Text Semantic Mining Model Based on the Algebra of Human Concept Learning

Jun Zhang, Xiangfeng Luo, Xiang He and Chuanliang Cai
Additional contact information
Jun Zhang: Shanghai University, China
Xiangfeng Luo: Shanghai University, China
Xiang He: Shanghai University, China
Chuanliang Cai: Shanghai University, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2011, vol. 5, issue 2, 80-96

Abstract: Dealing with the large-scale text knowledge on the Web has become increasingly important with the development of the Web, yet it confronts with several challenges, one of which is to find out as much semantics as possible to represent text knowledge. As the text semantic mining process is also the knowledge representation process of text, this paper proposes a text knowledge representation model called text semantic mining model (TSMM) based on the algebra of human concept learning, which both carries rich semantics and is constructed automatically with a lower complexity. Herein, the algebra of human concept learning is introduced, which enables TSMM containing rich semantics. Then the formalization and the construction process of TSMM are discussed. Moreover, three types of reasoning rules based on TSMM are proposed. Lastly, experiments and the comparison with current text representation models show that the given model performs better than others.

Date: 2011
References: Add references at CitEc
Citations:

Downloads: (external link)
https://services.igi-global.com/resolvedoi/resolve ... 018/jcini.2011040105 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:5:y:2011:i:2:p:80-96

Access Statistics for this article

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) is currently edited by Kangshun Li

More articles in International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) from IGI Global
Bibliographic data for series maintained by Journal Editor ().