Introduction to Searching the Internet
If you are a new Internet user, you may only know of a limited number of Web sites, perhaps just those that were programmed into your Web browser program, or you may just type in the URLs (Web addresses)
that you see at the bottom of advertisements.
You may use the What's New or Best of the Web buttons, or you get recommendations from friends and relatives.
Perhaps you have never strayed from your company's, organization's, or Internet provider's Web site.
Even if you are a regular Internet user, you may limit your searching to the first directory or search engine you come across or just those to which your browser links.
The purpose of this module is to provide you with an understanding of the breadth of information available on the Internet. You will accomplish this by exploring the different motivations for searching the Internet and by looking over a brief survey of
Internet information retrieval tools.
After you have completed this module, you should have a thorough understanding of the differences between search engines, Web directories, and metasearch engines. You will be ready to move on to the basic procedures of searching the Internet.
After completing this module, you will be able to
- List various reasons people search the Internet
- Describe the different categories of information retrieval services
- Identify information on a search results page
- Explain why you need a search strategy
Search Engine Data Processing
Underlying this enormous data processing task is the complex nature of the task itself. One of the most important things to understand about search engines are the spiders used to visit all the web pages across the internet.
Software programs are only as smart as the algorithms used in implementing them, and although artificial intelligence is being increasingly used in those algorithms, web crawling programs still do not have the adaptive intelligence of human beings.
However, with the advent of Deep Learning, machine learning algorithms are able to take on the thought processes of human beings.
Software programs cannot adequately interpret each of the various types of data that humans can. However, in the case of convolutional neural networks, deeplearning is able to recognize and classify images.
For example, images are to a certain extent less readable by a search engine crawler than they are through the eyes of humans. These are not their only limitations.This module will explore some of their shortcomings in more detail.
Of course, search technology is an ever-changing landscape and machine learning algorithms are starting to be used to improve search engine results. The search engines continuously invest in improving their ability to better process the content of web pages.
For example, advances in image and video search have enabled search engines to approach closer to human-like understanding.