How Does a Search Engine Works?

How Does a Search Engine Works?
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How Does a Search Engine Works?

How Does a Search Engine Works?
How Does a Search Engine Works?

While you should always create website content geared to your
customers rather than search engines, it is important to understand how a
search engine works. Once you know this, you can move on to the next
step, which is incorporating the elements that the search engine is
looking for.

How do search engines work?

Most search engines build an index based on crawling, which is the process through
which engines like Google, Yahoo and others find new pages to index.
Mechanisms known as bots or spiders crawl the Web looking for new pages
(1). The bots typically start with a list of website URLs determined
from previous crawls. When they detects new links on these pages,
through tags like HREF and SRC, they add these to the list of sites to
index. Then, search engines use their algorithms to provide you with a
ranked list from their index of what pages you should be most interested
in based on the search terms you used. 

Then, the engine will return a list of Web results ranked using
its specific algorithm. On Google, other elements like personalized and
universal results may also change your page ranking. In personalized
results, the search engine utilizes additional information it knows
about the user to return results that are directly catered to their
interests. Universal search results combine video, images and Google
news to create a bigger picture result, which can mean greater
competition from other websites for the same keywords.

Here are the top elements to edit when designing your store for SEO: 

Architecture – Make websites that search engines
can crawl easily. This includes several elements, like how the content
is organized and categorized and how individual websites link to one
another. An XML sitemap can allow you to give a list of URLs to search
engines for crawling and indexing. (2) 

Content – Great content is one the most
important elements for SEO because it tells search engines that your
website is relevant. This goes beyond just keywords to writing engaging
content your customers will be interested in on a frequent basis. 

Links – When a lot of people link to a certain
site, that alerts search engines that this particular website is an
authority, which makes its rank increase. This includes links from
social media sources. When your site links to other reputable platforms,
search engines are more likely to rate your content as quality also. 

Keywords– The keywords you use are one of the
primary methods search engines use to rank you. Using carefully selected
keywords can help the right customers find you. If you run a jewelry
store but never mention the word “jewelry,” “necklace,” or “bracelet,”
Google’s algorithm may not consider you an expert on the topic. 

Title descriptions – While it may not show up on
the website, search engines do pay attention to the title tag in your
site’s html code, the words between < title > < /title >,
because it likely describes what the website is about, like the title of
a book or a newspaper headline. 

Page content – Don’t bury important information
inside Flash and media elements like video. Search engines can’t see
images and video or crawl through content in Flash and Java plugins. 

Internal links – Including internal links helps
search engines crawl your website more effectively, but also boosts what
many SEO professionals refer to as “link juice.” In other words, it has
the same benefit of any link to your site: It demonstrates the value of
your content. 

Search engine is the popular term for an information retrieval (IR) system. While
researchers and developers take a broader view of IR systems, consumers
think of them more in terms of what they want the systems to do — namely
search the Web, or an intranet, or a database. Actually consumers would
really prefer a finding engine, rather than a search engine. 

Search engines match queries against an index that they create. The index consists of
the words in each document, plus pointers to their locations within the
documents. This is called an inverted file. A search engine or IR system
comprises four essential modules: 

  • A document processor
  • A query processor
  • A search and matching
  • A ranking capability

While users focus on “search,” the search and matching function is only one of the four modules.
Each of these four modules may cause the expected or unexpected results
that consumers get when they use a search engine.

Document Processor

The document processor
prepares, processes, and inputs the documents, pages, or sites that users
search against. The document processor performs some or all of the following


  • Normalizes the document
    stream to a predefined format.
  • Breaks the document
    stream into desired retrievable units.
  • Isolates and metatags
    subdocument pieces.
  • Identifies potential
    indexable elements in documents.
  • Deletes stop words.
  • Stems terms.
  • Extracts index entries.
  • Computes weights.
  • Creates and updates
    the main inverted file against which the search engine searches in order
    to match queries to documents.

Steps 1-3:
While essential and potentially important in affecting the outcome of a
search, these first three steps simply standardize the multiple formats
encountered when deriving documents from various providers or handling
various Web sites. 
The steps serve to merge all the data into a single
consistent data structure that all the downstream processes can handle.
The need for a well-formed, consistent format is of relative importance
in direct proportion to the sophistication of later steps of document processing.
Step two is important because the pointers stored in the inverted file
will enable a system to retrieve various sized units — either site, page,
document, section, paragraph, or sentence. 

Step 4:
elements to index.
Identifying potential indexable elements in documents

dramatically affects the nature and quality of the document representation
that the engine will search against. In designing the system, we must define
the word “term.” Is it the alpha-numeric characters between blank spaces
or punctuation? If so, what about non-compositional phrases (phrases in
which the separate words do not convey the meaning of the phrase, like
“skunk works” or “hot dog”), multi-word proper names, or inter-word symbols
such as hyphens or apostrophes that can denote the difference between “small
business men” versus small-business men.” Each search engine depends on
a set of rules that its document processor must execute to determine what
action is to be taken by the “tokenizer,” i.e. the software used to define
a term suitable for indexing. 

Step 5:
stop words
. This step helps save system resources by eliminating from

further processing, as well as potential matching, those terms that have
little value in finding useful documents in response to a customer’s query.
This step used to matter much more than it does now when memory has become
so much cheaper and systems so much faster, but since stop words may comprise
up to 40 percent of text words in a document, it still has some significance. 
A stop word list typically consists of those word classes known to convey
little substantive meaning, such as articles (a, the), conjunctions
(and, but), interjections (oh, but), prepositions (in,
), pronouns (he, it), and forms of the “to be” verb (is,
). To delete stop words, an algorithm compares index term candidates

in the documents against a stop word list and eliminates certain terms
from inclusion in the index for searching. 

Step 6: Term
Stemming removes word suffixes, perhaps recursively in layer

after layer of processing. The process has two goals. In terms of efficiency,
stemming reduces the number of unique words in the index, which in turn
reduces the storage space required for the index and speeds up the search
process. In terms of effectiveness, stemming improves recall by reducing
all forms of the word to a base or stemmed form.
For example, if a user
asks for analyze, they may also want documents which contain analysis,
and analyzed. Therefore, the document processor stems document terms
to analy- so that documents which include various forms of analy-
will have equal likelihood of being retrieved; this would not occur if
the engine only indexed variant forms separately and required the user
to enter all. Of course, stemming does have a downside. It may negatively
affect precision in that all forms of a stem will match, when, in fact,
a successful query for the user would have come from matching only the
word form actually used in the query. 

Systems may implement
either a strong stemming algorithm or a weak stemming algorithm. A strong
stemming algorithm will strip off both inflectional suffixes (-s, -es,
) and derivational suffixes (-able, -aciousness, -ability),

while a weak stemming algorithm will strip off only the inflectional suffixes
(-s, -es, -ed). 

Step 7:
index entries
. Having completed steps 1 through 6, the document processor

extracts the remaining entries from the original document. For example,
the following paragraph shows the full text sent to a search engine for


comments, carried by the official news agency Tanjug, cast doubt over the
governments at the talks, which the international community has called
to try to prevent an all-out war in the Serbian province. “President Milosevic
said it was well known that Serbia and Yugoslavia were firmly committed
to resolving problems in Kosovo, which is an integral part of Serbia, peacefully
in Serbia with the participation of the representatives of all ethnic communities,”
Tanjug said. Milosevic was speaking during a meeting with British Foreign
Secretary Robin Cook, who delivered an ultimatum to attend negotiations
in a week’s time on an autonomy proposal for Kosovo with ethnic Albanian
leaders from the province. Cook earlier told a conference that Milosevic
had agreed to study the proposal.

Steps 1 to 6 reduce
this text for searching to the following:

comm carri offic new agen Tanjug cast doubt govern talk interna commun
call try prevent all-out war Serb province President Milosevic said well
known Serbia Yugoslavia firm commit resolv problem Kosovo integr part Serbia
peace Serbia particip representa ethnic commun Tanjug said Milosevic speak
meeti British Foreign Secretary Robin Cook deliver ultimat attend negoti
week time autonomy propos Kosovo ethnic Alban lead province Cook earl told
conference Milosevic agree study propos.

The output of step
7 is then inserted and stored in an inverted file that lists the index
entries and an indication of their position and frequency of occurrence.
The specific nature of the index entries, however, will vary based on the
decision in Step 4 concerning what constitutes an “indexable term.” More
sophisticated document processors will have phrase recognizers, as well
as Named Entity recognizers and Categorizers, to insure index entries such
as Milosevic are tagged as a Person and entries such as Yugoslavia
and Serbia as Countries. 

Step 8: Term
weight assignment.
Weights are assigned to terms in the index file.

The simplest of search engines just assign a binary weight: 1 for presence
and 0 for absence. The more sophisticated the search engine, the more complex
the weighting scheme. Measuring the frequency of occurrence of a term in
the document creates more sophisticated weighting, with length-normalization
of frequencies still more sophisticated. Extensive experience in information
retrieval research over many years has clearly demonstrated that the optimal
weighting comes from use of “tf/idf.” This algorithm measures the frequency
of occurrence of each term within a document. Then it compares that frequency
against the frequency of occurrence in the entire database.

Not all terms are
good “discriminators” — that is, all terms do not single out one document
from another very well. 
A simple example would be the word “the.” This
word appears in too many documents to help distinguish one from another.
A less obvious example would be the word “antibiotic.” In a sports database
when we compare each document to the database as a whole, the term “antibiotic”
would probably be a good discriminator among documents, and therefore would
be assigned a high weight. Conversely, in a database devoted to health
or medicine, “antibiotic” would probably be a poor discriminator, since
it occurs very often. The TF/IDF weighting scheme assigns higher weights
to those terms that really distinguish one document from the others. 

Step 9: Create
The index or inverted file is the internal data structure that

stores the index information and that will be searched for each query.
Inverted files range from a simple listing of every alpha-numeric sequence
in a set of documents/pages being indexed along with the overall identifying
numbers of the documents in which the sequence occurs, to a more linguistically
complex list of entries, the tf/idf weights, and pointers to where inside
each document the term occurs. The more complete the information in the
index, the better the search results.


Query Processor

Query processing
has seven possible steps, though a system can cut these steps short and
proceed to match the query to the inverted file at any of a number of places
during the processing. Document processing shares many steps with query
processing. More steps and more documents make the process more expensive
for processing in terms of computational resources and responsiveness.
However, the longer the wait for results, the higher the quality of results.
Thus, search system designers must choose what is most important to their
users — time or quality. Publicly available search engines usually choose
time over very high quality, having too many documents to search against.

The steps in query
processing are as follows (with the option to stop processing and start
matching indicated as “Matcher”):


    • Tokenize query terms.

Recognize query
terms vs. special operators.
————————> Matcher

    • Delete stop words.
    • Stem words.
    • Create query representation.


————————> Matcher

    • Expand query terms.
    • Compute weights.


————————> Matcher

Step 1:
As soon as a user inputs a query, the search engine — whether a keyword-based
system or a full natural language processing (NLP) system — must tokenize
the query stream, i.e., break it down into understandable segments. Usually
a token is defined as an alpha-numeric string that occurs between white
space and/or punctuation. 

Step 2: Parsing.
Since users may employ special operators in their query, including Boolean,
adjacency, or proximity operators, the system needs to parse the query
first into query terms and operators. These operators may occur in the
form of reserved punctuation (e.g., quotation marks) or reserved terms
in specialized format (e.g., AND, OR). In the case of an NLP system, the
query processor will recognize the operators implicitly in the language
used no matter how the operators might be expressed (e.g., prepositions,
conjunctions, ordering).

At this point,
a search engine may take the list of query terms and search them against
the inverted file. In fact, this is the point at which the majority of
publicly available search engines perform the search.

Steps 3
and 4: Stop list and stemming. Some search engines will go further
and stop-list and stem the query, similar to the processes described above
in the Document Processor section. The stop list might also contain words
from commonly occurring querying phrases, such as, “I’d like information
about.” However, since most publicly available search engines encourage
very short queries, as evidenced in the size of query window provided,
the engines may drop these two steps. 

Step 5: Creating
the query.
How each particular search engine creates a query representation

depends on how the system does its matching. If a statistically based matcher
is used, then the query must match the statistical representations of the
documents in the system. Good statistical queries should contain many synonyms
and other terms in order to create a full representation. If a Boolean
matcher is utilized, then the system must create logical sets of the terms
connected by AND, OR, or NOT. 

An NLP system will
recognize single terms, phrases, and Named Entities. If it uses any Boolean
logic, it will also recognize the logical operators from Step 2 and create
a representation containing logical sets of the terms to be AND’d, OR’d,
or NOT’d. 

At this point,
a search engine may take the query representation and perform the search
against the inverted file. More advanced search engines may take two further

Step 6: Query
. Since users of search engines usually include only a single

statement of their information needs in a query, it becomes highly probable
that the information they need may be expressed using synonyms, rather
than the exact query terms, in the documents which the search engine searches
against. Therefore, more sophisticated systems may expand the query into
all possible synonymous terms and perhaps even broader and narrower terms. 

This process approaches
what search intermediaries did for end users in the earlier days of commercial
search systems. Back then, intermediaries might have used the same controlled
vocabulary or thesaurus used by the indexers who assigned subject descriptors
to documents. Today, resources such as WordNet are generally available,
or specialized expansion facilities may take the initial query and enlarge
it by adding associated vocabulary. 

Step 7: Query
term weighting
(assuming more than one query term). The final step

in query processing involves computing weights for the terms in the query.
Sometimes the user controls this step by indicating either how much to
weight each term or simply which term or concept in the query matters most
and must appear in each retrieved document to ensure relevance. 

Leaving the weighting
up to the user is not common, because research has shown that users are
not particularly good at determining the relative importance of terms in
their queries. They can’t make this determination for several reasons.
First, they don’t know what else exists in the database, and document terms
are weighted by being compared to the database as a whole. Second, most
users seek information about an unfamiliar subject, so they may not know
the correct terminology. 

Few search engines
implement system-based query weighting, but some do an implicit weighting
by treating the first term(s) in a query as having higher significance.
The engines use this information to provide a list of documents/pages to
the user.

After this final
step, the expanded, weighted query is searched against the inverted file
of documents.


Search and Matching Function

How systems carry
out their search and matching functions differs according to which theoretical
model of information retrieval underlies the system’s design philosophy.
Since making the distinctions between these models goes far beyond the
goals of this article, we will only make some broad generalizations in
the following description of the search and matching function. Those interested
in further detail should turn to R. Baeza-Yates and B. Ribeiro-Neto’s excellent
textbook on IR (Modern Information Retrieval, Addison-Wesley, 1999). 

Searching the inverted
file for documents meeting the query requirements, referred to simply as
“matching,” is typically a standard binary search, no matter whether the
search ends after the first two, five, or all seven steps of query processing.
While the computational processing required for simple, unweighted, non-Boolean
query matching is far simpler than when the model is an NLP-based query
within a weighted, Boolean model, it also follows that the simpler the
document representation, the query representation, and the matching algorithm,
the less relevant the results, except for very simple queries, such as
one-word, non-ambiguous queries seeking the most generally known information. 

Having determined
which subset of documents or pages matches the query requirements to some
degree, a similarity score is computed between the query and each document/page
based on the scoring algorithm used by the system. Scoring algorithms rankings
are based on the presence/absence of query term(s), term frequency, tf/idf,
Boolean logic fulfillment, or query term weights. Some search engines use
scoring algorithms not based on document contents, but rather, on relations
among documents or past retrieval history of documents/pages. 

After computing
the similarity of each document in the subset of documents, the system
presents an ordered list to the user. The sophistication of the ordering
of the documents again depends on the model the system uses, as well as
the richness of the document and query weighting mechanisms. For example,
search engines that only require the presence of any alpha-numeric string
from the query occurring anywhere, in any order, in a document would produce
a very different ranking than one by a search engine that performed linguistically
correct phrasing for both document and query representation and that utilized
the proven tf/idf weighting scheme. 

However the search
engine determines rank, the ranked results list goes to the user, who can
then simply click and follow the system’s internal pointers to the selected

More sophisticated
systems will go even further at this stage and allow the user to provide
some relevance feedback or to modify their query based on the results they
have seen. If either of these are available, the system will then adjust
its query representation to reflect this value-added feedback and re-run
the search with the improved query to produce either a new set of documents
or a simple re-ranking of documents from the initial search.


What Document Features Make
a Good Match to a Query

We have discussed
how search engines work, but what features of a query make for good matches?
Let’s look at the key features and consider some pros and cons of their
utility in helping to retrieve a good representation of documents/pages.


• Term
: How frequently a query term appears in a document is one

of the most obvious ways of determining a document’s relevance to a query.
While most often true, several situations can undermine this premise. First,
many words have multiple meanings — they are polysemous. Think of words
like “pool” or “fire.” Many of the non-relevant documents presented to
users result from matching the right word, but with the wrong meaning.

Also, in a collection
of documents in a particular domain, such as education, common query terms
such as “education” or “teaching” are so common and occur so frequently
that an engine’s ability to distinguish the relevant from the non-relevant
in a collection declines sharply. Search engines that don’t use a tf/idf
weighting algorithm do not appropriately down-weight the overly frequent
terms, nor are higher weights assigned to appropriate distinguishing (and
less frequently-occurring) terms, e.g., “early-childhood.”

• Location
of terms:
Many search engines give preference to words found in the

title or lead paragraph or in the metadata of a document. Some studies
show that the location — in which a term occurs in a document or on a page
— indicates its significance to the document. Terms occurring in the title
of a document or page that match a query term are therefore frequently
weighted more heavily than terms occurring in the body of the document.
Similarly, query terms occurring in section headings or the first paragraph
of a document may be more likely to be relevant.


• Link analysis:

search engines have introduced one dramatically different feature for weighting
and ranking pages. Link analysis works somewhat like bibliographic citation
practices, such as those used by Science Citation Index. Link analysis
is based on how well-connected each page is, as defined by Hubs and Authorities,
where Hub documents link to large numbers of other pages (out-links), and
Authority documents are those referred to by many other pages, or have
a high number of “in-links” (J. Kleinberg, “Authoritative Sources in a
Hyperlinked Environment,” Proceedings of the 9th ACM-SIAM Symposium
on Discrete Algorithms.
1998,pp. 668-77).


• Popularity

Google and several other search engines add popularity to link analysis
to help determine the relevance or value of pages. Popularity utilizes
data on the frequency with which a page is chosen by all users as a means
of predicting relevance. While popularity is a good indicator at times,
it assumes that the underlying information need remains the same.


• Date of Publication:

search engines assume that the more recent the information is, the more
likely that it will be useful or relevant to the user. The engines therefore
present results beginning with the most recent to the less current.


• Length

While length per se does not necessarily predict relevance, it is a factor
when used to compute the relative merit of similar pages. So, in a choice
between two documents both containing the same query terms, the document
that contains a proportionately higher occurrence of the term relative
to the length of the document is assumed more likely to be relevant.


• Proximity
of query terms
: When the terms in a query occur near to each other

within a document, it is more likely that the document is relevant to the
query than if the terms occur at greater distance. While some search engines
do not recognize phrases per se in queries, some search engines clearly
rank documents in results higher if the query terms occur adjacent to one
another or in closer proximity, as compared to documents in which the terms
occur at a distance.


• Proper nouns

have higher weights, since so many searches are performed on people, places,
or things. While this may be useful, if the search engine assumes that
you are searching for a name instead of the same word as a normal everyday
term, then the search results may be peculiarly skewed. Imagine getting
information on “Madonna,” the rock star, when you were looking for pictures
of madonnas for an art history class.

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