###
DOI:
Journal of Software:2009.20(zk):286-297

一种关系数据库关键词检索相关反馈方法
彭朝晖,崔立真,王珊,张俊,王长亮
(山东大学 计算机科学与技术学院,山东 济南 250101;数据工程与知识工程教育部重点实验室(中国人民大学),北京 100872;中国人民大学 信息学院,北京 100872;数据工程与知识工程教育部重点实验室(中国人民大学),北京 100872;大连海事大学 计算机科学与技术学院,辽宁 大连 116026;香港科技大学,香港)
Method of Relevance Feedback in Keyword Search over Relational Databases
PENG Zhao-Hui,CUI Li-Zhen,WANG Shan,ZHANG Jun,WANG Chang-Liang
()
Abstract
Chart / table
Reference
Similar Articles
Article :Browse 2664   Download 3404
Received:May 03, 2009    Revised:September 30, 2009
> 中文摘要: 在关系数据库关键词检索(KSORD)中,用户的检索往往不能一次成功,有时需要多次重构查询(找到一组新关键词)来进行检索,但是查询的重构往往要花费用户大量的时间和精力.针对KSORD的结果,提出了一种相关反馈方法来自动重构查询.该方法选用基于向量空间模型的打分机制对KSORD结果打分,根据用户反馈或伪反馈的结果信息,采用基于概率的方法计算扩展用的语词,以查询扩展的方法自动重构查询进行再次检索.实验结果表明,这种方法能够为用户提供更多的相关结果.
Abstract:In keyword search over relational databases (KSORD), retrieval of user’s initial query is often unsatisfying. User has to reformulate his query and execute the new query, which costs much time and effort. In this paper, a method of automatically reformulating user queries by relevance feedback is introduced. The method adopts a ranking method based on vector space model to rank KSORD results. Based on the results of user feedback or pseudo feedback, it computes expansion terms based on probability and reformulates the new query using query expansion. Experimental results verify that after KSORD systems executing the new query, more relevant results are presented to user.
文章编号:     中图分类号:    文献标志码:
基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60473069, 60496325 (国家自然科学基金); the China Postdoctoral Science Foundation under Grant No.200904501193 (中国博士后科学基金) Supported by the National Natural Science Foundation of China under Grant Nos.60473069, 60496325 (国家自然科学基金); the China Postdoctoral Science Foundation under Grant No.200904501193 (中国博士后科学基金)
Foundation items:
Reference text:

彭朝晖,崔立真,王 珊,张 俊,王长亮.一种关系数据库关键词检索相关反馈方法.软件学报,2009,20(zk):286-297

PENG Zhao-Hui,CUI Li-Zhen,WANG Shan,ZHANG Jun,WANG Chang-Liang.Method of Relevance Feedback in Keyword Search over Relational Databases.Journal of Software,2009,20(zk):286-297