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Constructing tree-based knowledge structures from text corpus

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Abstract

A knowledge structure identifies how people think and displays a macro view of human perception. By discovering the hidden structural relations of knowledge, significant reasoning patterns are retrieved to enhance further knowledge sharing and distribution. However, the utilization of such approaches is apt to be limited due to the lack of hierarchical features and the problem of information overload, which make it difficult to enhance comprehension and provide effective navigation. To address these critical issues, we propose a new approach to construct a tree-based knowledge structure from corpus which can reveal the significant relations among knowledge objects and enhance user comprehension. The effectiveness of the proposed method is demonstrated with two representative public data sets. The evaluation results show that the method presented in this work achieves remarkable consistency with the domain-specific knowledge structure, and is capable of reflecting appropriate similarities among knowledge objects along with hierarchical implications in the document classification task.

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Authors and Affiliations

  1. Institute of Information Management, National Cheng Kung University, Tainan, Taiwan, ROC

    Sheng-Tun Li & Fu-Ching Tsai

  2. Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan, ROC

    Sheng-Tun Li

Authors
  1. Sheng-Tun Li
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  2. Fu-Ching Tsai
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Corresponding author

Correspondence to Sheng-Tun Li.

Additional information

The work of S.-T. Li is partly supported by National Science Council, Taiwan under contract NSC98-2410-H-006-007.

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Li, ST., Tsai, FC. Constructing tree-based knowledge structures from text corpus. Appl Intell 33, 67–78 (2010). https://doi.org/10.1007/s10489-010-0243-2

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