Constructing tree-based knowledge structures from text corpus

Sheng Tun Li, Fu Ching Tsai

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)67-78
Number of pages12
JournalApplied Intelligence
Volume33
Issue number1
DOIs
Publication statusPublished - 2010 Aug 1

Fingerprint

Macros
Navigation

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

@article{805c4b3cb8e24c3ebd021d10614804e0,
title = "Constructing tree-based knowledge structures from text corpus",
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.",
author = "Li, {Sheng Tun} and Tsai, {Fu Ching}",
year = "2010",
month = "8",
day = "1",
doi = "10.1007/s10489-010-0243-2",
language = "English",
volume = "33",
pages = "67--78",
journal = "Applied Intelligence",
issn = "0924-669X",
publisher = "Springer Netherlands",
number = "1",

}

Constructing tree-based knowledge structures from text corpus. / Li, Sheng Tun; Tsai, Fu Ching.

In: Applied Intelligence, Vol. 33, No. 1, 01.08.2010, p. 67-78.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Constructing tree-based knowledge structures from text corpus

AU - Li, Sheng Tun

AU - Tsai, Fu Ching

PY - 2010/8/1

Y1 - 2010/8/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=77956186169&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956186169&partnerID=8YFLogxK

U2 - 10.1007/s10489-010-0243-2

DO - 10.1007/s10489-010-0243-2

M3 - Article

AN - SCOPUS:77956186169

VL - 33

SP - 67

EP - 78

JO - Applied Intelligence

JF - Applied Intelligence

SN - 0924-669X

IS - 1

ER -