@conference {citeulike:2526245,
	title = {FiVaTech: Page-Level Web Data Extraction from Template Pages},
	booktitle = {International Workshop on Data Mining in Web 2.0 Environments },
	year = {2007},
	note = {<p>n/a</p>
},
	month = {28 October},
	pages = {15{\textendash}20},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = {Omaha, USA},
	abstract = {<p>In this paper, we proposed a new approach, called FiVaTech for the problem of Web data extraction. FiVaTech is a page-level data extraction system which deduces the data schema and templates for the input pages generated from a CGI program. FiVaTech uses tree templates to model the generation of dynamic Web pages. FiVaTech can deduce the schema and templates for each individual Deep Web site, which contains either singleton or multiple data records in one Web page. FiVaTech applies tree matching, tree alignment, and mining techniques to achieve the challenging task. The experiments show an encouraging result for the test pages used in many state-of-the-art Web data extraction works.</p>
},
	keywords = {information\_extraction},
	isbn = {0-7695-3033-8},
	doi = {10.1109/icdmw.2007.46},
	url = {http://dx.doi.org/10.1109/icdmw.2007.46},
	attachments = {https://scholar.cu.edu.eg/sites/default/files/shaalan/files/chang_fivatech.pdf},
	author = {Mohammed Kayed and Chia-hui Chang and Khaled Shaalan and Moheb R. Girgis}
}
