Discovery And Classification Of Deep Web Data Sources

In the internet era, the internet information technology is rapidly becoming an important resource for the community. As we all know, when we query the information through the Web, usually we may use some search engines and submit the keywords to search. Though this we only can get some static web pages through these search engines. And the traditional method is difficult to get the Deep Web data resources which have higher value, thereby it reduce the utilization of information.According to the difference between the system response to the users queries, that is, the data is taken from the website of each application or local integration of the database, We design two different methods to query the data. And we distinguish the Deep Web data source discovery search framework into real-time and non-real-time two cases. This paper describes the framework and he corresponding function modules for both models systematically. We design the "data source discovery engine application modules" and "Client data source application module" to communicate with each other. And through this the users can explore the deep web data sources effectively. Facing the huge classify dictionary, in order to ensure the retrieval efficiency, this paper further proposed classification method based on the Tong Yi Ci Colin and the Hownet synonyms fused to classify the website and retrieved the user’s query keywords.The framework approach can effectively overcome the field limitations of traditional methods, and it can make better use of the structural characteristics of Deep Web database, which greatly facilitates the subsequent integration of work and can improve the query efficiency.