Information Retrieval in the Hidden Web

Author:

Ahmed Shakeel1ORCID,Sharma Shubham2,Yadav Saneh Lata3ORCID

Affiliation:

1. King Faisal University, Saudi Arabia

2. Tata Consultancy Services, India

3. K. R. Mangalam University, India

Abstract

Information retrieval is finding material of unstructured nature within large collections stored on computers. Surface web consists of indexed content accessible by traditional browsers whereas deep or hidden web content cannot be found with traditional search engines and requires a password or network permissions. In deep web, dark web is also growing as new tools make it easier to navigate hidden content and accessible with special software like Tor. According to a study by Nature, Google indexes no more than 16% of the surface web and misses all of the deep web. Any given search turns up just 0.03% of information that exists online. So, the key part of the hidden web remains inaccessible to the users. This chapter deals with positing some questions about this research. Detailed definitions, analogies are explained, and the chapter discusses related work and puts forward all the advantages and limitations of the existing work proposed by researchers. The chapter identifies the need for a system that will process the surface and hidden web data and return integrated results to the users.

Publisher

IGI Global

Reference23 articles.

1. An Architectural Framework of a Crawler for Locating Deep Web Repositories Using Learning Multi-agent Systems

2. Anuradha & Juneja. (2015). Dynamic Query Processing for Hidden Web Data Extraction. 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 1352-1356.

3. White Paper: The Deep Web: Surfacing Hidden Value

4. Bhatia, Chaudhary, & Dey. (2020). Opinion Mining in Information Retrieval. Springer Brief.

5. A Comparative Study of Opinion Summarization Techniques

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