An Efficient Knowledge Base Management Using Hybrid SOM


The KIPS Transactions:PartB , Vol. 9, No. 5, pp. 635-642, Oct. 2002
10.3745/KIPSTB.2002.9.5.635,   PDF Download:

Abstract

There is a rapidly growing demand for the intellectualization of information technology. Especially, in the area of KDD (Knowledge Discovery in Database) which should make an optimal decision of finding knowledge from a large amount of data, the demand is enormous. A large volume of Knowledge Base should be efficiently managed for a more intellectual choice. This study is proposing a Hybrid SOM for an efficient search and renewal of knowledge base, which combines a self-study nerve network, Self-Organization Map with a probable distribution theory in order to get knowledge needed for decision-making management from the Knowledge Base. The efficient knowledge base management through this proposed method is carried out by a stimulation test. This test confirmed that the proposed Hybrid SOM can manage with efficiency Knowledge Base.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
K. B. Yoon, J. H. Choi, C. J. Wang, "An Efficient Knowledge Base Management Using Hybrid SOM," The KIPS Transactions:PartB , vol. 9, no. 5, pp. 635-642, 2002. DOI: 10.3745/KIPSTB.2002.9.5.635.

[ACM Style]
Kyung Bae Yoon, Jun Hyeog Choi, and Chang Jong Wang. 2002. An Efficient Knowledge Base Management Using Hybrid SOM. The KIPS Transactions:PartB , 9, 5, (2002), 635-642. DOI: 10.3745/KIPSTB.2002.9.5.635.