• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>The Knowledge Engineering Review >Data mining for building knowledge bases: techniques, architectures and applications
【24h】

Data mining for building knowledge bases: techniques, architectures and applications

机译：建立知识库的数据挖掘：技术，体系结构和应用

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

Data mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have been proposed for what we call knowledge mining: integrating data mining for knowledge extraction from unstructured text (possibly making use of a knowledge base), and at the same time, consistently incorporating this new information into the knowledge base. After describing a number of existing knowledge mining systems, we review the state-of-the-art literature on both current text mining methods (emphasising stream mining) and techniques for the construction and maintenance of knowledge bases. In particular, we focus on mining entities and relations from unstructured text data sources, entity disambiguation, entity linking and question answering. We conclude by highlighting general trends in knowledge mining research and identifying problems that require further research to enable more extensive use of knowledge bases.
机译：用于从文本中提取知识的数据挖掘技术已广泛应用于包括问题解答，文档摘要，事件提取和趋势监视的应用程序。但是，当前的方法主要针对特定​​目的在小型定制数据集上进行了测试。大量数据和高速数据流（例如社交媒体提要）的可用性促使人们需要从此类数据源中自动提取知识，并将现有方法推广到更实际的应用中。最近，已经提出了几种我们称为知识挖掘的体系结构：集成数据挖掘以从非结构化文本中提取知识（可能利用知识库），同时将这一新信息始终集成到知识库中。在描述了许多现有的知识挖掘系统之后，我们回顾了有关当前文本挖掘方法（强调流挖掘）以及用于知识库的构建和维护技术的最新文献。特别是，我们专注于从非结构化文本数据源，实体消歧，实体链接和问题解答中挖掘实体和关系。最后，我们着重强调了知识挖掘研究的总体趋势，并确定了需要进一步研究以扩大知识库使用范围的问题。

著录项

  • 来源
    《The Knowledge Engineering Review》 |2016年第2期|97-123|共27页
  • 作者

    Krzywicki Alfred; Wobcke Wayne; Bain Michael; Martinez John Calvo; Compton Paul;

  • 作者单位

    Univ New S Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia;

    Univ New S Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia;

    Univ New S Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia;

    Univ New S Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia;

    Univ New S Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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