Generally, the database websites have provided the interfaces for giving users access their structured data. These data are usually represented in a form of data records in a coherent region of a result page. However, the page usually contains not only the data region, but also other extraneous ones. Therefore, the important tasks for extracting data records from these semistructured web pages are identifying the relevant data regions and ignoring the irrelevant regions. To figure out the stated problem, This paper proposes a region extractor to be a preprocessor tool for helping an information extractor to locate and extract the relevant data records from web pages. Most existing works analyze the DOM tree of an input page in a top-down manner. In another way, the proposed method traverses the DOM tree in the bottom-up direction that the similarity of the leaf nodes are analyzed prior to find a set of data items. After that, their parent nodes are recursively analyzed for identifying data records and data regions respectively. The proposed method is completely unsupervised and maintenance-free wrapper. For performance evaluation, it is empirically tested on 15 real-world websites. Experiments show that the proposed method achieves 94.37% accuracy of data record extraction and outperforms the wellknown top-down method, DEPTA (55.39%).
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