Comparative analysis of relative and exact search for web information retrieval

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 09 | September-2015, Available @ http://www.ijret.org 136
COMPARATIVE ANALYSIS OF RELATIVE AND EXACT SEARCH
FOR WEB INFORMATION RETRIEVAL
Yagnesh D. Dave1
, Bijendra S. Agrawal2
1
Associate Professor, Shri Chimanbhai Patel Post Graduate Institute of Computer Applications,Gujarat,India
2
Director, Kalol Institute of Management, Gujarat, India
Abstract
The volume of data on web repository is huge. To get specific and precise information for the web repository is a big challenge.
Existing Information Retrieval (IR) techniques, given by contemporary researchers, are very useful in field of IR. Here, the
authors have implemented and tested two of the techniques from the fields of IR. The authors dealt with Relative Search and Exact
Search techniques one by one. Initially relative search tested on web repository data using web mining tool and then its results
are analyzed. In the same manner, the exact search technique of IR tested on web repository data and the results are measured.
The researchers have experienced the significant importance on exact search and relative search. The focused of the research
paper is to retrieve relevant information from the web information repository. With the use of two searching criteria these can be
done. With the use of the suggested methods the searchers may retrieve a relevant web data in a fewer time.
Key Words: Web data Mining, Exact Search, Relative Search, PR, TM, CD, VSM and TASE.
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1. INTRODUCTION
The search engine is a common tool used for information
retrieval from web repository. Information for multiple
types are available on the public channels. The difficulties
are to get exact or relative expected search responses
depending on the searchers criteria. The web information
retrieval involves three types of search systems: exact,
relative and adaptive search. In this work relative and exact
searches are analyzed for information retrieval.
The relative search will produce a resultant web data
depending upon the likeliness from web repository. This
method works on probabilistic model [1] which can
summarize the ranks from either web page or web data.
With the use of this, it can produce the nearest result.
The exact method compares the word with the already
stored word in database. In the database, there works on two
fields: web page name and keyword which consists of some
certain kinds of keywords. The keyword belongs to
classification dictionary which built from the domain
sitemap [2]. This approach is also work on classification
dictionary. Each entry of the classification dictionary
contains a term-category pair, the contingency table for that
pair and its calculated strength of association. The dictionary
also consists of all possible term-category pairs with at least
one searching content, category outcome.
2. RELATED WORK
Thangaraj,et. al. have indicated the ontology repository and
thesaurus to get semantic web search for relative search in
Information Retrieval[3]. Zhao et. al have given a
framework emphasized on targeted data with the use of web
crawled web pages and also for performing retrieved data
depended on the location base rank. They focused on web
locations and keywords from web information repository
and compared keywords with its specific web page location
in a pair[4]. Lin,et.al have targeted for retrieving web data
depended on the retrieval time from specific web page by
using temporal-textual Web queries. They proposed Time-
Aware Search Engine [5]. Roy et. al. have proposed web IR
technique content and intend for topic of query and explicit
use of the word respectively[6]. Francès et. al. have
suggested a technique for improving the document
replication in a concern to web distribution techniques on
the basis of cost and time effectiveness[7].
3. PROBLEM STATEMENT
From the understanding of the literature review, it is found
that for obtaining the optimize and relevant retrieval
searcher has to choose the right technique. The significance
part of the research work has dealt with the comparison of
two different techniques. The authors focused on retrieval
results and also experimenting techniques to summarize
with the retrieval results.
4. PROPOSED WORK
The purposed work has targeted to compare and analyzed
the two different information retrieval methods for
identifying the efficient retrieval from web information
repository.
4.1 Methodology
The undertaken relative search methodology searches the
word by opening the file before comparing all the word in
the file. If the search word matches with the word in the file
then it will be listed to the file. In database there be two field

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that are URL field which would be a filename and keyword
which consists some certain kinds of keywords.
With the use of Term match, it produces a rank for retrieval
depending upon the different available web source
categories, which finds similar term in the web files
available on web site or web domain. It also generates the
relevance number of nodes for retrieving ranked based web
retrieval in listed manner:
1. For identifying related categories,
a. Determine the number of no duplicate term
based on different category in a query.
b. Determine the total frequency of no duplicate
term from different title from the entire
available web domain.
c. Calculate the ratio of available web domain
with the use of related term from the different
categories.
2. Based on the above steps, assign rank from the step a,
b and c in descending order for obtaining result from
relative search.
It is to point that the retrieval rank retrieved by different web
categories and these may contain similar types of relative
data which the searcher is looking for. In this research
paper, the authors have used categories available on web file
are like document, paragraphs, sentences, files. Based on
that ranking approach is quite producing a same result how
the website is producing. Only difference is that this uses a
combination of category and content match. The expansion
from original retrieval query containing the identification
depending on the basis of maximum retrieval occurrences,
and also consisting titles and descriptions from the top three
retrieval occurrences from most retrieving categories. The
term weight of the expanded query vector, which is
computed by multiplying term category.
Another methodology is based on the exact search with the
objective of comparative analysis searches the word by
compare with the already stored word in database. If the
search word matches with database word then it will be
listed to the file. In implemented database there are two
fields: one is URL field which is being a filename and
second one is keyword which consist some certain kinds of
keyboards.
To use this method for retrieving web data, the classification
method (CD) should be defined from the web domain on
which the web pages are resides. This methods works on the
association model which can be better for handling
performance issue in all criteria. To find the exact retrieval,
the searcher has to move from the web content to web page
and its categories. The following conditions are defined
below:
[Searching Content,
Category]
[Searching Content] ¬
[Category]
¬[Searching Content],
[Category]
¬[Searching Content]¬
[Category]
Each entry of the classification dictionary contains a term-
category pair, the contingency table for that pair, and its
calculated strength of association. The dictionary entries
consist of all possible term-category pairs with at least one
Searching Content, Category outcome.
5. RESULTS AND ANALYSIS
5.1 Exact Search Result
To retrieve data from the web, the first approach that the
researcher has used is exact search, where the researcher has
identified data by the keyword. All the results shown under
the head of exact search are retrieved based on the keyword
retrieved. Researcher retrieved results from all the phases
i.e. documents, paragraphs, sentences and files.
Fig-1 Agglomeration order after relatively
The Fig- 1 summarizes the relative frequencies retrieved
keywords from the selected data and it summarizes to the
maximum to minimum relative keyword found from the
resultant web data. As seen in classification, the retrieval
results are vast but the authors have removed clusters from
the table which retrieved in figure 1.

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Fig-2 Classifications of Most Frequent Data
The Fig- 2 retrieved a classification of relative data from the keywords and it shows the directions and action name have more
weight age then any one have.
Fig- 3 Classifications of Occurrences from Relevance Data
Fig- 3 shows the classification of occurrences of retrieval data depending upon the relevance. It identified the
likelihood of retrieval based on the relevance.
Fig- 4 Classifications of Frequencies of Relevance Data
Fig- 4 summarizes the classification chart of a frequency obtained by each case. It shows that the activity name occurred more
than 75% in vol_2_module which colored as red and the blue colored activity obtained 4% in class_1_outline.

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Fig- 5 Rate of Nonverbal per 1000 words
The Fig- 5 summarized that the vol_1_modules retrieved more non verbal resultant data i.e. 4.63%. it summarized that with the
relative search it also retrieved non verbal keyword on the basis of relevant search.
Fig- 6 Rate of verbal per 1000 words
The Fig- 6 summarized that the resultant data retrieved in the chart found the verb from the test data and found that good writing
guide and the good writing guide sociology have more verbs than any case has.
Fig- 7 Occurrences By Files
The Fig- 7 retrieved most occurrences keyword by each file and found that the different colored defined as keyword which
mapped in each different file. It identified that the vol_2_module.and Act_communication have more occurrences then
critical_writing test case.

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Fig- 8 Exact Searches With Case Occurrences By File
The Fig- 8 shows occurrences of retrieval data depending
upon the exact search retrieval and found that the four web
retrieval have common value i.e.1.
Fig- 9 Exact Searches With Word Frequency By File
Fig- 9 retrieved web information from the sentences
containing in a file and focused that the interpersonal
module have more frequencies than the rest. It achieves
more than 200%.
Fig- 10 Exact Searches By Case Occurrences From
Sentences
Fig- 10 containing the exact searches from the case
occurrence from the sentences containing in a test data and
found that the black colored are the file which is in active
and summarized that the all the mentioned web data files are
obtaining 100% result for a specified keyword.
Fig- 11 Exact Searches With Rate Per 10000 By File
The Fig- 11 shown a result based on rate per 10000 words
by file. It found that more than 180% achieves in the file
from interpersonal module and then it immediately move
down to 45% in nqimch08 and then increases in
vol_2_module in 60%.

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Sentences
The Fig- 12 retrieved web resultant data and found number
of case occurrences and found that the test files are
obtaining 100% exact result.
Fig- 13 Exact Searches By Category Percentage From
Sentences
The Fig- 13 shows that retrieval based on the category
percentage and from the selected test data, the vertical bar
shown the files (test file) which obtained 100% result based
on the category.
Sentences
The above Fig- 14 shows that case occurrences found by the
sentences. It is clearly indicating that these sentences are
classified by predicted classes and based on that the case
occurrences are identified by the paragraphs.
6. CONCLUSION
The result analysis led to conclude that to exact search,
searcher is expected to find the minimal retrieval in fewer
times, where as in the relative search the searcher get vast
retrieval and will also take bit more time than exact search.
In the exact search will not work on uncertain or unclear
keyword. In contrast to this, relative search gives
comparatively improved response option and is expected to
more possible values that the searcher is looking for from
the web information repository. While using the relative
search it also require more time to filter out the relevant
resultant data. Both the approaches are useful in text and
link based retrieval process. The extensibility of relative
search may give more options to retrieve from web domain.
The exact search produces better outcomes from different
sources like web documents, paragraphs containing in the
web documents, sentences containing the paragraphs. Thus
with the use these option searcher retrieves the better
outcome as compare to relative search. The obtained result
has justified and validated that the targeted outcomes tht
has been attained with integration/fusion of approaches
rather than their individual uses.
REFERENCES
[1] Robertson, S.E. Maron & Cooper (1982), Probability of
Relevance: A unification of two competing models for
document retrieval. Information Technology: Research
and Development,1,1-21.
[2] McCallum, A. Rosenfeld, R.,Mitchell & A.Y.
Improving text classification by shrinkage in a
hierarchy of classes. Proceeding of the 15th
international
conference on Machine Learning,359-367.
[3] Thangaraj, M., and G. Sujatha. "An architectural design
for effective information retrieval in semantic web."
Expert Systems with Applications 41.18 (2014): 8225-
8233.
[4] Zhao, Jie, et al. "Exploiting location information for
web search." Computers in Human Behavior 30 (2014):
378-388.
[5] Lin, Sheng, et al. "Exploiting temporal information in
Web search." Expert Systems with Applications 41.2
(2014): 331-341.
[6] Roy, Rishiraj Saha, et al. "Discovering and
understanding word level user intent in Web search
queries." Web Semantics: Science, Services and Agents
on the World Wide Web 30 (2015): 22-38.
[7] Francès, Guillem, et al. "Improving the efficiency of
multi-site web search engines." Proceedings of the 7th
ACM international conference on Web search and data
mining. ACM, 2014.

Comparative analysis of relative and exact search for web information retrieval

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