A Method to Eliminate Incompatible Knowledge and Equivalence Knowledge

  • Ping Guo
  • Li Fan
  • Lian Ye
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


Knowledge base is the foundation of intelligent systems. It is very important to insure the consistency and non-redundancy of knowledge in a knowledge base. Due to the variety of exterior knowledge sources, it is necessary to eliminate incompatible knowledge and equivalence knowledge in the process of knowledge integration. In this paper, we research a strategy to eliminate incompatible knowledge and equivalence knowledge in knowledge integration based on equivalence classification, and so present a new knowledge integration algorithm which is effective in improving the efficiency of knowledge integration.


Knowledge Base Equivalence Class Knowledge Source Equivalence Knowledge Knowledge Integration 
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.


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  1. 1.
    Gaines, B.R., Shaw, M.L.: Eliciting knowledge and transferring it effectively to a knowledge-based system. IEEE Transaction on Knowledge and Data Engineering 5(1), 4–14 (1993)CrossRefGoogle Scholar
  2. 2.
    Baral, C., Kraus, S., Minker, J.: Combining multiple knowledge bases. IEEE Transactions on Knowledge and Data Engineering 3(2), 208–220 (1991)CrossRefGoogle Scholar
  3. 3.
    Yuan, Y., Zhuang, H.: A genetic algorithm for generating fuzzy classification rules. Fuzzy Sets and Systems 84, 1–19 (1996)MATHCrossRefGoogle Scholar
  4. 4.
    Medsker, L., Tan, M., Turban, E.: Knowledge acquisition from multiple experts: problems and issues. Expert Systems with Applications 9(1), 35–40 (1995)CrossRefGoogle Scholar
  5. 5.
    Wang, C.H., Hong, T.P., Tseng, S.S.: Knowledge integration by genetic algorithms. In: Proceedings of the Seventh International Fuzzy Systems Association World Congress, vol. 2, pp. 404–408 (1997)Google Scholar
  6. 6.
    Wang, C.H., Hong, T.P., Tseng, S.S.: A genetic fuzzy-knowledge integration framework. In: The Seventh International Conference of Fuzzy Systems, pp. 1194–1199 (1998)Google Scholar
  7. 7.
    Wang, C.H., Hong, T.P., Tseng, S.S.: Integrating membership functions and fuzzy rule sets from multiple knowledge sources. Fuzzy Sets and Systems 112, 141–154 (2000)CrossRefGoogle Scholar
  8. 8.
    Wang, C.H., Hong, T.P., Tseng, S.S.: A Genetics-Based Approach to Knowledge Integration and Refinement. Journal of Information Science and Engineering 17, 85–94 (2000)Google Scholar
  9. 9.
    Mathias, K.E., Whity, L.D.: Transforming the Search Spacs with Gray Coding. In: Proc. of the 1st IEEE Intl. Conf. on Evolutionary Computation, Orlando, Florid, USA, pp. 519–542. IEEE Press, Los Alamitos (1994)Google Scholar
  10. 10.
    Wang, C.H., Hong, T.P., Tseng, S.S.: A Coverage-based Genetic Knowledge-integration strategy. Experty Systems with Applications 19, 9–17 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ping Guo
    • 1
  • Li Fan
    • 1
  • Lian Ye
    • 1
  1. 1.School of Computer ScienceChongqing UniversityChongqingChina

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