首页> 外文会议> >An Automated Domain-Specific Answer Ontology Construction
【24h】

An Automated Domain-Specific Answer Ontology Construction

机译:自动的特定领域答案本体构造

获取原文

摘要

Recently, with the fast development of interactive information sharing on the internet, the query-oriented answer search services are more and more popular, such as Yahoo Answers, Google Answers and so on. Their common advantages are high precision, domain-specific search, clear format, etc. However towards domain-specific search, people usually are unable to determine suitable concept terms as queries to submit on account of their lack of domain knowledge. In this paper, we propose an approach for constructing a domain-specific answer ontology automatically in respect of Chinese queries to solve the said problem. First, queries and their answers are collected from a web search space. Second, for extracting implicated concept terms from collected queries and answers, the CKIP system is utilized to make a Chinese part-of-speech tagging procedure to segment. Thirdly, use a similarity measure to converge duplicate queries with their corresponding answers and take fuzzy clustering method and degree of membership to describe the relationships between converged queries and their corresponding answers. Finally, to generate a domain-specific ontology is based on an improved hierarchical agglomerative clustering algorithm to hierarchically group queries.
机译:近年来,随着互联网上交互式信息共享的快速发展,面向查询的答案搜索服务越来越流行,例如Yahoo Answers,Google Answers等。它们的共同优点是精度高,特定领域的搜索,格式清晰等。但是,对于特定领域的搜索,由于缺乏领域知识,人们通常无法确定合适的概念术语作为要提交的查询。在本文中,我们提出了一种针对中文查询自动构建特定领域的答案本体的方法,以解决上述问题。首先,从网络搜索空间收集查询及其答案。其次,为了从收集到的查询和答案中提取隐含的概念术语,使用CKIP系统进行汉语词性标注过程进行分割。第三,使用相似性度量将重复的查询与其对应的答案进行收敛,并采用模糊聚类方法和隶属度来描述收敛的查询与其对应的答案之间的关系。最后,生成特定于域的本体是基于改进的分层聚集聚类算法对查询进行分层分组的。

著录项

  • 来源
    《》|2007年|378-381|共4页
  • 会议地点
  • 作者

    Ko; Wei-Min; Li; Huan-Chung;

  • 作者单位
  • 会议组织
  • 原文格式 PDF
  • 正文语种
  • 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