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Information extraction for deep web using repetitive subject pattern


In this paper, we propose an information extraction (IE) system for extracting data records from semi-structured documents on the Deep Web using a promising proposed technique, called Repetitive Subject Pattern. This technique was based on the hypothesis that data records in the web page must have a subject item, and the repetitive pattern of the subject items can be used to identify the boundary of data records. The system consists of four automatic tasks: (1) parsing a sample page to a DOM tree, (2) recognizing a subject string in the DOM tree, (3) using the subject string for identifying the pattern of data records and generating a wrapper, and (4) using the generated wrapper for extracting data records. This approach enables the very flexible wrapper generator; when the automatic process generated the wrong wrapper, user can also provide a new sample subject string for generating better wrapper. As the result, the system can be both semi-supervised and unsupervised system. The experimentation shows that the proposed technique provides the outstanding results in generating the very high quality wrappers, with both recall and precision close to 100 % when tested on a number of datasets.

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Correspondence to Wachirawut Thamviset.

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Thamviset, W., Wongthanavasu, S. Information extraction for deep web using repetitive subject pattern. World Wide Web 17, 1109–1139 (2014).

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  • Information  extraction
  • Web  data extraction
  • Web content mining
  • Subject pattern
  • Wrapper induction
  • Unsupervised learning