Enhance Crawler For Efficiently Harvesting Deep Web Interfaces

International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 5 Issue: 10 117 – 121
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117
IJRITCC | October 2017, Available @ http://www.ijritcc.org
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Enhance Crawler For Efficiently Harvesting Deep Web Interfaces
Sujata R. Gutte
M.E. CSE Dept
M. S. Bidwe Egineering College,
Latur , India
e-mail: omgutte22@gmail.com
Shubhangi S. Gujar
M.E. CSE Dept
M. S. Bidwe Egineering College,
Latur , India
e-mail: shubhangigujars@gmail.com
Abstract— Scenario in web is varying quickly and size of web resources is rising, efficiency has become a challenging problem for crawling
such data. The hidden web content is the data that cannot be indexed by search engines as they always stay behind searchable web interfaces.
The proposed system purposes to develop a framework for focused crawler for efficient gathering hidden web interfaces. Firstly Crawler
performs site-based searching for getting center pages with the help of web search tools to avoid from visiting additional number of pages. To
get more specific results for a focused crawler, projected crawler ranks websites by giving high priority to more related ones for a given search.
Crawler accomplishes fast in-site searching via watching for more relevant links with an adaptive link ranking. Here we have incorporated spell
checker for giving correct input and apply reverse searching with incremental site prioritizing for wide-ranging coverage of hidden web sites.
Keywords- Focused Crawler, Hidden Web , Feature Selection, Ranking;
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I. INTRODUCTION
Net search tools attempt to get data as relevant as possible.
One of the parts of net search tools is the Web Crawler. A web
crawler is a program that goes across the net assemble and
collect data in a database for further analysis. The method of
web crawling includes gathering pages from the net and
organizing them out in a manner that the search engine can use
them efficiently.
The objective is to do it fast and efficiently without much
barrier with the operation of the remote server. A web crawler
starts with a URL also called as seed. The crawler visits the
links in the list and it also looks for hyperlinks to additional
web pages. It then adds them to the present list of URLs in the
list. This procedure of crawler visiting URLs depend on upon
set of rules for the crawler. Usually, crawler crawls the URLs
in the present list incrementally. In addition to that, crawler
gather data from the Web Pages. This paper is organized into
following sections. Section II describes the investigates done
on focused crawlers earlier. Section III states the goal of
proposed architecture for focused crawler. in section IV
Methodology, algorithms are described. Initial result obtained
is show in section V.
II. LITERATURE SURVEY
Form-Focused Crawler (FFC) [6] and Adaptive Crawler for
Hidden-web Entries (ACHE) [7] mechanically search online
databases for a defined topic. FFC includes link, page and form
classifiers for focused crawling of web forms and ACHE is an
improvement of FFC with modules for form filtering and
adaptive link learner. The FFC and ACHE are focused crawlers
planned for searching concerned deep web interfaces. FFC
Crawler performs a broad search by focusing the search on
definite topic efficiently by learning to detect promising links.
The crawler uses two type of classifiers the page and the link
classifier to conduct the search. And the third classifier, the
form classifier filters out unusable forms. The page The hidden
web content is the data that cannot be indexed by search
engines as they usually stay behind searchable web interfaces
[2]. A Crawler encounters a variety of web pages during a
crawling procedure . For efficient crawling and wide-ranging
coverage, ranking and prioritizing links of dissimilar sites is
necessary. In earlier work two types of crawlers were proposed,
these crawlers are generic crawlers and focused crawlers.
Generic crawlers are mostly created for representing hidden
web and construction of directory for hidden web resources.
The search is not limited to a specific topic, but tries to get all
searchable forms [3,4]. Thus, generic crawlers gathers all
searchable forms and does not focus on a exact topic. Focused
Crawler is a web crawler for fetching web pages that are related
to a particular area of interest [5]. It gathers the documents that
are related to a given subject. It is called as a Topic Crawler as
the outcome of the way it works. The focused crawler decides
the significance of the document before crawling the page. It
predicts if the given page is relevant to a particular topic and
how to proceeds. The essential point of preference of this sort
of crawler is that it needs less tools assets.
Form-Focused Crawler (FFC) [6] and Adaptive Crawler for
Hidden-web Entries (ACHE) [7] automatically search online
databases for input topic. FFC includes link, page and form
classifiers for focused crawling of web forms and ACHE is an
improvement of FFC with works for form filtering and
adaptive link learner. The FFC and ACHE are focused crawlers
intended for searching interested hidden web interfaces.FFC
Crawler performs a broad search by focusing the search on
specified topic well by learning to identify promising links. The
crawler uses two classifiers the page and the link classifier to
perform the search. And the third classifier, which is the form
classifier filters out useless forms. The page classifier is trained
so as to categorize pages according to topics specified. When
the crawler fetches a page links are extracted from homepage.
The link classifier examines links which are extracted from
topic specific pages and adds them to the crawler frontier
according to their precedence. The link classifiers trained to
identify links that can to lead to pages which consist of
searchable form interfaces. But this approach requires huge

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manual tuning, also appropriate features needs to be selected
and the link classifier has to be created. ACHE framework was
projected where crawlers could learn patterns of relevant links
automatically and accordingly adapt their focus while crawling
the net. This method compact work of manual setup and tuning
which was a major drawback of FFC crawlers. Adaptive
learning strategy efficiently manages the exploration of
acquired knowledge with the exploitation of links with
unidentified patterns, this makes crawling process robust and
corrects biases introduced in the learning process. Since this
crawler learn from scratch, it is able to get harvest rates that is
equal or even extra than the manually configured crawlers, so
the ACHE framework reduce the effort to configure a crawler
manually. ACHE enhanced FFC by incorporating an adaptive
link learner and automatic feature selection method. G.
Almpanidis, C. Kotropoulos and I. Pitas [8] presented a latent
semantic indexing classifier that incorporates link analysis
along with text content in order to redeem and index the
domain explicit net documents. Gunjan H.Agre and Nikita V.
Mahajan [9] introduced extraction of links on the basis of
search criteria or keyword. It fetches links of web pages that
comprises searched keyword in their resources. It gives priority
only to those pages and does not fetch web pages unrelated to
search keyword. It enhances search effectively with more
accuracy, thus providing high optimality as compared to the
long-established web crawler. The main characteristic of
focused crawler is to collect topic related pages. The earlier
crawling experience can help crawler to build knowledge base
and learn from it, so that it can get better its performance. Niran
et al [10] presented an algorithm that built knowledge bases
using seed URLs, keywords and URL guess. These knowledge
bases helps crawler to learn and produce the result in more
well-organized way. The knowledge bases are incrementally
built from the log of previous crawling. Seed URLs allow the
sycophant to collect as many related web pages as possible.
Keywords support the crawler to recognize proper documents.
URL prediction enables the crawler to guess the relevancy of
the content of unvisited URLs. Qingyang Xu et al [11]
introduced a new general structure for focused crawler that is
based on “relational subset discovery”. To describe the
significance in between the pages that are not visited in the
crawl frontier predicates are used, after that first order
classification rules are introduced using subgroup discovery
method. Then the learned relational rules guide crawler with
sufficient support and confidence.
A new advance for predicting the links that lead to
relevant pages based on a Hidden Markov Model (HMM) was
presented by Hongyu Liu et al [12]. The method includes three
stages: user data collection, user modelling using sequential
pattern learning, and focused crawling. Initially internet pages
are collected while browsing through web. Then these
WebPages are clustered, and then hierarchical linkage pattern
within pages from dissimilar clusters is then used to learn
sequence of pages that lead to target pages. The Hidden
Markov Model (HMM) is used for learning procedure. During
the crawling process the precedence of links are decided on
basis of a learned estimate of how likely the page will guide to
a target page. The performance was compare with Context-
Graph crawling during experiments it was establish that this
approach performed better than Context-Graph crawling.
III. METHODOLOGY
The hidden web contents are the information content that
cannot be indexed by search engines as they stay behind
searchable web interfaces. Current hidden web directories
mostly have less coverage of related web resources which
degrade their ability. A Crawler goes across a no of variety of
web pages during a crawling process. Hence to do efficient
crawling and wide coverage , ranking and prioritizing links of
different sites is necessary. The objective of this method is to
extract hidden web information with wide coverage for hidden
web resource and uphold efficient crawling for focused web
crawler. Spell checker is apply to input for getting correct
input and then reverse searching is performed for getting
center pages of known website. Incremental site prioritizing
algorithm use to precisely rank the relevant links. User is then
provided with the top ranked links available in searchable
form.
Fig. 1.Flow of Proposed System
The method existing in this paper is based on the framework
proposed by Feng Zhao, Jingyu Zhou, Chang Nie, Heqing
Huang and Hai Jin [1]. The system consist of a structure with
two stages to address the problem of searching for hidden-web
resources. The two stages framework of Crawler contains, site
locating and in-site exploring as shown in Figure2. The site
locating stage discover sites relevant to the given topic.
Depend upon the similarity and frequency of sites with known
seed sites. Site Similarity means how many content are
similar to the known sites in database And The site frequency
Spellcheck Algorithm
Search Keyword
Crawl The Extracted Link
Web Page Fetching
Parsing Center Pages
Checking Page Rank
Display The relevant link

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determine the number of presences of sites into another sites.
The site frequency is defined in equation 1.
SF(s) = ∑ Ii (1)
where Ii = 1 if s appears in a known hidden web site, else Ii =
0.
If a site is consider as topic relevant, further operation of in-
site exploring is done to see searchable forms. An adaptive
learning algorithm does online feature selection and build link
rankers automatically using those features. Each feature
context can be represented as array of terms having specific
weight. The weight of term t is represented as vector of term .
And weight w of term was given in equation 2.
Wt,d = 1 + logtft,d (2)
Where tft,d denote the no of count of appearance of term t in
document d. Adaptive learning method updates this
information collected successfully during crawling.
a) Site Locating - This stage finds the related sites for
specified topic by classifying and ranking them. Site
Collecting strives to maximize the number of hidden websites
and minimize the number of visited URLs. To maximize the
number of hidden websites links in the fetched pages is not
enough because websites generally contain less links to other
sites. As a solution to this problem reverse searching and
incremental two-level site prioritizing strategy are used to get
additional sites. Reverse searching is performed using seed site
in the crawler list (Site collecting).The links are extracted after
parsing of the page getting from the search engine. These
pages are then downloaded and analyzed to find if the links are
related. Then all related links found are intermediate output.
To achieve incremental crawling and reach wide coverage
Incremental Site Prioritizing is done using High priority
Queue Hq and Low priority Queue Lq. The knowledge that is
the deep websites or links gained through crawling are
recorded. Then, unvisited sites are assigned to Site Frontier
and are prioritized by Site Ranker and visited sites are
included into the fetched site list. Once Site frontier gets
enough sites, Site Ranker allocates a score to the sites
depending on how relevant they are to the topic. Site Classifier
function will classify the sites according to the search query. If
site is found to be related to the topic, crawling process is
carried on. Else, that site is abstained and a new site is drawn
from the frontier in its place. Topic relevance of a site is based
on the matters of its homepage. The homepage content of
newly fetched site is collected and parsed by removing stop
words and stemming. Then a feature vector is created for the
site the resulting vector is given as input to the Classifier to
choose if the page is relevant to the topic or not.
b) In-Site Exploring - In-Site Exploring gather the searchable
forms in the ranked web sites provided by site locator. Link
Ranker offer high relevance score to the links pointing to the
pages with searchable form. It gives priorities to the links so
that crawler can find searchable forms rapidly. Form Classifier
classify the irrelevant and non search able forms
c) Feature selection - Feature selection determines new search
patterns. Feature space of hidden web sites (FSS) and Feature
space of links (FSL) are used for constructing feature space
and training data. The feature space of hidden web sites (FSS)
is given as, FSS = (U, A, T)
where U, A, T are vectors which maps to the feature context of
URL, anchor and text around URL of the hidden web sites.
The feature space of links of a site (FSL) is given as,
FSL = (P, A, T)
where A and T are anchor and text around links of deep web
sites and P is a vector related to the path of the URL , because
all the links of a particular site have same domain.
Fig. 2. System Architecture for Hidden(Deep) Web Crawling
A. Algorithms
Following are two algorithms used for deep web crawling.
Here reverse searching algorithm proposed by Feng Zhao et al
[1] is used as base. Before applying this algorithm spell
checker method is used for giving correct input to the system.
a) Reverse searching algorithm:-
Reverse searching algorithm
uses Seed sites and harvested deep web sites (Dws) and Web
pages (Wp) to maximize the number of deep web sites in
crawling process and provide the relevant sites.
Input : Seed sites and harvested deep websites
Output: Relevant sites
Step 1. While of candidate Ws< Threshold do
Step 2. Ws= get(Dws)
Step 3. ResultPage = ReverseSearch(Ws)
Step 4. L= ExtractLinks(ResultPage)
Step 5. For ∀ L do
(Wp) = DownloadPage(L)
Relevant = Classify(Wp)
Step 6. If Relevant then
RelevantSites = ExtractUnvisitedSite(Wp)
Step 7. Provide Relevant Ws
End
b) Incremental Site Prioritizing algorithm:-
This algorithm uses learned patterns of deep websites to
achieve broad coverage on websites Ws and provides the topic

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relevant links on the basis of crawling status Cs and page rank
PR.
Step 1. While SiteFrontier ≠ empty do
Step 2. If Hq ≠ Empty then
Step 3. Hq.Add All(Lq)
Step 4. Lq.Clear()
end
Step 5. (Ws) = Hq.poll()
Step 6. Relevant = ClassifySite(Ws)
Step 7. If Relevant then PerformInSiteExploring(Ws)
Output forms and OutOfSiteLinks
SiteRanker.rank(OutOfSiteLinks)
Step 8. If forms ≠Empty
Hq.Add (OutOfSiteLinks)
else
Step 9. Lq.Add(OutOfSiteLinks)
Step 10. Check(Cs)
Step 11. Calculate(PR)
Step 15. Sort(PR,Cs)
Step 16. Provide the relevant Links in descending order.
Here Fi is input function, Fi= {Q,N,Ws} Where Q=Search
keyword by the user , N = Number of search results required ,
Ws = Web Sites , Fo is output function,
Fo- {L} Where L= Links Retrieved,
F= {Pf ,PR,Rr} Where Pf (Ws,L) - Fetch Webpages and new
Links.
PR(Ws,L) - Ranking of webpages
PR(A)=(1-∂) + ∂ (PR(T1)/C(T1) + ... + ∂ (PR(Tn)/C(Tn) (3)
where,
PR(K)= PageRank of page K.
PR(Ti)= PageRank of pages Ti which link to page A.
C(Ts)= Number of outbound links on page
∂ = damping factor which can be set between 0 and 1.
IV. EXPERIMENTAL RESULT S AND ANALYSIS
The proposed system implementation is done on a machine
having 4 GB RAM , Dual core processor, 4 GB RAM and
500GB Hard disk memory for storing URL data is required.
Internet connection speed required is minimum 1Mbps for fast
hidden web crawling. For security reason it neglects some
encrypted or protected URLs from crawling process by hard
coding their URL names. Minimum threshold value for
number of deep websites is indicated.
To calculate the performance of our crawling framework, we
compare Proposed Crawler to the SCDI(site-based crawler for
deep web interfaces) .
SCDI : This system is similar to Smart Crawler, SCDI shares
the same stopping criteria with Smart Crawler. Only difference
is , SCDI follows the out-of-site links of related sites by site
classifier without employing incremental site prioritizing
technique. It also does not employ reverse searching for
collecting sites. prioritizing strategy for sites and links.
Smart Crawler: This is our proposed crawler for harvesting
deep web interfaces. Smart Crawler uses an offline-online with
offline-online learning to train link classifiers. It leverages
learning results for site ranking and link ranking.
Fig 3 shows comparison result between SCDI and proposed
system.
Fig. 3. Comparison of Smart Crawler with SCDI method
Fig. 4. Total no. of internal sites related to that sites
Table 1
Experimental result
id Search key
word
ExTime in Mili Sec Total number
of main sites
crawled
1 Music 21523902363 25
2 Apartment 60982853685 15
3 Airfare 25043328930 21
4 Book 79943724779 30
5 People 69693107146 21
6 America 17209862449 19
Fig. Using different keywords of variable length experiment
was done to explore the efficiency of proposed system. Google
API is used to acquire the center web sites. The links around
those center websites are crawled using the Incremental Site
Prioritizing with the reverse search technique to get more links
relevant to the topic. The results gained as per experimental

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method explained above are given in Table 1. Number of links
crawled shows the number of hidden web links harvested
during the crawling process. For key word people crawler
crawled no of link and display 21main sites and for each sites
crawler perform crawling and displayed all internal link of
that related sites in fig 5. Similar results were found for
keywords Music, Apartment, Airfare and Book.
V. CONCLUSION
In this system an enhanced framework is proposed for hidden
web harvesting framework for hidden-web interfaces. The
method proposed here is expected to provide efficient
crawling and more coverage of hidden web. So it is a focused
crawler, its searches are topic related and it can rank the
fetched sites. Reverse searching and Incremental Site
Prioritizing prioritize the relevant links according to the page
ranks and displays links with higher page rank, so that it
achieves accurate results. The proposed system is expected to
achieve more harvest rates than other crawlers
REFERENCES
[1] Feng Zhao, Jingyu Zhou, Chang Nie, Heqing Huang, Hai
Jin,“SmartCrawler: A Two-stage Crawler for Efficiently
Harvesting Deep-Web Interfaces”, IEEE Transactions on
Services Computing 2015.
[2] YeyeHe , Dong Xin , VenkateshGanti, “Crawling Deep
Web Entity Pages”, ACM 2013.
[3] “Information Retrieval”, by David A. Grossman and
OphirFrieder.
[4] “Modern Information Retrieval”, by Ricardo Baeza-Yates
and Berthier Ribeiro-Neto.
[5] SoumenChakrabarti, Martin Van den Berg, and Byron
Dom, “Focused crawling: a new approach to topic-specific
web resource Discovery”, Computer Networks, 1999.
[6] L. Barbosa and J. Freire., “Searching for Hidden-Web
Databases”, In Proceedings of WebDB, pages 1-6, 2005.
[7] Luciano Barbosa and Juliana Freire, “An adaptive crawler
for locating hidden-web entry points”,In Proceedings of the
16th international conference on World Wide Web, pages
441-450.ACM, 2007.
[8] G. Almpanidis, C. Kotropoulos, I. Pitas, “Combining text
and link analysis for focused crawling-An application for
vertical search engines”,Elsevire Information Systems
2007.
[9] Gunjan H. Agre, Nikita V. Mahajan, “Keyword Focused
Web Crawler”, IEEE sponsored ICECS 2015.
[10] NiranAngkawattanawit and ArnonRungsawang, “Learnable
Crawling: An Efficient Approach to Topic-specific Web
Resource Discovery” , 2002.
[11] QingyangXu ,WanliZuo, “First-order Focused
Crawling”,International World Wide Web Conferences
2007.
[12] Hongyu Liu, Jeannette Janssen, EvangelosMilios, “Using
HMM to learn user browsing patterns for focused Web
crawling” ,Elsevire Data Knowledge Engineering 2006.

Enhance Crawler For Efficiently Harvesting Deep Web Interfaces

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