...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >A Generalized Flow-Based Method for Analysis of Implicit Relationships on Wikipedia
【24h】

A Generalized Flow-Based Method for Analysis of Implicit Relationships on Wikipedia

机译：基于通用流的隐式关系分析方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We focus on measuring relationships between pairs of objects in Wikipedia whose pages can be regarded as individual objects. Two kinds of relationships between two objects exist: in Wikipedia, an explicit relationship is represented by a single link between the two pages for the objects, and an implicit relationship is represented by a link structure containing the two pages. Some of the previously proposed methods for measuring relationships are cohesion-based methods, which underestimate objects having high degrees, although such objects could be important in constituting relationships in Wikipedia. The other methods are inadequate for measuring implicit relationships because they use only one or two of the following three important factors: distance, connectivity, and cocitation. We propose a new method using a generalized maximum flow which reflects all the three factors and does not underestimate objects having high degree. We confirm through experiments that our method can measure the strength of a relationship more appropriately than these previously proposed methods do. Another remarkable aspect of our method is mining elucidatory objects, that is, objects constituting a relationship. We explain that mining elucidatory objects would open a novel way to deeply understand a relationship.
机译：我们专注于测量Wikipedia中成对的对象之间的关系，这些对象的页面可以视为单个对象。两个对象之间存在两种关系：在Wikipedia中，显式关系由对象两个页面之间的单个链接表示，而隐式关系由包含两个页面的链接结构表示。先前提出的一些用于度量关系的方法是基于内聚的方法，虽然低估了具有较高程度的对象，但是这些对象对于在Wikipedia中构成关系很重要。其他方法不足以衡量隐式关系，因为它们仅使用以下三个重要因素中的一个或两个：距离，连通性和引诱。我们提出了一种使用广义最大流的新方法，该方法可以反映所有三个因素，并且不会低估具有高度的对象。我们通过实验证实，与这些先前提出的方法相比，我们的方法可以更恰当地衡量一种关系的强度。我们方法的另一个显着方面是挖掘说明性对象，即构成关系的对象。我们解释说，挖掘说明性对象将为深入理解关系开辟一种新颖的方式。

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