Using cultural algorithms to re-engineer large-scale semantic networks

International Journal of Software Engineering and Knowledge Engineering

Volumn 15, Issue 4, 2005, Pages 665-693

  Rychtyckyj, Nestor ^a   Reynolds, Robert G ^b  

^a FORD MOTOR COMPANY   (United States)

^b Wayne State University   (United States)

Author keywords

Cultural algorithms; Evolutionary computation; Knowledge maintenance; Knowledge base re engineering; Semantic networks; Subsumption; Top down re engineering

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER NETWORKS; EVOLUTIONARY ALGORITHMS; KNOWLEDGE BASED SYSTEMS; LARGE SCALE SYSTEMS; REENGINEERING;

CULTURAL ALGORITHMS; KNOWLEDGE MAINTENANCE; KNOWLEDGE-BASE RE-ENGINEERING; SEMANTIC NETWORKS; SUBSUMPTION; TOP-DOWN RE-ENGINEERING;

SEMANTICS;

EID: 24644512791     PISSN: 02181940     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218194005002506     Document Type: Article

References (28)

1. R. Brachman and J. Schmolze, An overview of the KL-ONE knowledge representation system, Cognitive Science 9(2) (1985) 171-216.
2. R. Brachman, D. McGuiness, P. Patel-Schneider, L. Resnisck, and A. Borgida, Living with CLASSIC: When and how to use a KL-ONE-like language, in Principles of Semantic Networks, Morgan Kaufmann, 1991, pp. 401-456.
3. G. Piatetsky-Shapiro and W. Frawley (eds.), Knowledge Discovery in Databases, AAAI Press, 1991.
4. V. Rajlich, A model for change propogation based on graph rewriting, Proc. 1997 IEEE Int. Conf. on Software Maintenance, IEEE Press, 1997, pp. 84-91.
5. M. Sternberg and R. Reynolds, Using cultural algorithms to support re-engineering of rule-based expert systems in dynamic performance environments: A case study in fraud detection in IEEE Trans. on Evolutionary Computation 1(4) (1997) 225-243.
6. H. Al-Shehri, Evolution-Based Decision Tree Optimization Using Cultural Algorithms, Ph.D. Dissertation, Wayne State University, 1997.
7. N. Rychtyckyj, DLMS: Ten years of AI for vehicle assembly process planning, AAAI-99/IAAI-99 Proceedings, Orlando, FL, July 18-22, 1999, pp. 821-828.
8. N. Rychtyckyj and R. Reynolds, Using cultural algorithms to improve performance in semantic networks, in Proc. 1999 Congress of Evolutionary Computation, Washington D.C, July 6-9, vol. 3, 1997, pp. 1651-1656.
9. N. Rychtyckyj and R. Reynolds, Assessing the performance of cultural algorithms for semantic network re-engineering, in Proc. 2000 Congress on Evolutionary Computation, July 16-19, La Jolla, CA, 2000, pp. 1482-1491.
10. N. Rychtyckyj and R. Reynolds, Long-term maintenance of deployed knowledge representation systems, in Proc. Seventh Int. Conf. on the Principles of Knowledge Representation and Reasoning, April 12-17, Breckenridge, CO, 2000, pp. 494-504.
11. J. Debenham, Knowledge base maintenance through knowledge representation, in Proc. 12th Int. Conf. on Database and Expert Systems Applications, Munich, Germany, September 3-5, 2001.
12. Spangler, Scott and Kreulen, Jeffrey, Knowledge base maintenance using knowledge gap analysis, in Proc. 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, 2000, pp. 462-466.
13. L. Zhang, Y. Perl, M. Halper, J. Geller, and J. Cimino, An enriched unified medical language system semantic network with a multiple subsumption hierarchy, Journal of the American Medical Informatics Association 11(3) (2004) 195-206.
14. M. Quillian, Semantic Memory in Semantic Information Processing, MIT Press, 1975, pp. 216-270.
15. J. Schmolze and T. Lipkis, Classification in the KL-ONE knowledge representation system in Proc. Eight Int. Joint Conf. on Artificial Intelligence, Morgan Kaufmann, 1983, pp. 330-332.
16. W. A. Woods, Understanding subsumption and taxonomy, in Principles of Semantic Networks, Morgan Kaufmann, 1991, pp. 45-94.
17. M. Schmidt-Strauss, Subsumption in KL-ONE is undecidable, in Proc. First Int. Conf. on Knowledge Representation and Reasoning, Morgan Kaufmann, 1989, pp. 421-431.
18. N. Rychtyckyj, Classification in DLMS utilizing a KL-ONE representation language, in Proc. Sixth Int. Conf. on Tools with Artificial Intelligence, IEEE Computer Science Press, 1994, pp. 339-345.
19. R. G. Reynolds and C. Chung, A self-adaptive approach to representation shifts in cultural algorithms, in Proc. 1996 IEEE Int. Conf. on Evolutionary Computing, IEEE Press, 1996, pp. 94-99.
20. R. G. Reynolds and C. Chung, Regulating the amount of information used for self-adaptation in cultural algorithms, in Proc. Seventh Int. Conf. on Genetic Algorithms, Morgan Kaufmann, 1997, pp. 401-408.
21. R. G. Reynolds, An introduction to cultural algorithms, in Proc. 3rd Annual Conf. on Evolutionary Programming, eds. A. Sebald and L. Fogel, 1994, pp. 131-139.
22. L. Booker, Classifier systems, endogeneous fitness, and delayed rewards: A preliminary investigation, in Proc. 2001 Genetic and Evolutionary Computation Conference, San Francisco, CA, July 7-11, 2001, pp. 921-934.
23. P. Angeline, Tracking extrema in dynamic environments, in the 6th Int. Conf. in Evolutionary Programming VI, Indianapolis, Indiana, USA, April 1997, pp. 335-345.
24. C. Chan-Jin, Knowledge-Based Approaches to Self-Adaptation in Cultural Algorithms, Ph.D. thesis, Wayne State University, Detroit, MI, 1997.
25. G. Miller, The magic number seven plus or minus two: Some limits on our capacity for processing information, Psychological Review 63 (1956) 81-93.
26. R. G. Reynolds, Cultural algorithms: Theory and applications in New Ideas in Optimization, McGraw Hill, 1999, pp. 367-377.
27. N. Rychtyckyj, DLMS: An evaluation of KL-ONE in the automobile industry, in Proc. Fifth Int. Conf. on the Principles of Knowledge Representation and Reasoning, Morgan Kaufmann, 1996, pp. 588-596.
28. N. Rychtyckyj and R. Reynolds, Learning to re-engineer semantic networks using cultural algorithms, in Evolutionary Programming VII, Springer-Verlag, 1998, pp. 181-190.