On Hierarchical Knowledge Acquisition and Application

Author

Fu, Xixu ; Wei, Hui

Author_Institution

Dept. of Comput. Sci., Fudan Univ., Shanghai, China

fYear

2009

fDate

25-26 April 2009

Firstpage

185

Lastpage

189

Abstract

Classification is a famous branch of machine learning. We have tried many ways to invent and improve algorithms to get better results from given data. However, few have been done on how to revise data to adapt machine learning. In this paper, the same classifiers are implemented on same object sets which are different in the granularity of classification to show different classification can make great difference in the quality of classification first. Then the development of knowledge-base is studied. At last, a progressive knowledge acquisition method is advanced inspired by humanpsilas cognition behavior.

Keywords

data mining; knowledge based systems; learning (artificial intelligence); ontologies (artificial intelligence); pattern classification; data mining; hierarchical knowledge acquisition; human cognition behavior; knowledge base; machine learning; ontology; pattern classification; Application software; Artificial intelligence; Classification tree analysis; Computer science; Data mining; Decision trees; Knowledge acquisition; Knowledge based systems; Machine learning; Ontologies; class hierarchy; knowledge acquisition; knowledge representation; ontology;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence, 2009. JCAI '09. International Joint Conference on

Conference_Location

Hainan Island

Print_ISBN

978-0-7695-3615-6

Type

conf

DOI

10.1109/JCAI.2009.147

Filename

5158970