Research On Technology Of Deep Web Schema Matching

A large number of data sources on the Web are only accessible through their query interfaces. These sources are commonly known as Deep Web sources. For any domain of interest, there may be many such sources with varied query capabilities and content coverage. As a result, users frequently need to access multiple sources in order to find the desired information, which can be a very time-consuming and labor-expensive process. To address this problem, an effective solution is to build an integration system over the sources. Such a system provides uniform accesses for users.Schema Matching is prerequisite to data integration. This paper firstly focuses on technology of Deep Web schema matching, and proposes a new method according to existed methods, then integrates query interfaces using matching results. Finally Deep Web object-level query interfaces integration archetypal system is presented. The main work is summarized as follows:(1) Firstly, introduce relevant knowledge of Deep Web and its related research work, and then expatiate the concept and principle of schema matching, and compare traditional methods and current popular methods.(2) Study on Deep Web schema matching, then propose a new holistic schema matching method based on data mining, named as Correlated-clustering, which mines positively correlated attributes to form potential attribute groups, and finds synonym attributes by clustering.(3) The domain-specific global schema is constructed according to matching results, data types and value domain, which provides uniform query interfaces for users.(4) Design a Deep Web object-level query interfaces integration system according to above technology.Finally we design experiments to implement mentioned algorithms and technology. Experimental results demonstrate our solution yields a higher accuracy and efficiency.