A Hybrid Method for Extracting Deep Web Information

被引：0
|
作者：
Zhang, Yuanpeng [1 ]
Wang, Li [1 ]
Jiang, Kui [1 ]
Qian, Danmin [1 ]
Dong, Jiancheng [1 ]
机构：
[1] Nantong Univ, Sch Med, Dept Med Informat, Nantong 226001, Jiangsu, Peoples R China
来源：
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTOMATION, MECHANICAL CONTROL AND COMPUTATIONAL ENGINEERING | 2015年 / 124卷
关键词：
information extraction; clinic expert information; domain model; block importance model; SVM;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Some previous works show that more than 60% of the information available on the Web is located in Deep Web database. Such information cannot be directly indexed by search engines. In this paper, a hybrid method, which is composed of a domain model and a block importance model is proposed to extract information in Deep Web. The domain model is used for classifying and identifying whether a form is a WQI. The block importance model is used for filtering noisy information in response pages. These two models are both compared with a rule-based method. The experiment results indicate that the domain model yields a precision6.44% higher than that of the rulebased method, whereas the block importance model yields an F1 measure 10.5% higher thanthat of the XPath method.
引用
收藏
页码：777 / 782
页数：6
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