标题:A Query Interface Matching Approach Based on Extended Evidence Theory for Deep Web
作者:Dong, Yong-Quan; Li, Qing-Zhong; Ding, Yan-Hui; Peng, Zhao-Hui
作者机构:[Dong, Y.-Q] School of Computer Science and Technology, Shandong University, Jinan 250014, China, School of Computer Science and Technology, Xuzhou No 更多
通讯作者:Li, QZ
通讯作者地址:[Li, QZ]Shandong Univ, Sch Comp Sci & Technol, Jinan 250014, Peoples R China.
来源:计算机科学技术学报(英文版)
出版年:2010
卷:25
期:3
页码:537-547
DOI:10.1007/s11390-010-9343-z
关键词:query interface matching;schema matching;Deep Web;Web data integration
摘要:Matching query interfaces is a crucial step in data integration across multiple Web databases.Different types of information about query interface schemas have been used to match attributes between schemas.Relying on a single aspect of information is not sufficient and the matching results of individual matchers are often inaccurate and uncertain.The evidence theory is the state-of-the-art approach for combining multiple sources of uncertain information.However,traditional evidence theory has the limitations of treating individual matchers in different matching tasks equally for query interface matching,which reduces matching performance.This paper proposes a novel query interface matching approach based on extended evidence theory for Deep Web.Our approach firstly introduces the dynamic prediction procedure of different matchers\' credibilities.Then,it extends traditional evidence theory with the credibilities and uses exponentially weighted evidence theory to combine the results of multiple matchers.Finally,it performs matching decision in terms of some heuristics to obtain the final matches.Our approach overcomes the shortage of traditional method and can adapt to different matching tasks.Experimental results demonstrate the feasibility and effectiveness of our proposed approach.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:1
Scopus被引频次:9
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955906569&doi=10.1007%2fs11390-010-9343-z&partnerID=40&md5=afeda0b78e175138095e24b7a185ad2c
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