Dynamic Non-Cooperative Structured Deep Web Selection

Abstract:

Most of structured deep web data sources are non-cooperation, therefore establish an accurate data source content summary by sampling is the core technology of data source selection. The content of deep web data source is updated from time to time, however existing efficient methods of non-cooperation structured data source selection does not consider summary update problem. Unrenewed summary of data source can not accurately characterize the content of the data source that produce a greater impact on data source selection. Base on this, we propose a dynamic data source selection method for non-cooperative structured deep web by combining subject headings sampling technology and subject headings extension technology. The experiment results show that our dynamic structured data source selection method has good recall ratio and precision besides being efficient.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 2911-2914
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.2911
• Online since: September 2014
• Authors: Song Deng*
• Keywords: Data Source Selection, Dynamic, Non-Cooperative, Structured Deep Web

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