Duplicate Identification in Deep Web Data Integration
Duplicate identification is a critical step in deep web data integration, and generally, this task has to be performed over multiple web databases. However, a customized matcher for two web databases often does not work well for other two ones due to various presentations and different schemas. It is not practical to build and maintain Cn2 matchers for n web databases. In this paper, we target at building one universal matcher over multiple web databases in one domain. According to our observation, the similarity on an attribute is dependent of those of some other attributes, which is ignored by existing approaches. Inspired by this, we propose a comprehensive solution for duplicate identification problem over multiple web databases. The extensive experiments over real web databases on three domains show the proposed solution is an effective way to address the duplicate identification problem over multiple web databases.
duplicate identification deep web data integration web database
Wei Liu Xiaofeng Meng Jianwu Yang Jianguo Xiao
Institute of Computer Science & Technology, Peking University, Beijing, China School of Information, Renmin University of China, Beijing, China
国际会议
11th International Conference,WAIM 2010(第十一届网络时代管理国际会议)
九寨沟
英文
5-17
2010-07-14（万方平台首次上网日期，不代表论文的发表时间）