期刊文献+

基于思维导图的小学教育语义本体库构建 被引量:3

Construct primary education semantic ontology library based mind mapping
下载PDF
导出
摘要 针对小学教育领域知识的特点,以利用小学教育语义本体创建思维导图为应用目的,本文提出了一种基于思维导图层次结构的本体库创建方法。该方法通过对本体的需求分析,应用逻辑描述对本体提供了语义定义;从信息收集、概念选择、属性关系的建立和语义标签的添加等方面描述了本体的创建过程;最后,利用经过改进的满足标签层次结构属性的相似度计算方法来完成标签比对过程,将不同本体关联形成本体库。 Researches conducted for Mind mapping application in primary education semantic ontology, while considering unique characteristics of primary education, paper proposed a solution based on cluster structure derived from mind mapping. The method provided a logical description of the ontologies to precisely define semantics by analyzing requirement; The process of constructing ontology is described from information collection, concept selection, estabhshment of attribute relationship and adjunction of semantic tags; Finally, similarity calculation method that improved and meet the hierarchical structure of tags were completed the process of tag comparison and adapted to associate different ontologies to form ontology library.
作者 邱聃
出处 《电子设计工程》 2016年第3期53-56,共4页 Electronic Design Engineering
关键词 思维导图 小学教育 语义本体 标签 相似度计算 mind mapping primary education semantic ontology tag similarity
  • 相关文献

参考文献7

二级参考文献75

  • 1唐静.叙词表转换为Ontology的研究[J].情报理论与实践,2004,27(6):642-645. 被引量:36
  • 2陈晓云,李荣陆,胡运发.基于最小词频阈值的文档特征选择[J].模式识别与人工智能,2006,19(4):531-537. 被引量:7
  • 3张玉芳,彭时名,吕佳.基于文本分类TFIDF方法的改进与应用[J].计算机工程,2006,32(19):76-78. 被引量:121
  • 4索红光,刘玉树,曹淑英.一种基于词汇链的关键词抽取方法[J].中文信息学报,2006,20(6):25-30. 被引量:88
  • 5李洁,丁颖.语义网关键技术概述[J].计算机工程与设计,2007,28(8):1831-1833. 被引量:40
  • 6Hall Jiawei,Kamber M.数据挖掘概念与技术[M].范明,孟小峰等,译.北京:机械工业出版社,2001:149-176.
  • 7Agrawal R, Imielinski T, Swami A. Mining Association Rules between Sets of Items in Large Databases[C]//Proceedings of the 1993 ACM SIGMOD Conference. Washington D C: [ s. n ], 1993: 207 - 216.
  • 8Agrawal. R,Srikant R. Fast Algorithm for Mining Association Rules[C]//Proceedings of the 20th Very Large Data Bases (VLDB' 94) Conference. Santiago, Chile: [ s. n. ], 1994: 487 - 499.
  • 9Agrawal R,Srikant R. Fast Algorithm for Mining Association Rules in Large Databases[R]. San Jose, CA: IBM Almaden Research Center, 1994.
  • 10Park J S, Chen M S, Yu P S. An effective hash based algorithm for mining association rules[ C]//In: Proc. 1995 ACM SIGMOD. San Jose, CA: [ s. n. ], 1995 : 175 - 186.

共引文献147

同被引文献35

引证文献3

二级引证文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部