document: Web data extraction based on partial tree alignment This paper studies the problem of extracting data from a Web page that contains several structured data records . The objective is to segment these data records , extract data items\/fields from them and put the data in a database table . This problem has been studied by several researchers . However , existing methods still have some serious limitations . The first class of methods is based on machine learning , which requires human labeling of many examples from each Web site that one is interested in extracting data from . The process is time consuming due to the large number of sites and pages on the Web . The second class of algorithms is based on automatic pattern discovery . These methods are either inaccurate or make many assumptions . This paper proposes a new method to perform the task automatically . It consists of two steps , ( 1 ) identifying individual data records in a page , and ( 2 ) aligning and extracting data items from the identified data records . For step 1 , we propose a method based on visual information to segment data records , which is more accurate than existing methods . For step 2 , we propose a novel partial alignment technique based on tree matching . Partial alignment means that we align only those data fields in a pair of data records that can be aligned ( or matched ) with certainty , and make no commitment on the rest of the data fields . This approach enables very accurate alignment of multiple data records . Experimental results using a large number of Web pages from diverse domains show that the proposed two-step technique is able to segment data records , align and extract data from them very accurately .
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