Search Engine Demystified

Search Engines
Sudarsun Santhiappan., M.Tech.,
Director – R & D,
Burning Glass Technologies

Copyleft ( ) 2009 Sudarsun Santhiappanɔ 2
Today's Coverage
● Introduction
● Types of Search Engines
● Components of a Search Engine
● Semantics and Relevancy
● Search Engine Optimization

What is a Search Engine ?
● What is a Search ?
● Why do we need a Search Engine ?
● What are we searching against ?
● How good is a Search Engine ?
● What is Search on Search (Meta SE) ?
● Compared Search Engines Side-by-Side ?
● How are Images and Videos searched ?
● Apart from Web Search, what else ?

Introduction
● Web Search Engine is a software program that searches
the Internet (bunch of websites) based on the words
that you designate as search terms (query words).
● Search engines look through their own databases of
information in order to find what it is that you are
looking for.
● Web Search Engines are a good example for massively
sized Information Retrieval Systems.
– Tried “Similar pages” Link in Google result set ?

Dictionary Definitions
Search
COMPUTING (transitive verb) to examine a computer file, disk,
database, or network for particular information
Engine
something that supplies the driving force or energy to a movement,
system, or trend
Search Engine
a computer program that searches for particular keywords and
returns a list of documents in which they were found, especially a
commercial service that scans documents on the Internet

About definition of search engines
● oh well … search engines do not search
only for keywords, some search for other
stuff as well
● and they are really not “engines” in the
classical sense
– but then mouse is not a “mouse”

use of search engines
… among others

Types of Search Engines
● Text Search Engines
– General: AltaVista, AskJeeves, Bing, Google
– Specialized: Google Scholar, Scirus, Citeseer
● Intranet vs Internet Search Engines
● Image Search Engines
– How can we search on the Image content ?
● Video Search Engines
– Image Search with Time dimension !!

Types of Search Engine
● Crawler Powered Indexes
– Guruji.com, Google.com
● Human Powered Indexes
– www.dmoz.org
● Hybrid Models
– Submitted URLs to a search engine ?
● Semantic Indexes
– Hakia.com,

Have you tried Hakia ?
● What is Semantic Search ?
● How's it different from Keyword Search?
● What is categorized search ?
● Side-by-Side comparison with Google!!
● Have you compared Bing with Google ?

Directories
● www.dmoz.org
● Website classified into a Taxonomy
● Website are categorically arranged
● Searching vs Navigation
● Instead of Query, you Click and navigate
● Accurate search always! (if data is available)
● Problem: Mostly Manually created

How does a Search Engine work ?

Your
Browser
How Search Engines Work
(Sherman 2003)
The Web
URL1
URL2
URL3 URL4
Crawler
Indexer
Search
Engine
Database Eggs?
Eggs.
Eggs - 90%
Eggo - 81%
Ego- 40%
Huh? - 10%
All About
Eggs
by
S. I. Am

how do search engines work? elaboration
• crawlers, spiders: go out to find content
– in various ways go through the web looking
for new & changed sites
– periodic, not for each query
● no search engine works in real time
– some search engines do it for themselves,
others not
● buy content from companies such as Inktomi
– for a number of reasons crawlers do not
cover all of the web – just a fraction
– what is not covered is “invisible web” ?

Elaboration …
• organizing content: labeling, arranging
– indexing for searching – automatic
● keywords and other fields
● arranging by URL popularity - PageRank as Google
– classifying as directory
● mostly human handpicked & classified
● as a result of different organization we have
basically two kinds of search engines:
● search – input is a query that is searched & displayed
● directory – classified content – a class is displayed
– and fused: directories have search capabilities & vice versa

Elaboration (cont.)
• databases, caches: storing content
– humongous files usually distributed over many computers
• query processor: searching, retrieval, display
– takes your query as input
● engines have differing rules on how they are handled
– displays ranked output
● some engines also cluster output and provide visualization
● some engines provide categorically structured results
● at the other end is your browser

Similarities & Differences
● All search engines have these basic parts in
common
● BUT the actual processes – methods how they
do it – are based on various algorithms and
they significantly differ
– most are proprietary (patented) with details kept
mostly secret (or protected) but based on well
known principles from information retrieval or
classification
– to some extent Google is an exception – they
published their method

Google Search
● In the beginning it ran on Stanford computers
● Basic approach has been described in their
famous paper “The Anatomy of a Large-Scale
Hypertextual Web Search Engine”
– well written, simple language, has their pictures
– in acknowledgement they cite the support by NSF’s Digital Library
Initiative i.e. initially, Google came out of government sponsored research
– describe their method PageRank - based on ranking hyperlinks as in
citation indexing
– “We chose our system name, Google, because it is a common spelling of
googol, or ten on hundredth power”

coverage differences
● no engine covers more than a fraction of WWW
– estimates: none more than 16%
– hard (even impossible) to discern & compare coverage, but they differ
substantially in what they cover
● in addition:
– many national search engines
● own coverage, orientation, governance
– many specialized or domain search engines
● own coverage geared to subject of interest
– many comprehensive sources independent of search engines
● some have compilations of evaluated web sources

searching differences
● substantial differences among search engines
on searching, retrieval display
– need to know how they work & differ in respect to
● defaults in searching a query
● searching of phrases, case sensitivity, categories
● searching of different fields, formats, types of resources
● advance search capabilities and features
● possibilities for refinement, using relevance feedback
● display options
● personalization options

Limitations
● every search engine has limitation as to
– Coverage: meta engines just follow coverage
limitations & have more of their own search
capabilities
– finding quality information
● some have compromised search with economics
– becoming little more than advertisers
● but search engines are also many times victims
of spamdexing
– affecting what is included and how ranked

Spamming a search engine
● use of techniques that push rankings higher
than they belong is also called spamdexing
– methods typically include textual as well as link-
based techniques
– like e-mail spam, search engine spam is a form of
adversarial information retrieval
● the conflicting goals of accurate results of search
providers & high positioning by content page rank

Meta Search Engines
Search on Search

Meta search engines
● meta engines search multiple engines
– getting combined results from a variety of
engines
● do not have their own databases
– but have their own business models affecting
results
● a number of techniques used
– interesting ones: clustering, statistical analysis

Some Meta engines - with organized results
Dogpile : results from a number of leading
search engines; gives source, so overlap can
be compared; (has also a (bad) joke of the day)
Surfwax : gives statistics and text sources &
linking to sources; for some terms gives related
terms to focus
Teoma : results with suggestions for narrowing;
links resources derived; originated at Rutgers
Turbo10 : provides results in clusters; engines
searched can be edited

Some Meta Engines (cont.)
● Large directory
 Complete Planet
– directory of over 70,000 databases & specialty engines
● Results with graphical displays
– Vivisimo clusters results; innovative
– Webbrain results in tree structure – fun to use
– Kartoo results in display by topics of query

Domain Specific Search Engines

Domain Search Engines &
Catalogs
● cover specific subjects & topics
● important tool for subject searches
– particularly for subject specialist
– valued by professional searchers
● selection mostly hand-picked rather than
by crawlers, following inclusion criteria
– often not readily discernable
– but content more trustworthy

Domain Search Engines …
Open Directory Project
large edited catalog of the web – global, run by volunteers
BUBL LINK
selected Internet resources covering all academic subject
areas; organized by Dewey Decimal System – from UK
Profusion
search in categories for resources & search
engines
Resource Discovery Network – UK
“UK's free national gateway to Internet
resources for the learning, teaching and
research community”

Domain Engines … sample
Think Quest – Oracle Education Foundation
• education resources, programs; web sites created by students
All Music Guide
• resource about musicians, albums, and songs
Internet Movie Database
• treasure trove of American and British movies
Genealogy links and surname search engines
well.. that is getting really specialized (and popular)
Daypop
searches the “living web” “The living web is composed of sites that update on a
daily basis: newspapers, online magazines, and weblogs”

Science, scholarship engines …sample
 Psychcrawler - Amer Psychological Association
 web index for psychology
 Entrez PubMed – Nat Library of Medicine
biomedical literature from MEDLINE & health journals
 CiteSeer - NEC Research Center
 scientific literature, citations index; strong in computer science
Scholar Google
searches for scholarly articles & resources
Infomine
scholarly internet research collections
Scirus
scientific information in journals & on the web

Science, scholarship engines …sample
commercial access
● an addition to freely accessible engines many
provide search free but access to full text paid
– by subscription or per item
– RUL provides access to these & many more:
ScienceDirect Elsevier: “world's largest electronic collection of science, technology
and medicine full text and bibliographic information”
ACM Portal Association for Computing Machinery: access to ACM Digital Library &
Guide to Computing

Search Engine Internals

Search Engine Internals
● Crawlers
● Indexers
● Searching
● Semantics
● Ranking

Standard Web Search Engine Architecture
crawl the
web
create an
inverted
index
Check for duplicates,
store the
documents
Inverted
index
Search
engine
servers
user
query
Show results
To user
DocIds

Typical Search Engine

Crawlers
● What is Crawling ?
● How does Crawling happen ?
● Have you tried “wget -r <url>” in Linux ?
● Have you tried “DAP” to download entire site?
● Page Walk
● Spidering & Crawlbots

Spidering the Web
● Replicating the Spider's behavior of building
the Internet (web) by adding spirals (sites)
● But, can the web be fully crawled ?
● By the time, one round of indexing is over, the
page might have changed already!
● That's why we have cached page link in the
search result!

Crawler Bots
● How to make your website Crawlable ?
● White-listing and Black-listing!
● Meta Tags to control the Bots
● Can HTTPS pages be crawled ?
● Does Sessions maintained while crawling ?
● Can dynamic pages be crawled ?
● URL normalization
– cool.com?page=2 [crawler unfriendly]
– cool.com/page/2 [norm'd and crawler friendly]

How to control Robots ?
<HTML>
<HEAD>
<META NAME="ROBOTS" CONTENT="NOINDEX,NOFOLLOW">
<TITLE>...</TITLE>
</HEAD>
<BODY>
Index: This tell the spider/bot that it’s OK to index this page
Noindex: Spider/bot see this and don’t index any of the content on this page.
Follow: This let the spider/bot know that it’s OK to travel down links found on this
page.
Nofollow: It tells the spider/bot not to follow any of the links on this page.

Crawling – Process Flow

Data Structures
● Tree primarily while Crawling
● Both Depth-First-Search and Breadth-First-
Search are used
● Every page that the crawler visits shall be
added as a node to the Tree
● Fan-out information is represented as
Children for a node (page).

Inverted Indexes the IR Way

How Inverted Files
Are Created
● Periodically rebuilt, static otherwise.
● Documents are parsed to extract
tokens. These are saved with the
Document ID.
Now is the time
for all good men
to come to the aid
of their country
Doc 1
It was a dark and
stormy night in
the country
manor. The time
was past midnight
Doc 2
Term Doc #
now 1
is 1
the 1
time 1
for 1
all 1
good 1
men 1
to 1
come 1
to 1
the 1
aid 1
of 1
their 1
country 1
it 2
was 2
a 2
dark 2
and 2
stormy 2
night 2
in 2
the 2
country 2
manor 2
the 2
time 2
was 2
past 2
midnight 2

How Inverted
Files are Created
● After all
documents have
been parsed the
inverted file is
sorted
alphabetically.
Term Doc #
a 2
aid 1
all 1
and 2
come 1
country 1
country 2
dark 2
for 1
good 1
in 2
is 1
it 2
manor 2
men 1
midnight 2
night 2
now 1
of 1
past 2
stormy 2
the 1
the 1
the 2
the 2
their 1
time 1
time 2
to 1
to 1
was 2
was 2
Term Doc #
now 1
is 1
the 1
time 1
for 1
all 1
good 1
men 1
to 1
come 1
to 1
the 1
aid 1
of 1
their 1
country 1
it 2
was 2
a 2
dark 2
and 2
stormy 2
night 2
in 2
the 2
country 2
manor 2
the 2
time 2
was 2
past 2
midnight 2

How Inverted
Files are Created
● Multiple term
entries for a
single
document are
merged.
● Within-
document term
frequency
information is
compiled.
Term Doc # Freq
a 2 1
aid 1 1
all 1 1
and 2 1
come 1 1
country 1 1
country 2 1
dark 2 1
for 1 1
good 1 1
in 2 1
is 1 1
it 2 1
manor 2 1
men 1 1
midnight 2 1
night 2 1
now 1 1
of 1 1
past 2 1
stormy 2 1
the 1 2
the 2 2
their 1 1
time 1 1
time 2 1
to 1 2
was 2 2
Term Doc #
a 2
aid 1
all 1
and 2
come 1
country 1
country 2
dark 2
for 1
good 1
in 2
is 1
it 2
manor 2
men 1
midnight 2
night 2
now 1
of 1
past 2
stormy 2
the 1
the 1
the 2
the 2
their 1
time 1
time 2
to 1
to 1
was 2
was 2

How Inverted Files are Created
● Finally, the file can be split into
– A Dictionary or Lexicon file
and
– A Postings file

How Inverted Files are Created
Dictionary/Lexicon
Postings
Term Doc # Freq
a 2 1
aid 1 1
all 1 1
and 2 1
come 1 1
country 1 1
country 2 1
dark 2 1
for 1 1
good 1 1
in 2 1
is 1 1
it 2 1
manor 2 1
men 1 1
midnight 2 1
night 2 1
now 1 1
of 1 1
past 2 1
stormy 2 1
the 1 2
the 2 2
their 1 1
time 1 1
time 2 1
to 1 2
was 2 2
Doc # Freq
2 1
1 1
1 1
2 1
1 1
1 1
2 1
2 1
1 1
1 1
2 1
1 1
2 1
2 1
1 1
2 1
2 1
1 1
1 1
2 1
2 1
1 2
2 2
1 1
1 1
2 1
1 2
2 2
Term N docs Tot Freq
a 1 1
aid 1 1
all 1 1
and 1 1
come 1 1
country 2 2
dark 1 1
for 1 1
good 1 1
in 1 1
is 1 1
it 1 1
manor 1 1
men 1 1
midnight 1 1
night 1 1
now 1 1
of 1 1
past 1 1
stormy 1 1
the 2 4
their 1 1
time 2 2
to 1 2
was 1 2

Inverted indexes
● Permit fast search for individual terms
● For each term, you get a list consisting of:
– document ID
– frequency of term in doc (optional)
– position of term in doc (optional)
● These lists can be used to solve Boolean queries:
● country -> d1, d2
● manor -> d2
● country AND manor -> d2
● Also used for statistical ranking algorithms

Inverted Indexes for Web Search
Engines
● Inverted indexes are still used, even though
the web is so huge.
● Some systems partition the indexes across
different machines. Each machine handles
different parts of the data.
● Other systems duplicate the data across
many machines; queries are distributed
among the machines.
● Most do a combination of these.

From description of the FAST search engine, by Knut Risvik
In this example, the data
for the pages is
partitioned across
machines. Additionally,
each partition is allocated
multiple machines to
handle the queries.
Each row can handle 120
queries per second
Each column can handle
7M pages
To handle more queries,
add another row.

Cascading Allocation of CPUs
● A variation on this that produces a cost-
savings:
– Put high-quality/common pages on many
machines
– Put lower quality/less common pages on fewer
machines
– Query goes to high quality machines first
– If no hits found there, go to other machines

The Search Process

Searching – Process Flow

Google Query Evaluation
1. Parse the Query
2. Convert words to WordID
3. Seek to the start of the doclist in the short barrel for every word.
4. Scan through the doclists until there is a document that
matches all the search terms.
5. Compute the rank of that document for the query.
6. If we are in the short barrels and at the end of any doclist, seek
to the start of the doclist in the full barrel for every word and
go to step 4.
7. If we are not at the end of any doclist go to step 4.
8. Sort the documents that have matched by rank and return the
top k.

Queries
● Search engines are one tool used to answer info needs
● Users express their information needs as queries
● Usually informally expressed as two or three words (we
call this a ranked query)
● A recent study showed the mean query length was 2.4
words per query with a median of 2
● Around 48.4% of users submit just one query in a
session, 20.8% submit two, and about 31% submit
three or more
● Less than 5% of queries use Boolean operators (AND,
OR, and NOT), and around 5% contain quoted phrases

Queries...
● About 1.28 million different words were used in queries in
the Excite log studied (which contained 1.03 million
queries)
● Around 75 words account for 9% of all words used in
queries. The top-ten non-trivial words occurring in
531,000 queries are “sex” (10,757), “free” (9,710),
“nude” (7,047), “pictures” (5,939), “university” (4,383),
“pics” (3,815), “chat” (3,515), “adult” (3,385), “women”
(3,211), and “new” (3,109)
● 16.9% of the queries were about entertainment, 16.8%
about sex, pornography, or preferences, and 13.3%
concerned commerce, travel, employment, and the
economy

Answers
What is a good answer to a query?
● One that is relevant to the user’s information need!
● Search engines typically return ten answers-per-page,
where each answer is a short summary of a web
document
● Likely relevance to an information need is approximated
by statistical similarity between web documents and
the query
● Users favour search engines that have high precision,
that is, those that return relevant answers in the first
page of results

Approximating Relevance
● Statistical similarity is used to estimate the relevance of a
query to an answer
● Consider the query “Richardson Richmond Football”
● A good answer contains all three words, and the more
frequently the better; we call this term frequency (TF)
● Some query terms are more important—have better
discriminating power—than others. For example, an
answer containing only “Richardson” is likely to be
better than an answer containing only “Football”; we
call this inverse document frequency (IDF)

Ranking
To improve the accuracy of search engines:
● Google Inc. use their patented PageRank(tm) technology.
Google ranks a page higher if it links to pages that are
an authorative source, and a link from an authorative
source to a page ranks that page higher
● Relevance feedback is a technique that adds words to a
query based on a user selecting a more like this option
● Query expansion adds words to a query using thesaural or
other techniques
● Searching within categories or groups to narrow a search

Resolving Queries
● Queries are resolved using the inverted index
● Consider the example query “Cat Mat Hat”. This is
evaluated as follows:
– Select a word from the query (say, “Cat”)
– Retrieve the inverted list from disk for the word
– Process the list. For each document the word occurs in, add
weight to an accumulator for that document based on the
TF, IDF, and document length
– Repeat for each word in the query
– Find the best-ranked documents with the highest weights
– Lookup the document in the mapping table
– Retrieve and summarize the docs, and present to the user

Fast Search Engines
● Inverted lists are stored in a compressed format. This
allows more information per second to be retrieved
from disk, and it lowers disk head seek times
● As long as decompression is fast, there is a beneficial
trade-off in time
● Documents are stored in a compressed format for the
same reason
● Different compression schemes are used for lists (which
are integers) and documents (which are multimedia,
but mostly text)

Fast Search Engines
● Sort disk accesses to minimise disk head movement
when retrieving lists or documents
● Use hash tables in memory to store the vocabulary; avoid
slow hash functions that use modulo
● Pre-calculate and store constants in ranking formulae
● Carefully choose integer compression schemes
● Organise inverted lists so that the information frequently
needed is at the start of the list
● Use heap structures when partial sorting is required
● Develop a query plan for each query

Search Engine Architecture

Search Engine architecture
● The inverted lists are divided amongst a number of servers,
where each is known as a shard
● If an inverted list is required for a particular range of words,
then that shard server is contacted
● Each shard server can be replicated as many times as
required; each server in a shard is identical
● Documents are also divided amongst a number of servers
● Again, if a document is required within a particular range,
then the appropriate document server is contacted
● Each document server can also be replicated as many times
as required

Google, Case Study

Google Architecture

Components
● URL Server: Bunch of URLs (white-list)
● Crawler: Fetch the page
● Store Server: To store the fetched pages
● Repository: Compressed pages are put here
● Every unique page has a DocID
● Anchor: Page transition [to, from] information
● URLResolver: Relative URL to Absolute URL
● Lexicon: list of known words

Indexer
● Parses the document
● Build Word-Frequency table {word, position, font,
capitalization} [hits]
● Pushes the hits to barrels as partially sorted
forward index
● Identifies anchors (page transition out info)

Searcher
● Forward Index to Inverted Index
– Maps keywords to DocIds
– DocIds mapped to URLs
● Reranker
– Uses Anchor information to rank the pages for
the given query keyword.
– Thumbrule: Fan In increases page rank

Reranking

What about Ranking?
● Lots of variation here
– Often messy; details proprietary and fluctuating
● Combining subsets of:
– IR-style relevance: Based on term frequencies,
proximities, position (e.g., in title), font, etc.
– Popularity information
– Link analysis information
● Most use a variant of vector space ranking to
combine these. Here’s how it might work:
– Make a vector of weights for each feature
– Multiply this by the counts for each feature

Relevance: Going Beyond IR
● Page “popularity” (e.g., DirectHit)
– Frequently visited pages (in general)
– Frequently visited pages as a result of a query
● Link “co-citation” (e.g., Google)
– Which sites are linked to by other sites?
– Draws upon sociology research on
bibliographic citations to identify “authoritative
sources”

Link Analysis for Ranking Pages
● Assumption: If the pages pointing to this
page are good, then this is also a good
page.
● References: Kleinberg 98, Page et al. 98
● Draws upon earlier research in sociology
and bibliometrics.
– Kleinberg’s model includes “authorities” (highly
referenced pages) and “hubs” (pages
containing good reference lists).
– Google model is a version with no hubs, and is
closely related to work on influence weights by
Pinski-Narin (1976).

Link Analysis for Ranking Pages
● Why does this work?
– The official Toyota site will be linked to by lots
of other official (or high-quality) sites
– The best Toyota fan-club site probably also has
many links pointing to it
– Less high-quality sites do not have as many
high-quality sites linking to them

PageRank
● Let A1, A2, …, An be the pages that point
to page A. Let C(P) be the # links out of
page P. The PageRank (PR) of page A is
defined as:
● PageRank is principal eigenvector of the
link matrix of the web.
● Can be computed as the fixpoint of the
above equation.
PR(A) = (1-d) + d ( PR(A1)/C(A1) + … + PR(An)/C(An) )

PageRank: User Model
● PageRanks form a probability distribution over web pages:
sum of all pages’ ranks is one.
● User model: “Random surfer” selects a page, keeps
clicking links (never “back”), until “bored”: then randomly
selects another page and continues.
– PageRank(A) is the probability that such a user visits A
– d is the probability of getting bored at a page
● Google computes relevance of a page for a given search
by first computing an IR relevance and then modifying that
by taking into account PageRank for the top pages.

Search Engine Optimization

● Location, Location, Location...and Frequency
● Tags (<title>, <meta>, <b>, top of the page)
● How close words (from the query) are to each other on the
website
● Quality of links going to and from a page
● Penalization for "spamming“, when a word is repeated
hundreds of times on a page, to increase the frequency and
propel the page higher in the listings.
● Off the Page ranking criteria:
● By analyzing how pages link to each other.
How Search Engines Rank Pages?

Why do results differ ?
● Some search engines index more web pages
than others.
● Some search engines also index web pages
more often than others.
● The result is that no search engine has the
exact same collection of web pages to
search through.
● Different algorithms to compute relevance of
the page to a particular query

Search Engine Placement Tips
● Why is it important to be on the first page of the
results?
– Most users do not go beyond the first page.
● How to optimize your website?
– Pick your target keywords: How do you think people will
search for your web page? The words you imagine them
typing into the search box are your target keywords.
– Pick target words differently for each page on your website.
– Your target keywords should always be at least two or more
words long.

Position your Keywords
● Make sure your target keywords appear in the
crucial locations on your web pages. The page's
HTML <title> tag is most important.
● The titles should be relatively short and attractive.
Several phrases are enough for the description.
● Search engines also like pages where keywords
appear "high" on the page: headline, first
paragraphs of your web page.
● Keep in mind that tables and large JavaScript
sections can make your keywords less relevant
because they appear lower on the page.

Have Relevant Content
● Keywords need to be reflected in the page's content.
● Put more text than graphics on a page
● Don't use frames
● Use the <ALT….> tag
● Make good use of <TITLE> and <H1>
● Consider using the <META> tag
● Get people to link to your page

Hiding Web pages
● You may wish to have web pages that are not
indexed (for example, test pages).
● It is also possible to hide web content from robots,
using the Robots.txt file and the robots meta tag.
● Not all crawlers will obey this, so this is not foolproof.

Submitting To Search Engines
 Search engines should find you naturally, but
submitting helps speed the process and can
increase your representation
 Look for Add URL link at bottom of home page
 Submit your home page and a few key
“section” pages
 Turnaround from a few days to 2 months

Deep Crawlers
 AltaVista, Inktomi, Northern Light will add the
most, usually within a month
 Excite, Go (Infoseek) will gather a fair amount;
Lycos gathers little
 Index sizes are going up, but the web is
outpacing them…nor is size everything
 Here are more actions to help even the odds…

“Deep” Submit
 A “deep” submit is directly submitting pages from
“inside” the web site – can help improve the odds
these will get listed.
 At Go, you can email hundreds of URLs. Consider
doing this.
 At HotBot/Inktomi, you can submit up to 50 pages per
day. Possibly worth doing.
 At AltaVista, you can submit up to 5 pages per day.
Probably not worth the effort.
 Elsewhere, not worth doing a “deep” submit.

Big Site? Split It Up
 Expect search engines to max out at around
500 pages from any particular site
 Increase representation by subdividing large
sites logically into subdomains
 Search engines will crawl each subsite to more
depth
 Here’s an example...

Subdomains vs. Subdirectories
I nstead of Do this
gold.ac.uk/science/ science.gold.ac.uk
gold.ac.uk/english/ english.gold.ac.uk
gold.ac.uk/admin/ admin.gold.ac.uk

I Was Framed
 Don't use them. Period.
 If you do use them, search engines will have
difficulty crawling your site.

Dynamic Roadblocks
 Dynamic delivery systems that use ? symbols in the
URL string prevent search engines from getting to
your pages
 http://www.nike.com/ObjectBuilder/ObjectBuilder.iwx?ProcessName=
IndexPage&Section_Id=17200&NewApplication=t
 Eliminate the ? symbol, and your life will be rosy
 Look for workarounds, such as Apache rewrite or
Cold Fusion alternatives
 Before you move to a dynamic delivery system,
check out any potential problems.

How Directories Work
 Editors find sites, describe them, put them in a
category
 Site owners can also submit to be listed
 A short description represents the entire web
site
 Usually has secondary results from a crawler-
based search engine

The Major Directories
 Yahoo
 The Open Directory
 (Netscape, Lycos, AOL Search, others)
 LookSmart
 UK Plus
 Snap

Submitting To Directories
 Directories probably won't find you or may list you
badly unless you submit
 Find the right category (more in a moment), then use
Add URL link at top or bottom of page
 Write down who submitted (and email address), when
submitted, which category submitted to and other
details
 You’ll need this info for the inevitable resubmission attempt
– it will save you time.

Submitting To Directories
 Take your time and submit to these right
 Write 3 descriptions: 15, 20 and 25 words long, which
incorporate your key terms
 Search for the most important term you want to be
found for and submit to first category that's listed
which seems appropriate for your site
 Be sure to note the contact name and email address
you provided on the submit form
 If you don't get in, keep trying

Subdomain Advantage
 Directories tend not to list subsections of a
web site.
 In contrast, they do tend to see subdomains as
independent web sites deserving their own
listings
 So, another reason to go with subdomains
over subdirectories

How to do Search ?

What do we search ?
● Information
● Reviews, news
● Advice, methods
● Bugs
● Education stuff
● Examples:
– Access Violation 0xC0000005
– Search Engine ppt

Main Steps
● Make a decision about the search
● Formulate a topic. Define a type of resources that
you are looking for
● Find relevant words for description
● Find websites with information
● Choose the best out of them
● Feedback: How did you search?

Main Problems
Why is it difficult to search?
– Know the problem, don’t know what to look for
– Lose focus (go to interesting but non-relevant
sites)
– Perform superficial (shallow) search
– Search Spam

Typical Problems
● Links are often out of date
● Usually too many links are returned
● Returned links are not very relevant
● The Engines don't know about enough pages
● Different engines return different results
● Political bias

Typical Mistakes
● Unnecessary words in a query
● Unsuitable choice of keywords
● Not enough flexibility in changing keywords (Ses)
● Divide the time devoted to search and evaluation of
search results
● “Your search did not match any documents. ” – Bad
Query!

Search Tricks
What can we search for?
● Thematic resource (http://www.topicmaps.org)
● Community
● Collection of articles
● Forum
● Catalog of resources, links
● File (file types)
● Encyclopedia article
● Digital library
● Contact information (i.e. email)

Improving Query Results
● To look for a particular page use an unusual phrase
you know is on that page
● Use phrase queries where possible
● Check your spelling!
● Progressively use more terms
● If you don't find what you want, use another Search
Engine!

Useful words
● download
● pdf, ppt, doc, zip, mp3
● forum, directory, links
● faq, for newbies, for beginners, guide, rules,
checklist
● lecture notes, survey, tutorials
● how, where, correct, howto
● Copy-pasting the exact error message
● Have you tried http://del.icio.us/ ?

Search Engine Features

Features
● Indexing features
● Search features
● Results display
● Costs, licensing and registration requirements
● Unique features (if any)

Indexing Features
● File/document formats supported: HTML, ASCII, PDF, SQL, Spread
Sheets, WYSIWYG (MS-Word, WP, etc.)
● Indexing level support: File/directory level, multi-record files
● Standard formats recognized: MARC, Medline, etc
● Customization of document formats
– Stemming: If yes, is this an optional or mandatory feature?
– Stop words support: If yes, is this an optional or mandatory
features ?

Searching Features
● Boolean Searching: Use of Boolean operators AND, OR and NOT as
search term connectors
● Natural Language: Allows users to enter the query in natural language
● Phrase: Users can search for exact phrase
● Truncation/wild card: Variations of search terms and plural forms can
be searched
● Exact match: Allows users to search for terms exactly as it is entered
● Duplicate detection: Remove duplicate records from the retrieved
records
● Proximity: With connectors such as With , Near, ADJacent one can
specify the position of a search terms w.r.t to others

Searching Features
● Field Searching: Query for a specific field value in the database
● Thesaurus searching: Search for Broader or Narrower or Related terms or
Related concepts
● Query by example: Enables users to search for similar documents
● Soundex searching: Search for records with similar spelling as the search term
● Relevance ranking: Ranking the retrieved records in some order
● Search set manipulation: Saving the search results as sets and allowing users to
view search history

Results Display
● Formats supported: Can it display in native format or just HTML; Display in
different formats, Display number of records retrieved
● Relevancy ranking: If the retrieved records are ranked, how the relevance score
is indicated
● Keyword-in-context: KWIC or highlighting of matching search terms
● Customization of results display: allow users to select different display formats
● Saving options: Saving in different formats; number of records that can be saved
at a time

Evaluation of Search Engines

CRITICAL EVALUATION
Why Evaluate What You Find on the Web?
● Anyone can put up a Web page
– about anything
● Many pages not kept up-to-date
● No quality control
– most sites not “peer-reviewed”
● less trustworthy than scholarly publications
– no selection guidelines for search engines

Web Evaluation Techniques
Before you click to view the page...
● Look at the URL - personal page or site ?
~ or % or users or members
● Domain name appropriate for the content ?
edu, com, org, net, gov, ca.us, uk, etc.
● Published by an entity that makes sense ?
● News from its source?
www.nytimes.com
● Advice from valid agency?
www.nih.gov/
www.nlm.nih.gov/
www.nimh.nih.gov/

Scan the perimeter of the page
● Can you tell who wrote it ?
● name of page author
● organization, institution, agency you recognize
● e-mail contact by itself not enough
● Credentials for the subject matter ?
– Look for links to:
“About us” “Philosophy” “Background” “Biography”
●
Is it recent or current enough ?
● Look for “last updated” date - usually at bottom
● If no links or other clues...
● truncate back the URL
http://hs.houstonisd.org/hspva/academic/Science/Thinkquest/gail/text/ethics.html

Indicators of quality
● Sources documented
● links, footnotes, etc.
– As detailed as you expect in print publications ?
● do the links work ?
● Information retyped or forged
● why not a link to published version instead ?
● Links to other resources
● biased, slanted ?

What Do Others Say ?
● Search the URL in alexa.com
– Who links to the site? Who owns the domain?
– Type or paste the URL into the basic search box
– Traffic for top 100,000 sites
● See what links are in Google’s Similar pages
● Look up the page author in Google

STEP BACK & ASK: Does it all add up ?
● Why was the page put on the Web ?
● inform with facts and data?
● explain, persuade?
● sell, entice?
● share, disclose?
● as a parody or satire?
● Is it appropriate for your purpose?

Try evaluating some sites...
Search a controversial topic in Google:
 "nuclear armageddon"
 prions danger
 “stem cells” abortion
Scan the first two pages of results
Visit one or two sites
 try to evaluate their quality and reliability

Ufff, The End
● Have you learned something today ?
● Try whatever we've discussed today!
● If you need help, let me know at
sudarsun@gmail.com

Search Engine Demystified

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