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A Web Knowledge Discovery Engine Based on Concept Algebra

A Web Knowledge Discovery Engine Based on Concept Algebra

Kai Hu, Yingxu Wang, Yousheng Tian
Copyright: © 2010 |Volume: 4 |Issue: 1 |Pages: 18
ISSN: 1557-3958|EISSN: 1557-3966|ISSN: 1557-3958|EISBN13: 9781616929312|EISSN: 1557-3966|DOI: 10.4018/jcini.2010010105
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MLA

Hu, Kai, et al. "A Web Knowledge Discovery Engine Based on Concept Algebra." IJCINI vol.4, no.1 2010: pp.80-97. http://doi.org/10.4018/jcini.2010010105

APA

Hu, K., Wang, Y., & Tian, Y. (2010). A Web Knowledge Discovery Engine Based on Concept Algebra. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 4(1), 80-97. http://doi.org/10.4018/jcini.2010010105

Chicago

Hu, Kai, Yingxu Wang, and Yousheng Tian. "A Web Knowledge Discovery Engine Based on Concept Algebra," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 4, no.1: 80-97. http://doi.org/10.4018/jcini.2010010105

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Abstract

Autonomous on-line knowledge discovery and acquisition play an important role in cognitive informatics, cognitive computing, knowledge engineering, and computational intelligence. On the basis of the latest advances in cognitive informatics and denotational mathematics, this paper develops a web knowledge discovery engine for web document restructuring and comprehension, which decodes on-line knowledge represented in informal documents into cognitive knowledge represented by concept algebra and concept networks. A visualized concept network explorer and a semantic analyzer are implemented to capture and refine queries based on concept algebra. A graphical interface is built using concept and semantic models to refine users’ queries. To enable autonomous information restructuring by machines, a two-level knowledge base that mimics human lexical/syntactical and semantic cognition is introduced. The information restructuring model provides a foundation for automatic concept indexing and knowledge extraction from web documents. The web knowledge discovery engine extends machine learning capability from imperative and adaptive information processing to autonomous and cognitive knowledge processing with unstructured documents in natural languages.

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