吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (1): 236-244.doi: 10.13229/j.cnki.jdxbgxb201501035

• 论文 • 上一篇    下一篇

面向文本的本体学习方法

王俊华1，2，3，左万利1，2，彭涛1，2   

  1. 1.吉林大学 计算机科学与技术学院，长春 130012；
    2.吉林大学 符号计算与知识工程教育部重点实验室，长春 130012；
    3.长春工业大学 计算机科学与工程学院，长春 130012
  • 收稿日期:2013-09-02 出版日期:2015-02-01 发布日期:2015-02-01
  • 通讯作者: 左万利(1957),男,教授,博士.研究方向:本体工程,Web数据挖掘.E-mail:wanli@jlu.edu.cn
  • 作者简介:王俊华(1982)，女，博士研究生.研究方向:本体工程和自然语言处理.E-mail:wangjh10@mails.jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(60973040)；国家自然科学青年基金项目(60903098,61300148)；吉林省重点科技攻关项目(20130206051GX)；吉林省科技计划青年科研基金项目(20130522112JH).

Test-oriented ontology learning methods

WANG Jun-hua1,2,3,ZUO Wan-li1,2,PENG Tao1,2   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education,Changchun 130012,China;
    3.College of Computer Science and Engineering,Changchun University of Technology,Changchun 130012,China
  • Received:2013-09-02 Online:2015-02-01 Published:2015-02-01

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摘要： 借助文本预处理工具Gate和通用本体WordNet，采用统计、频繁项挖掘、模式匹配、启发式学习和主动学习等技术，学习本体基元——概念(含实例)、概念间的分类关系、概念间的语义关系和概念属性，其中概念属性学习为本文首次提出。实验结果表明，本文方法改善了概念语义排歧效果，丰富了短语概念学习与语义关系学习，提高了本体自动构建的准确度，降低了本体学习的代价。

关键词: 人工智能, 本体学习, 主动学习, 模式匹配, 频繁项挖掘, 启发式学习

Abstract: The techniques of statistics, frequent item mining, pattern matching, heuristic learning and active learning are employed to learn the concepts (including instances), taxonomic relations, semantic relations and the concept properties from the documents based on preprocessing tool Gate and general ontology WordNet. The concept property learning was first proposed in this paper. Experiment results show that the proposed ontology learning method can improve the effect of word semantic disambiguation, enrich phrase concept learning and semantic relationship learning, increase the accuracy of automatic ontology construction and reduce the cost of ontology learning.

Key words: artificial intelligence, ontology learning, active learning, pattern matching, frequent item mining, heuristic learning

中图分类号: 

  • TP18
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