DocumentCode
2709656
Title
Background knowledge driven ontology discovery
Author
Chen, Shan ; Alahakoon, Damminda ; Indrawan, Maria
Author_Institution
Sch. of Comput. Sci. & Software Eng., Monash Univ., Clayton, Vic., Australia
fYear
2005
fDate
29 March-1 April 2005
Firstpage
202
Lastpage
207
Abstract
We have previously proposed a GSOM-based hybrid model for automatic discovery of ontology, the first step towards semi-automation of ontology construction. One of the shortcomings of this previous model is the use of a threshold for selecting abstraction levels. The threshold might introduce an inappropriate concept and cause information loss. In this paper, we introduce a new parameter called context ratio (cr) to overcome this drawback. The cr is used as stopping criteria for traversing hypernyms and identifying appropriate abstraction levels. It allows us to extend the previously proposed framework to integrate methodology for multiple inheritance validation in the discovered ontology.
Keywords
data mining; ontologies (artificial intelligence); self-organising feature maps; GSOM-based hybrid model; abstraction levels; context ratio; growing self-organising map; knowledge driven ontology discovery; multiple inheritance validation; Automation; Documentation; Knowledge acquisition; Knowledge based systems; Neural networks; Ontologies;
fLanguage
English
Publisher
ieee
Conference_Titel
e-Technology, e-Commerce and e-Service, 2005. EEE '05. Proceedings. The 2005 IEEE International Conference on
Print_ISBN
0-7695-2274-2
Type
conf
DOI
10.1109/EEE.2005.40
Filename
1402295
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