Nowadays, most popular methods for web data extraction (WDE) are top-down ones depending on structure. However, these techniques are not scalable enough when coming to complex pages. Consequently, we put forward a bottom-up approach for WDE based on entity recognition and integration to avoid over dependency to structure of web pages. The approach proposed focuses on primary text sequences labeling first and also gives consideration to repetitive patterns of them as well. We propose a Two-Level extraction model for entity recognition and repetitive pattern extraction algorithm for entity integration. Our approach can effectively reduce the attribute labeling mistakes. Also, we demonstrate our approach by scientifically experimental results. The conclusion is that our approach perform better than the traditional extraction techniques, especially on complex Web pages.

Because the amount of information contained on the Deep Web is much larger than the surface web, how to use it well has become a popular problem to research. When a query is sent to a deep web resource and the data sources return few results or even no result, a proper query relaxation solution should be adopted to get more satisfactory results to users. In this paper, such a query relaxation solution is presented. First, it solves the problem of relaxing attributes which contain multiple key words by value. That is, such attributes are not simply removed in the relaxation, but the query values of the attributes are modified. Second, when a data source returns many result pages, instead of getting all the pages, it evaluates the quality of the results in the current page to decide whether to send another query to fetch the next page. Thus, the number of query times is reduced. Finally, the experimental results demonstrate that both the result quality and the query efficiency are improved.