首页> 外文期刊>IEEE transactions on industrial informatics >Hierarchy-Cutting Model Based Association Semantic for Analyzing Domain Topic on the Web
【24h】

Hierarchy-Cutting Model Based Association Semantic for Analyzing Domain Topic on the Web

机译:基于层次切割模型的关联语义在网络上分析领域主题

获取原文
获取原文并翻译 | 示例
           

摘要

Association link network (ALN) can organize massive Web information to provide many intelligent services in our big data society. Effective semantic layered technologies not only can provide theoretical support for knowledge discovery in Web resources, but also can improve the searching efficiency of related information systems such as Web information system and industrial information system. How to realize the layer division of association semantic by the hierarchy analysis of ALN is an important research topic. To solve this problem, this paper proposes a hierarchy-cutting model of association semantic. First, experiments of four types of keywords with different linking roles are conducted to discover the possible distribution law. Experimental results show that these keywords with association role reveal previous power-law distribution. Then, based on the discovered power-law distribution, up-cutting and down-cutting points are presented to divide the association semantic into three layers. At the same time, theories of the hierarchy-cutting model are presented. Finally, examples of current core topic and permanent topics belonging to a domain are given. The experiments show that hierarchy-cutting points have high accuracy. The multilayer theory of association semantic can provide a theoretical support for knowledge recommendation with different particle sizes on ALNs.
机译:协会链接网络(ALN)可以组织大量的Web信息,以在我们的大数据社会中提供许多智能服务。有效的语义分层技术不仅可以为Web资源中的知识发现提供理论支持,而且可以提高Web信息系统,工业信息系统等相关信息系统的搜索效率。如何通过ALN的层次分析实现联想语义的层次划分是一个重要的研究课题。为了解决这个问题,本文提出了一种关联语义的层次切割模型。首先,对具有不同链接角色的四种类型的关键字进行了实验,以发现可能的分布规律。实验结果表明,这些具有关联作用的关键词揭示了以前的幂律分布。然后,根据发现的幂律分布,提出了上切点和下切点,将关联语义分为三层。同时,提出了层次切割模型的理论。最后,给出了属于一个域的当前核心主题和永久主题的示例。实验表明层次分割点具有较高的精度。关联语义的多层理论可以为ALN上不同粒度的知识推荐提供理论支持。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号