2014•Transactions on Machine Learning and Artificial IntelligenceOpen access

Identification of Query Forms for Retrieving the Information From Deep Web

Nripendra Narayan Das, Ela Kumar

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Abstract

Web databases are now present everywhere. The data from the Deep Web can not be accessed by Search engine and web crawlers directly. The only way to access the hidden database is through query interfaces and filling up number of HTML forms for a specific domain [18]. In this paper a technique called QFORT (QUERY FORM RETRIEVAL TECHNIQUE) has been developed for identifying the relevant query form is presented. Retrieving information from deep web pages using wrappers is a fundamental problem arising in a huge range of web pages of vast practical interests. In this paper, we propose a novel technique to the problem of identifying the query forms from Web pages, which is one of the key problems in automatic extraction approach. The problem is resolved by many authors by using different technique Intensive experiments on real web sites show that the proposed technique can effectively help extracting desired data with high accuracies in most of the cases.

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Web databases are now present everywhere. The data from the Deep Web can not be accessed by Search engine and web crawlers directly. The only way to access the hidden database is through query interfaces and filling up number of HTML forms for a specific domain [18]. In this paper a technique called QFORT (QUERY FORM RETRIEVAL TECHNIQUE) has been developed for identifying the relevant query form is presented. Retrieving information from deep web pages using wrappers is a fundamental problem arising in a huge range of web pages of vast practical interests. In this paper, we propose a novel technique to the problem of identifying the query forms from Web pages, which is one of the key problems in automatic extraction approach. The problem is resolved by many authors by using different technique Intensive experiments on real web sites show that the proposed technique can effectively help extracting desired data with high accuracies in most of the cases.

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Available abstract

Web databases are now present everywhere. The data from the Deep Web can not be accessed by Search engine and web crawlers directly. The only way to access the hidden database is through query interfaces and filling up number of HTML forms for a specific domain [18]. In this paper a technique called QFORT (QUERY FORM RETRIEVAL TECHNIQUE) has been developed for identifying the relevant query form is presented. Retrieving information from deep web pages using wrappers is a fundamental problem arising in a huge range of web pages of vast practical interests. In this paper, we propose a novel technique to the problem of identifying the query forms from Web pages, which is one of the key problems in automatic extraction approach. The problem is resolved by many authors by using different technique Intensive experiments on real web sites show that the proposed technique can effectively help extracting desired data with high accuracies in most of the cases.

Key concepts: Computer science, Web query classification, Web search query, Information retrieval, Deep Web, Web page, Query expansion, Web crawler

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