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

Author

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  • Jun Zhang

    (Shanghai University, China)

  • Xiangfeng Luo

    (Shanghai University, China)

  • Xiang He

    (Shanghai University, China)

  • Chuanliang Cai

    (Shanghai University, China)

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.

Suggested Citation

  • Jun Zhang & Xiangfeng Luo & Xiang He & Chuanliang Cai, 2011. "Text Semantic Mining Model Based on the Algebra of Human Concept Learning," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 5(2), pages 80-96, April.
    • Handle: RePEc:igg:jcini0:v:5:y:2011:i:2:p:80-96
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