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

Authors:
Xiangfeng Luo;Jun Zhang;Xiang He;Chuanliang Cai
Affiliations:
Shanghai University, China;Shanghai University, China;Shanghai University, China;Shanghai University, China
Venue:
International Journal of Cognitive Informatics and Natural Intelligence
Year:
2011

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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.