Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Guo, P., Fan, L., Ye, L. (2006). A Method to Eliminate Incompatible Knowledge and Equivalence Knowledge. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_30
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DOI: https://doi.org/10.1007/11739685_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
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