repertory grids for knowledge acquisition for spatial expert system,” Processing on Intelligent Information System, November 1996.
  7. [7] G. J. Hwang and S. S. Tseng, “EMCUD: A knowledge acquisition method which captures embedded meanings under uncertainty,” International Journal of Man-Machine Studies, Vol.33, No.4, pp. 431-451, 1990.
  8. [8] G. A. Kelly, “The psychology of personal construct,” New York: Norton, 1955.
  9. [9] H. Leitich, H. P. Kiener, G. Kolarz, C. Schuh, W. Graninger, and K. P. Adlassnig, “A prospective evaluation of the medical consultation system CADIAG-II/RHEUMA in a rheumatological outpatient clinic,” Methods Inform Med, Vol.40, pp. 213-220, 2001.
  10. [10] M. L. G. Shaw and B. R. Gaines, “Web Grid: Knowledge Modeling and Inference through the World Wide Web,” Proc. of tenth knowledge acquisition workshop, pp. 65-1-65-14, 1996.
  11. [11] S. S. Tseng and S. C. Lin, “VODKA: Variant objects discovering knowledge acquisition,” revised to International Journal of Human-Computer Studies, 2006.
  12. [12] C. Wanger, “Breaking the knowledge acquisition bottleneck through conversational knowledge management,” Information Resources Management Journal, Vol.19, No.1, 2006.
  13. [13] K. Wang, G. Cretu, and S. J. Stolfo, “Anomalous payload-based worm detection and signature generation,” International Symposium on Recent Advance In Intrusion Detection, 2005.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 05, 2024