Automated Selection of Web Form Text Field Values Based on Bayesian Inferences

Abstract

The deep web is comprised of a large corpus of information hidden behind the searchable web interfaces. Accessing content through searchable interfaces is somehow a challenging task. One of the challenges in accessing the deep web is automatically filling the searchable web forms for retrieving the maximum number of records by a minimum number of submissions. The paper proposes a methodology to improve the existing method of getting informative data behind searchable forms by automatically submitting web forms. The form text field values are obtained through Bayesian inferences. Using Bayesian networks, the authors aim to infer the values of text fields using the existing values in the label value set (LVS) table. Various experiments have been conducted to measure the accuracy and computation time taken by the proposed value selection method. It proves to be highly accurate and takes less computation time than the existing term frequency-inverse document frequency (TF-IDF) method, hence increasing the performance of the crawler.