International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 6 - September 2015
A Novel Link and Prospective terms Based Page Ranking
Technique
Ashlesha Gupta#1, Ashutosh Dixit*2, Taruna Kumari#3
#1
Assistant Professor , #2Associate Professor , #3Associate Professor
Department of Computer Engineering
YMCA University of Science & Technology
Faridabad, India.
Keywords — Search Engine, Page Ranking, Page
content, Link popularity, Prospective Keywords
I. INTRODUCTION
WWW is a large collection of information resources
that include text, image, audio, video and
metadata .An explosive growth in the size of WWW
has made it very difficult to manage and access the
desired information on the web. Therefore, Internet
users today use tools like search engines for accessing
the desired information on the Internet. These Search
ISSN: 2231-5381
Engine help locate information by presenting a list of
clickable URL’s generated on the basis of search
terms entered by the user. The search engine maintains
a huge repository of web pages in its database for
search purposes. The general architecture of web
search engine is shown in fig 1.
Basically a web search engine has three major
components: Crawler, Indexer and Query Engine. A
crawler downloads the web pages while traversing the
web and stores the downloaded pages in a large buffer.
World
Wide Web
Crawler
docUrl buffer
Documents
URLs
Abstract— Since size of the web is of the order of
more than a billion pages, finding relevant
information is a tedious task therefore many Internet
users make use of search engines to find desired
information on WWW. These Search engines find
relevant information based on important words i.e.
keywords supplied in the form of queries. For a given
query search engine may return large number of web
pages in the result-set which may or may not contain
relevant information Since users hardly look at results
coming after first search result page therefore it is
necessary to rank these pages in order of relevance so
that top pages contain most relevant data. Therefore
page ranking mechanisms are being employed by
search engines to rank the web pages. Present page
ranking algorithms either consider the link structure
of the web page or the keywords entered in the query
for rank purpose .These algorithms however suffer
from topic drift and lack of quality problems and as a
result users have to scan through large result-sets
refining it manually to gather the required
information. So there is a need to improve these page
ranking mechanisms.
This paper focuses on a
prospective term based page ranking mechanism that
not only considers the link structure and query
keywords of the web page but takes a perspective view
by taking into consideration synonyms and related
keywords to provide better ranking solution wherein
the user gets the desired information with less number
of clicks. The proposed algorithm is aimed at
improving user satisfaction by providing full
information within the first few URL’s thereby
improving search experience. The results of the
proposed algorithm are analyzed and compared with
the existing scheme.
Indexer
Dispatcher
Repository
User
Query Engine
Search Interface
Fig. 1 : Architecture of Search Engine
The Indexer than processes the pages in this buffer.
It first extracts the keywords from each page and
maintains an index containing information about the
keyword and
the URL where each keyword has
occurred in a large repository. The query engine is
responsible for receiving and filling search requests
from user. When a user fires a query, query engine
receives it and after matching the query keywords
with the index, returns the URL’s of the pages to the
user.
For a given query, Query Engine may return hundreds
of URL’s that match the query keywords. This
returned result set however may contain a mixture of
both relevant and irrelevant information. Therefore it
is necessary to arrange the web pages in order of their
importance. So page ranking mechanisms are used by
most search engines for putting the important pages on
top leaving the less important pages in the bottom of
result list.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 6 - September 2015
Current Page Ranking algorithm either use Linkstructure of the web page or the Content Information
of the web page to calculate page rank. But both
techniques have some shortcoming and they suffer
from topic drift and lack of quality in the result-sets.
Moreover users try to find desired information on the
first page of the search result only and results coming
after first search result page are nearly invisible for
general user. If user does not get information on the
first page they consider the search to be a miss and try
to reformulate the query to find the desired result.
therefore a global rank may not provide actual
importance of the page for a given individual user.
Conventional Query Dependent Page Ranking
algorithms like Page Content Algorithm(PCR) use
only term occurrence frequency and occurrence
position of the given query keywords for computing
page rank. They do not consider pages for page
ranking that may contain either a synonym of query
keywords or pages that may contain the related
information with respect to given query even without
containing the actual keywords in the query. For
example the query – ―holiday‖ would not return pages
Keeping in view the above mentioned problems a that contain the term ―vacation‖ . As two terms are
Prospective Term based page ranking mechanism is synonyms of each other computer should provide web
being proposed that not only considers link structure pages that contain either of the terms. Similarly a
of a web page but also combines query dependent query about ―Ayurved in India‖ should provide
factors like occurrence frequency of keyword, resultant pages containing information about ―Baba
synonyms along with the prospective words (words Ram Dev‖, because they are indirectly related to each
having direct/indirect relation with the word) for others. Since traditional ranking is limited to
ranking to improve overall quality of search result.
keywords only, users have to scan through the resultsets refining the query multiple times to acquire all the
The rest of the paper is organized as follows. The needed information.
Related work and Back Ground is covered in Section
A critical look at the available literature indicates
II. Section III discusses the architecture, components the following deficiencies in the existing page ranking
and algorithms of the proposed page ranking techniques:
algorithm. Section IV discusses the implementation
Some web pages may get higher ranking
and results of the proposed algorithm. Section V
because of duplicate links and self links that
includes the conclusion.
are meant only for increasing the popularity
of the web page, but actually they do not
II. BACKGROUND & RELATED WORK
contain any relevant information. Similarly
(Size 10 & Normal) Search engines use two
new web pages that actually contain the latest
different kinds of ranking factors: query-dependent
information can’t get higher page rank values,
factors (i.e word frequency, position of query terms
because of lack of the corresponding back
etc) and query independent factors (i.e. link popularity,
links.
click popularity etc.) for ranking web page documents.
Different people may have different
Query-dependent are all ranking factors that are
preference; therefore a global rank may not
specific to a given query, while query-independent
provide actual importance of web page for a
factors are attached to the documents, regardless of a
given individual user
given query.
Traditional Query Dependent Page ranking
Link structure based page ranking for determining
algorithms are limited to keywords only.
the ―importance‖ of web pages has become an
important technique in today’s search engines. Some
Therefore, there is a need to introduce other query
of the common page ranking algorithms are PageRank dependent factors to provide a better ranking solution.
Algorithm [2], Weighted Page Rank Algorithm [4]
III. PROPOSED WORK
and Hyperlinked Induced Topic search Algorithm[5].
Page Rank takes the back links into account and
All Due to the prevalent deficiencies in the current
propagates the ranking through links. Rank score of a page ranking algorithms, users are not able to get
page p is evenly divided among its outgoing links. desired results in top pages and have to scan through
Whereas WPR takes into account the importance of multiple search pages to meet their demand. To
both inlinks and the outlinks of the pages and overcome these shortcomings, a novel page ranking
distributes rank scores based on the popularity of the mechanism is being proposed that considers
pages [2,3,4]. HITS (Hyperlink-Induced Topic Search popularity of a web page (based on in-links and outrank pages based on their textual contents to a given links) and occurrence frequency of keywords along
query, after assembling the pages it ignores textual with the synonyms and prospective words (words
content and focuses itself on the structure of web only having direct/indirect relation with the keyword) for
[5]. These link based algorithms based on global rank ranking to improve overall quality of search results.
however suffer from quality problems and are biased.
Moreover the importance of a page may depend on
Prospective Term based page rank mechanism uses
different interests and knowledge of different people computed Document weight to rank the web pages.
The computation of Document weight of a web page
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 6 - September 2015
is a sum of its link score and content score. Link score
is specified by calculating total number of in-links and
out-links of a web Page and content score is based on
the occurrence frequency of both query keywords and
prospective keywords of a web page
that contains keywords that may relate with the given
query keywords syntactically or may have some
direct/indirect relation with the query keywords. The
prospective table that suggests the prospective words
for a given keyword is created at the search engine
side.
A user generally supplies a query to search engine
with multiple keywords. Based on this assumption,
Perspective table is created according to the following
rules:
For each keyword available in a web page, a
prospective table is constructed that contains
keywords that may relate with entered keywords
syntactically or may have some direct/indirect
relation . For example prospective table for keyword
―Web-Mining‖ would contain the related keywords
Rule 1: If query contains only single keyword say
such as Search Engine, Architecture-of-Search-Engine, ―X‖ then perspective table will contain:
Indexing Techniques, Crawler, Page rank etc. The
architecture of the proposed ranking algorithm is
Synonym of ―X‖ and/or Inferred keywords
shown in Fig 2.
(words having direct/indirect relation) with
keyword ―X‖.
For
example
the records of prospective table for the
Prospective
QUERY
SEARCH
Table
keyword ―Crawlerr‖ will contain the following:
PROCESSO
INTERFAC
R
E
“Automated Program, Topical , Focused ,
Incremental ”
PageRankScore
Link
Popularity
Score
Matched
Documents
Rule 2: If query contains two keywords say ―X‖
and ―Y‖then prospective table will contain:
Repository
Content
Based Score
Fig. 2 Architecture of Proposed Algorithm
Synonym of ―X‖ and/or Inferred keywords
(words having direct/indirect relation) with
keyword ―X‖.
Synonym of ―Y‖ and/or Inferred keywords
(words having direct/indirect relation) with
keyword ―Y‖.
Inferred
keywords
(words
having
direct/indirect relation) with the combination
of keywords ―X‖ and ―Y‖.
The user first enters a search query in the search
interface. This query is passed to the Query Processor,
which then processes the query by parsing it,
removing stop words and identifying the query terms.
The prospective keywords for the query terms are then
For example the records of perspective table for the
fetched from the Prospective table. These are then query ―Protest Delhi‖ is shown in Table I:
passed to the Indexer to fetch all the URL’s that
contain either or both of the query and perspective
terms. For the fetched URLs a page rank score based
TABLE I: PERSPECTIVE WORDS TABLE
on link and content score is calculated. The web pages
Keywords
Prospective Term Table
are then ranked on the basis of Page Rank Score and
Record
passed to Query Processor, which then presents the
Election
Vote,
Choice,
results to the user.
Commissioner, Election-poll
There are three main stages of the proposed
Delhi
Capital of India, Delhi-map,
algorithm namely: Link Popularity weight calculation,
Delhi-Tourism, Delhi-Metro
Prospective table construction, and Context weight
Election Delhi
CM, Kiran Bedi, Arvind
calculation.
Kejriwal, 7th February
A. Link Popularity calculation
The popularity of each web page is measured with
the help of its in-links and out-links. Link popularity
calculation is based on equation (1)
Link_Score= (No. of Inlinks)+(No. of Outlinks) -(1)
B. Prospective Table Construction
For each keyword available in a page, a list of
prospective terms are created from prospective table
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And likewise new rule may be generated for queries
containing more keywords. This table is created by the
search engine at the back end by using classification
algorithms such as apriori algorithm. The table may
get dynamically updated with respect to the news sites
for latest keyword relation and current perception.
The Apriori Algorithm is a classic algorithm for
mining frequent item sets for boolean association
rules. , the algorithm attempts to find subsets which
are common to at least a minimum number C of the
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 6 - September 2015
itemsets. Apriori uses a "bottom up" approach, where
frequent subsets are extended one item at a time (a
step known as candidate generation), and groups of
candidates are tested against the data. The algorithm
terminates when no further successful extensions are
found. Apriori algorithm can be used to find the terms
which are co occurring in various documents of index
file of a search engine and these co occurring terms
are called context terms. For example if the keywords
laptop, desktop, keyboard, mouse are co occurring
with the term computer in some minimum number of
documents then we can say these terms are
contextually related to the term computer. The
working of the Apriori algorithm is explained below:
Let D be the index of web documents. Support S, is
the occurrence frequency of a keyword in a document.
Frequent k-term set is the set of k terms which co
occur in some minimum no. of documents
.
1. Scan the index of web documents D, to get
the support S of each 1-keyword (term) set,
compare S with min_sup, and get a set of
frequent 1- term sets, L1.
2.
3.
4.
Use Lk-1 join Lk-1 to generate a set of
candidate k-term set.
Scan the index database to get the support S
of each candidate k-term set in the final set,
compare S with min-sup, and get a set of
frequent k- term set , Lk
If candidate set is empty then stop else go to
step 2.
An example showing the working of the apriori
algorithm is shown below:
Consider an example database consisting 6
documents as shown below in Table II .Suppose
minimum support count required is 2.
TABLE II: DATABASE COLLECTION
Document
Doc1
Doc2
Doc3
Doc4
Doc5
Doc6
TABLE III: C1
Term
Computer
Computer
generation
Desktop
Laptop
CPU
SuperComputer
Keyboard
Mouse
Printer
Monitor
Hp
Dell
Support Count
6
2
5
5
1
1
2
2
3
1
1
1
Compare candidate support count with min_sup
count to generate L1.
TABLE IV: L1
Term
Computer
Support Count
6
Computer
generation
Desktop
2
Laptop
5
Keyboard
2
Mouse
2
Printer
3
5
Step 2-> Generating 2-termset frequent pattern :
Generate candidate set C2 from L1.
Keywords
Computer, computer generation,
desktop, laptop, CPU
Computer, computer generation,
desktop, laptop, super computer
Computer, desktop, keyboard,
mouse, printer, monitor
Computer, mouse, printer,
keyboard, laptop
Computer, hp, printer, laptop,
desktop
Computer, laptop, desktop, dell
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Step-1-> Generating 1-termset frequent pattern
Scan the database D for count of each candidate to
generate C1
Term
{computer, computer generation}
{computer, desktop}
{computer, laptop}
{computer, keyboard}
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 6 - September 2015
{computer, mouse}
{computer, printer}
{computer generation, desktop}
{computer generation, laptop}
{computer generation, keyboard}
{computer generation, mouse}
{computer generation, printer}
{desktop, laptop}
{desktop, keyboard}
{desktop, mouse}
{desktop, printer}
{laptop, keyboard}
{laptop, mouse}
{laptop, printer}
{keyboard, mouse}
{keyboard, printer}
{mouse, printer}
{Computer,computer
generation}
{computer,
computer
desktop}
{computer , laptop}
{computer, keyboard}
{computer, mouse}
Support
Count
2
5
5
2
2
{computer, printer}
3
{computer generation,
desktop}
{computer generation,
laptop}
{computer generation,
keyboard}
{computer generation,
mouse}
{computer generation,
printer}
{desktop, laptop}
2
{desktop, keyboard}
1
2
0
0
0
4
Term
{computer , computer generation,
desktop}
{computer, computer generation, laptop}
{computer, desktop, laptop}
{computer, desktop, printer}
{computer, laptop, printer}
{computer, keyboard , mouse}
{computer, keyboard , printer}
{computer, mouse, printer}
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1
{desktop, printer}
2
{laptop,keyboard}
1
{laptop,mouse}
{laptop, printer}
1
2
{keyboard, mouse}
2
{keyboard, printer}
2
{mouse, printer}
2
C2
Scan the database D for count of each candidate C2.
Term
{desktop, mouse}
Compare candidate support count with min_sup
count and generate L2.
Term
Support Count
{computer,
computer
generation
{computer,
desktop}
{computer,lapt
op}
{computer,
keyboard}
{computer,
mouse}
{computer,
printer}
{computer
generation
,
desktop}
{computer
generation laptop}
{desktop,
laptop}
{desktop,
printer}
{laptop,
printer}
{keyboard,
mouse}
{keyboard,
printer}
{mouse,
printer}
2
5
5
2
2
3
2
2
4
2
2
2
2
2
L2
Step 3-> Generating 3-termset frequent pattern
Generate candidate set C3 from L2.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 6 - September 2015
Scan the database D for count of each candidate C3.
Term
Support
count
2
{computer,
computer
generation, desktop}
{computer,
computer
2
generation, laptop}
{computer,
desktop,
4
laptop}
{computer,
desktop,
2
printer}
{computer,
laptop,
2
printer}
{computer,
keyboard,
2
L3
mouse}
{computer,
keyboard,
2
printer}
{computer,
mouse,
2
printer}
After comparing support count of C3 term set with
min_support we get L3 as shown above.
Step – 4 generating 4-termset frequent pattern
Generate C4 candidates from L3 and scan the database
D, for count of each candidate
Term
{computer, computer
generation,
desktop,
laptop}
{computer,
desktop,
laptop, printer}
{computer, keyboard,
mouse, printer}
Support Count
2
Page Rank Score= Link_Score + Content_Score------(3)
IV. IMPLEMENTATION
To implement the proposed ranking system core
java is used as front end development tool and mysql
is used as database management system. To calculate
popularity weight of web pages there is need to extract
link information from the web pages. Program is
developed which will extract link information from
the web pages and store it in proper tables described
as follows:
1.
WebPages’ table: This table stores
information about every web page.
TABLE V : WEB_PAGE TABLE STRUCTURE
Field Name
Page_id
Data Type
Number
Page_link
Varchar
Inlink
Outlink
Link_score
Number
Number
Number
2
2
L4
Now it is not possible to generate C5 from L4.
In this way by using apriori algorithm prospective
table is created.
C. Context weight calculation
Context weight of document is calculated based on
the presence of query term and prospective terms in
the document. The weight is calculated as how many
terms out of query term and prospective terms are
present within the document. Content Score
calculation is based on equation 2
ntd
Content_Score =
------------------------(2)
tnt
Where ntd is number of terms (query term and
prospective terms) present in the document and tnt is
the total no. of terms in the web page.
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D. Page Rank Score Calculation: The final rank of
a web page is based on the sum of its link_score and
content score. Page Rank Score is calculated
according to equation 3.
Page_id field will store unique id given to a web
page. Page_link field will store the complete link of
the webpages. No. of inlniks and outlinks of the
webpage will be stored in inlink and outlink field.
Link_Score will
Page field as in webpages_inlink table will store
link of the webpage and outlink field will store
outlinks of a webpage.
2. Term_doc table: This table is like index. It will
store keywords and the documents containing them.
TABLE VI: TERM_DOC TABLE
Field Name
Data Type
Term
Varchar
Document
Varchar
3. Prospective table: This table will store terms
that are occurring together in various number of
documents. To create prospective table, index of web
pages is required. Program implementing this module
will take term_doc table as input and applies apriori
algorithm that will return prospective table as shown
in Fig 3
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 6 - September 2015
The Ranking order of the URLs in response to the
query ―Holidays in Delhi‖ based on prospective terms
is shown in Table IX.
TABLE IX : SEARCH RESULTS USING PROPOSED
MECHANISM
Fig 3: Prospective Table Snapshot
When user gives query at search interface, the
program will searches for prospective terms in the
prospective term table. The documents that contain
terms or prospective terms or both are then sorted
according to link score stored in web_pages table.
After that every matched document is sorted based on
content_score and the results are returned to the user.
A comparison between the results of popular search
engine called ―Google‖ and the proposed page rank
method was also performed . A query ―Holidays in
Delhi‖ was fired to find information related to Tourist
places in Delhi. The response URL’s returned by
google for query ―Holidays In Delhi‖ are shown in
Table VII.
TABLE VII : SEARCH RESULTS BY GOOGLE
Rank
URLs
1
www.publicholidays.in/delhi
2
6
www.officeholidays.com
www.hindustantimes.com/new-delhi
10
http://www.ebookers.com/travelguide/India/New_Delhi.tg29592
www.wikitravel.org/en/delhi
www.delhiweekendbreaks.com
31
50
The effect of the proposed Prospective Terms based
page rank mechanism on the same set of web pages is
analyzed below. The combined perspective terms for
the given query are shown in Table VIII.
TABLE VIII : PERSPECTIVE TABLE FOR
― HOLIDAYS IN DELHI‖
Rank
1
2
3
4
5
6
URLs
www.wikitravel.org/en/delhi
http://www.ebookers.com/travelguide/India/New_Delhi.tg29592
www.delhiweekendbreaks.com
www.officeholidays.com
www.publicholidays.in/delhi
www.hindustantimes.com/new-delhi
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Query
Prospective Terms
Holiday
in Delhi
Vacations , trip, break, tourist-guide,
resort, packages, old-delhi, new-delhi,
Delhi-map, Delhi-metro, Delhi-tourism,
Delhi-airport, hotel
It can be observed that the highest PageRankScore
comes
out
to
be
that
of
―www.wikitravela.org/en/delhi‖ since this site
contains links and other information related to the
keywords fired in the query as well as keywords in the
perspective
table.
The
URL
http://www.ebookers.com/travelguide/India/New_Delhi is placed at second position .
The site is a guide to tourist places in Delhi and also
give details related to accomadation and travelling in
Delhi. The site www.hindustntimes .com/new-delhi is
a news site and gives information about Chath being
declared as Public as Public Holiday. Since the URL
is not related to query it is placed at the bottom.
A survey was conducted to check the relevancy of
the proposed algorithm. User’s perception of the two
systems were compared. In particular concentration
was on two aspects: user satisfaction with the search
and time of search to get the desired information.
Survey was conducted with a group of graduate
students. Volunteers were expected to select relevant
URLs satisfying their choice of preference on both the
systems and answer a questionnaire determining the
quality of two systems by comparing the two systems
as to in which of the two systems they were more
satisfied i.e they were able to get all the information
within first few URLs of the result-set and time
required to get the desired information with respect to
the given query.
It can be observed that proposed system
outperforms Google system in terms of user
satisfaction. The advantage of the proposed
mechanism is that user is able to retrieve all the
information within the first few URLs. While these
preliminary results are not highly significant statically
given the very small user study, but they are
promising. The proposed system seems to provide a
mechanism that can help retrieve high quality
documents with maximized user satisfaction.
V. CONCLUSION
Many users try to find desired information on the first
page of the search result only and results coming after
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 6 - September 2015
first search result page are nearly invisible for general
user.
Fig 3 : User Satisfaction Graph
If user does not get information on the first page they
consider the search to be a miss and try to reformulate
the query to find the desired result. Moreover, due to
deficiency in the page rank algorithms important
pages may lie in relative lower order in the results.
The mechanism proposed in this paper for computing
the page rank not only considers the link popularity
and keywords supplied in a query but adapts a
perspective view by considering the synonyms and
related query keywords, so that the pages that are
indirectly related to users query may also be
considered and be placed in the proper position in the
results. The advantage of the proposed mechanism is
that user gets the full information within the first few
URLs and will not have to go deeper into the search
results returned by the search engine.
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