Evolution and evaluation in knowledge fusion system

Authors:
Jin Gou;Jiangang Yang;Qian Chen
Affiliations:
Ningbo Institute of Technology, Zhejiang University, Ningbo, China;Ningbo Institute of Technology, Zhejiang University, Ningbo, China;Ningbo Institute of Technology, Zhejiang University, Ningbo, China
Venue:
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Year:
2005

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Abstract

The paper presents a method to control evolution of pattern in a knowledge fusion system. A self-adapt evaluation mechanism to assign proper value dynamically to weight parameters is also described. Some rules are defined with aid of the matrix theory to promise the controllablity and describability to the evolution process. A new knowledge object, called LKS (local knowledge state), that can redirect path in knowledge fusion system and evolve to other knowledge object(s) is formed in that model. Experimental results of a case study show that it can improve the efficiency and reduce computational complexity of a knowledge fusion system.