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 This
article is about search engines. For a specific search engine, see List of
search engines.
A search engine is an information
retrieval system designed to help find information stored on a computer
system. Search engines help to minimize the time required to find information
and the amount of information which must be consulted, akin to other
techniques for managing information overload.
The most
popular form of a search engine is a Web search engine which searches for
information on the public World Wide Web. Other kinds of search engines
include enterprise search engines, which search on intranets, desktop search
engines, and mobile search engines.
How
Search Engines Work
Querying
 Search engines provide an interface to a group of
items that enables users to specify criteria about an item of interest and
have the engine find the matching items within the group.
In the most
popular form of search, items are documents or web pages and the criteria are
words or concepts that the documents may contain.
There are
several varieties of syntax in which a search engine user can express a
query. Some methods are formalized and require a strict, logical and
algebraic syntax. Other approaches are less strict and allow for a less
defined query. One form of a less-restricted query syntax is referred to as
Natural Language Search, which is a term typically used to describe web
search engines that apply natural language processing of some form. For
example, instead of searching for one or two words, a query could consist of
an English sentence or paragraph. A natural language search engine will then
parse the query into words and evaluate searches for these words. This places
less burden on the search engine user to formulate a
specific query using restrictive, and sometimes difficult to learn, syntax. A
second definition of natural language search engines reflects how the search
engine performs indexing, unrelated to the query syntax. This requires a
semantic understanding of the query in order to disambiguate the text.
Traditional
search engines tend to use a non-linguistic model of language and the
hypothesis is that NLS will provide better results - that is to say, results
that more accurately and efficiently support a user's need.
Ranking
A Boolean
search for an item within a group of items will either return the exact
matching item or nothing. This is a rather orthodox search method where the
equality between the desired item and the actual item must be exact. In
application, it is sometimes far more beneficial and useful to incorporate a
more lax measure of similarity between the desired item(s) and the items that
exist in the group being searched.
For example,
instead of finding only the exact book in a library, a library search engine
may return a list of 'similar' books, with the exact book listed first.
The list of items that meet the criteria specified by the
query are typically sorted, or ranked, in some regard so as to place the most
'relevant' items first. Placing the most relevant items first reduces the
time required by users to determine whether one or more of the resulting
items are sufficiently similar to the query. It has become common knowledge
through the use of Web search engines that the further down the list of
matching items you browse, the less relevant the items become.
Indexing
 To provide a set of matching items quickly, a
search engine will typically collect information, or metadata, about the
group of items under consideration beforehand. For example, a library search
engine may determine the author of each book automatically and add the author
name to a description of each book. Users can then search for books by the author's
name. Other metadata in this example might include the book title, the number
of pages in the book, the date it was published, and so forth.
The metadata
collected about each item is typically stored on a computer in the form of an
index. The index typically requires a smaller amount of computer storage and
provides a way for the search engine to calculate the relevance, or
similarity, between the query and the set of items.
Helpful
Features
1.
Spell checker
2.
Highlighter
 Users can save time in typing correct words with
auto correct options enable/disable. Highlighters such as yellow line markers
help in highlighting certain search item on the results. Could be used for
copying and editing as well.
Predictive
search engines such as study consumer or end user pattern in presenting
results. Variants of search engines present personalized data.
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