Internet Research Two
How Search Engines Rank Pages &
Constructing Complex Searches

How do Search Engines Crawl?

Gathering data from the Web is like browsing:
1. Visit a page.
2. Record all the words on the page
3.
Choose a link you haven’t seen/recorded
4. Click on the link.
Repeat 8 billion times.

Crawling the Web

One person with a Web browser, following one link per second.

How long does it take to browse the surface Web (8 billion pages)?
8 billion seconds = 133 million minutes
= 2 million hours
= 93 thousand days
= 256 years

Crawling the Web

How many people would it take to crawl the surface Web in a week? If each person follows one link per second (with no sleep):
One week = six hundred thousand seconds
Six hundred thousand / eight billion = thirteen thousand

Challenges:
 Remembering where you’ve been
 Remembering where you haven’t been

Storing all the data

A (small) Server Farm

The Deep Web

Not all pages get crawled:
 Private pages on Intranets (company networks)
 Pages that people don’t want crawled
 Dynamic content pages (from databases)

Dynamic content pages make the size of the
Internet infinite!

Dynamic Content Example
 zillow.com
 Won’t be indexed

Identifying High Quality Web
Pages

Google has ranked billions of Web pages by "quality".

You enter your search terms:
UNC Charlotte HCI

Google finds the highest quality page associated with these search terms.

Google Pagerank
Pretend you're surfing the Web randomly.
To move from page to page you could:
1) type in an address ( www.
sis.uncc.edu
) includes using a bookmark
OR
2) follow a link.
Pagerank measures how likely you are to reach a particular page through random surfing (either 1 or 2).
The main idea is that links to your page from important web pages indicate that your page is important.

Computing Pagerank
(what’s the probability of getting to this page?)
A, B, C, ...
L(A), L(B), L(C),...
= Pages pointing to Q
= number of links on each page
Pagerank of Q :
R(Q) = ( 1-d ) + d · (R(A)/L(A) + R(B)/L(B) + ...) d represents the relative chance of following a link to page Q and 1-d represents the relative chance of going directly to page Q (via typing in the address or using a bookmark):
Usually these are: d = 0.9 ( 1-d ) = 0.1

Computing Pagerank
Pretend the Web has only four pages:
W X Y Z
Links:
W

X Y

W Y

Z Z

W
L(W) =1 L(X) =0 L(Y) =2 L(Z) =1
Which page has the highest “quality”?

Computing Pagerank
Links: W

X Y

W Y

Z Z

W
L(W) =1 L(X) =0 L(Y) =2 L(Z) =1
R(W) = (1-d) + d * (R(Y)/L(Y) + R(Z)/L(Z))
= 0.1 + 0.9 * (R(Y)/ 2 + R(Z)/ 1 ))
R(X) = 0.1 + 0.9 * R(W)
R(Y) = 0.1
R(Z) = 0.1 + 0.9 * (R(Y)/ 2 )
Now, solve for:
R(W), R(X), R(Y),
R(Z)

Computing Values for R(W), R(X), R(Y) and R(Z)
We could use algebra to find the values, in the same way we could solve for x and y in: x = 1 + 2 x + y y = 2 + x + 3 y

Algebraic Solution w = R(W) x = R(X) y = R(Y) z = R(Z) w = 0.1 + 0.45
y + 0.9
z x = 0.1 + 0.9
w y = 0.1
w = 0.2775
x = 0.34795
y = 0.1
z = 0.1 + 0.45
y z = 0.145
But solving for eight billion variables is hard.
Instead, we'll use fixed point iteration .

Solution by Fixed-Point Iteration
Start with initial estimates of PageRank for each page:
R(W) = 1.0 R(X) = 1.0
R(Y) = 1.0
R(Z) = 1.0
Apply equations to compute new estimates: new R(W) = 0.1 + 0.9 * (R(Y) /2 + R(Z))
= 0.1 + 0.9 * ( 1.0
/2 + 1.0
)
= 1.45
new R(X) = 0.1 + 0.9 * R(W) = 0.1 + 0.9 * 1.0
= 1.0
new R (Y) = 0.1
new R(Z) = 0.1 + 0.9 * (R(Y)/ 2 ) = 0.1 + 0.9 * ( 1.0
/2) = 0.55

Solution by Fixed-Point Iteration
Start with updated estimates:
R(W) = 1.45 R(X) = 1.0
R(Y) = 0.1
R(Z) = 0.55
Apply equations to compute new estimates: new R(W) = 0.1 + 0.9 * (R(Y) /2 + R(Z))
= 0.1 + 0.9 * ( 0.1
/2 + 0.55
)
= 0.64
new R(X) = 0.1 + 0.9 * R(W) = 0.1 + 0.9 * 1.45
= 1.405
new R (Y) = 0.1
new R(Z) = 0.1 + 0.9 * (R(Y)/ 2 ) = 0.1 + 0.9 * ( 0.1
/2) = 0.145

Solution by Iteration iteration R(W) R(X) R(Y) R(Z)
0 1.00000 1.00000 1.00000 1.00000
1 1.45000 1.00000 0.10000 0.55000
2 0.64000 1.40500 0.10000 0.14500
3 0.27550 0.67600 0.10000 0.14500
4 0.27550 0.34795 0.10000 0.14500
5 0.27550 0.34795 0.10000 0.14500
... ... ... ...
Compute new estimates from the old until the estimates stop changing. Note that this is the same answer as the traditional algebraic approach, but this way scales better.

Final Pageranks highest page X R(X) = 0.34795
page W R(W) = 0.2755
.
.
.
page Z R(Z) = 0.14500
lowest page Y R(Y) = 0.10000

Final Pageranks
0.14500
0.10000
Y
2
Z
1
W X
1 0
0.27550
0.34795

How does Google Use
Pagerank?
 You enter search terms, such as “UNC
Charlotte HCI”

Google finds all the pages that have all those words on them

Of all those pages, Google will list the ones with the highest page rank first, but…
 …other ‘magic ingredients’ are used by
Google: trade secrets of their algorithms.

Introduction

Basic queries are somewhat limited
 One or two keywords
 Simple relationships
 Limited syntax

Complex queries provide more power
 Keywords & phrase can be connected to form more complex relationships
 Search filters can be employed to limit results

Understanding Boolean Operators

Syntax
 Rules for combining simple words to form complex sentences

Search engine syntax implemented by applying Boolean logic

George Boole
 1815-1864

Understanding Boolean Operators

Boolean logic
 Keywords act as nouns
 Boolean operators act as conjunctions

They define the connections between keywords
 Illustrated with Venn diagrams

John Venn

1834-1923

Understanding Boolean Operators
All web pages containing the word cats cats
W W W

Understanding Boolean Operators
All web pages containing the word dogs dogs
W W W

Understanding Boolean Operators
All web pages containing the words cats and dogs
Searches containing both words cats dogs
Intersection of the two sets
W W W

Understanding Boolean Operators
All web pages containing the words cats or dogs
Searches containing either word cats dogs
Union of the two sets
W W W

Understanding Boolean Operators
All web pages containing the words cats and not dogs
Searches containing one word but not the other cats dogs
Exclusion of the dogs set
W W W

Understanding Boolean Operators
All web pages containing the words dogs and not cats
Searches containing one word but not the other cats dogs
Exclusion of the cats set
W W W

Understanding Boolean Operators

Boolean operators
 AND
 OR
 NOT

Instruct the engine on how to combine keywords to produce results

Always use capital letters to avoid confusion with and, or, not as keywords

Understanding Boolean Operators

AND
 All these keywords must be on the Web page

OR
 These keywords may or may not be on the
Web page
 At least one of them must be

NOT
 None of these keywords can be on the Web page

Understanding Boolean Operators

Default operator
 Some engines have a default Boolean operator
 Usually AND
 Might be OR

Some engines may search for multiple words as phrases

Understanding Boolean Operators

Boolean operators may be
 Allowed on main page
 Confined to Advanced search pages

Some engines use symbols instead
 + for AND
 - for NOT
 No space between sign and word:

+solar +energy -windmill

Narrowing Searches with AND

AND
 Limits results
 Forces inclusion of a stop word

Indicates that all keywords must be found on Web page

Adding more ANDed keywords limits search more

Results should be more relevant because the keyword list has expanded

Narrowing Searches with AND

Example:
 “solar energy association” AND Portland
Solar energy association
Portland
W W W

Narrowing Searches with AND

Example:
 Henry +I same as “Henry I”
Henry I
W W W

Expanding Searches with OR

OR expands results
 Useful if you didn’t get enough returns from your first search
 The more keywords you add, the more results you should get

Every page returned must have at least one of the keywords on it
 Good to use when you have synonyms

Expanding Searches with OR

Example:
 oregon OR northwest oregon northwest
W W W

Restricting Queries with AND NOT

AND NOT excludes the keyword that follows NOT

Limits your search
 Produces fewer results

Useful if first search returns irrelevant results
 Use AND NOT to get rid of those results

Restricting Queries with AND NOT

Equivalent forms:
 cats AND NOT dogs
 cats AND-NOT dogs
 cats NOT dogs
 cats –dogs

Restricting Queries with AND NOT

Example:
 “solar energy association” AND portland
AND NOT maine portland
Solar energy association maine

Multiple Boolean Operators

Boolean operators allow you to focus a search

Any logical combination of operators is allowed

If it makes sense when spoken like a sentence it’s probably OK to use

Order of operations is usually left to right
 Use parentheses to organize terms

Multiple Boolean Operators

Bad example:
 constitution +american OR “united states” constitution american
“united states”

Multiple Boolean Operators

Good example:
 constitution +(american OR “united states”) constitution american
“united states”