Learning Causal Semantic Representation from Information Extraction

Author

Xin, Zuo ; Limin, Wang ; Shuang, Zhou

Author_Institution

Sch. of Foreign Languages, ChangChun Univ. of Technol., Changchun, China

fYear

2009

fDate

15-16 May 2009

Firstpage

404

Lastpage

407

Abstract

For reasoning with uncertain knowledge causal semantic analysis is proposed to construct logical rules,which are extracted from decision tree induction and Bayes inference based on generalized information theory. These rules can represent multi-level semantic knowledge of the relationship between the data and information implicated. Empirical studies on a set of natural domains show that the semantic completeness of generalized information theory has clear advantage in representing semantic knowledge from different levels.

Keywords

decision trees; inference mechanisms; information retrieval; knowledge representation; learning (artificial intelligence); Bayes inference; causal semantic representation learning; decision tree induction; generalized information theory; information extraction; logical rules; uncertain knowledge causal semantic analysis; Classification tree analysis; Competitive intelligence; Computer science education; Data mining; Decision trees; Educational technology; Inference algorithms; Information theory; Machine learning algorithms; Ubiquitous computing; causal semantic representation; generalized information theory; logical rules;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Ubiquitous Computing and Education, 2009 International Symposium on

Conference_Location

Chengdu

Print_ISBN

978-0-7695-3619-4

Type

conf

DOI

10.1109/IUCE.2009.73

Filename

5222934