Constructing tree-based knowledge structures from text corpus

Sheng Tun Li, Fu Ching Tsai

研究成果: Article同行評審

11 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)67-78
頁數12
期刊Applied Intelligence
33
發行號1
DOIs
出版狀態Published - 2010 八月

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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