A Large Knowledge Base Partitioning and Reasoning Study Based on Graph Theory

In this paper, we present a method for solving the partitioning problem of complex knowledge bases. This proposed method utilizes the feature of fuzzy cognitive map (FCM) to construct partitioning rules, the partition algorithm based on strongly connected of directed graph helps to partition a fuzzy cognitive map into a series of sub-FCM. Further, we discuss the inference among sub-FCM. Finally, an illustrative example is provided, and its results suggest that the method is capable of partitioning and reasoning large knowledge bases. Keywords-knowledge base; fuzzy cognitive map; partition; graph theory

[1]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[2]  P. P. Groumpos,et al.  Fuzzy cognitive maps: a soft computing technique for intelligent control , 2000, Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No.00CH37147).

[3]  Chrysostomos D. Stylios,et al.  Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule , 2003, Australian Conference on Artificial Intelligence.

[4]  Michael N. Vrahatis,et al.  A first study of fuzzy cognitive maps learning using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[5]  Zhao Yu-hua Strong Kernel Graphic Algorithm for Searching All Essential Circuits of Simple Directed Graph , 2004 .

[6]  Thomas Parisini,et al.  INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL , 2009 .