Deep Web crawling: a survey

Challenges in Crawling the Deep Web

Efficient Deep Web Crawling Using Reinforcement Learning

Deep web refers to the hidden part of the Web that remains unavailable for standard Web crawlers. To obtain content of Deep Web is challenging and has been acknowledged as a significant gap in the coverage of search engines. To this end, the paper proposes a novel deep web crawling framework based on reinforcement learning, in which the crawler is regarded as an agent and deep web database as t...

Crawling Deep Web Content through Query Forms

This paper proposes the concept of Minimum Executable Pattern (MEP), and then presents a MEP generation method and a MEP-based Deep Web adaptive query method. The query method extends query interface from single textbox to MEP set, and generates local-optimal query by choosing a MEP and a keyword vector of the MEP. Our method overcomes the problem of “data islands” to a certain extent which res...

Crawling Deep Web Using a New Set Covering Algorithm

Crawling the deep web often requires the selection of an appropriate set of queries so that they can cover most of the documents in the data source with low cost. This can be modeled as a set covering problem which has been extensively studied. The conventional set covering algorithms, however, do not work well when applied to deep web crawling due to various special features of this applicatio...

A Task-specific Approach for Crawling the Deep Web

There is a great amount of valuable information on the web that cannot be accessed by conventional crawler engines. This portion of the web is usually known as the Deep Web or the Hidden Web. Most probably, the information of highest value contained in the deep web, is that behind web forms. In this paper, we describe a prototype hidden-web crawler able to access such content. Our approach is b...