Ontology Based Search Engine

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Web Site: www.ijettcs.org Email: [email protected]
Volume 4, Issue 5(2), September - October 2015
ISSN 2278-6856

Ontology Based Search Engine
Vasantha Kavitha1, Dr. M. Hanumanthappa2, Dr. B R Prakash3
1

Assistant Professor, Maharani Lakshmi Ammanni College for women, Bangalore.

2

Professor, Department of Computer Science & Applications, Bangalore University, Bangalore.
3

Assistant Professor, Dept. of MCA, Sri Siddhartha Institute of Technology, Tumkur.

Abstract
An ontology based search engine helps in identifying the most
efficient and useful result for the input query. The result
produced by the ontology based search engines are purely
based on the literal meaning of the word in the given
sentence. It does not take the keyword in the given sentence;
instead it takes the meaning of the query submitted. The
presence of huge amount of resources on the Web thus poses
a serious problem of accurate search. This is mainly because
today’s Web is a human-readable Web where information
cannot be easily processed by machine. Highly sophisticated,
efficient keyword based search engines that have evolved
today have not been able to bridge this gap. There are many
kind of techniques followed in implementing the ontology
based search engines. Here, in this paper we identify the some
of the techniques to be used in developing the search engine.
All of the techniques are different from one other and that the
efficiency is also different. These techniques form a special
pattern of accuracy and they are disused in the paper. The
difference in the working of the keyword based search engines
and the ontology based search engines are shown with
examples. Also the ontology based search engine that is build
up using the fuzzy logic ontology is considered here. An
ontology based search engine that is developed in many
steps with the help of multi crawlers is also taken into
consideration.

Key words: search engines, Ontology, Information
retrieval, fuzzy logic, crawler, Semantic .

I. INTRODUCTION
The current existing web mainly concentrates on the
human and the document available on web is also human
reliable one. Nowadays the web is not only used by
humans but also the software agents. This reality case
brought the usage of the semantic ontology based search
on web. Most of the traditional web users are not sure
about their query for which they need the search engine to
provide the results. Hence the normal keyword based
search will not be in a position to provide the accurate
search results to the user. In this situation we need a
semantically proven search engine. The figure I represents
the general framework of the semantic web. Here levels of
the query that must it pass through is clearly port rated.
When a user is not sure about the query he will provide
only relative words together and in that case the semantic
based search engine will compare the words and users the
relationship between those words to provide the result.
A search engine is a document retrieval system designed
to help find information stored in a computer system, such
as on the World Wide Web, inside a corporate or

Volume 4, Issue 5(2), September – October 2015

proprietary network, or in a personal computer. The
search engine allows one to ask for content meeting
specific criteria (typically those containing a given word
or phrase) and retrieves a list of items that match those
criteria.

Figure 1 Semantic Web Framework
In this case the result provided will be efficient and more
meaningful. Always the users will expect the desired
results to appear as the first result and the semantic based
search engine will provide the same means rather than
using the keyword based or context based search engines.
Recently many search engines that are semantically
proven are developed using ontology languages like RDF,
OWL, HTML. The paper compares the performance of the
search engines that are developed using these languages

2.SEMANTIC WEB SEARCH ENGINE
2.1 The working of a regular search engine
For most internet users, a search engine is the starting
point of finding desired information in the Web. The most
common form of text search used by the majority of
popular search engines on the Web is keyword based
search that is, they do their text query and retrieval using
keywords. The working of any regular search engine may
be summarized as follows:
 Search engine searches its enormous database for the
keyword - entered by the user (after pressing the
search button.)
 Every engine has its own collection system to fill its
database.

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Web Site: www.ijettcs.org Email: [email protected]
Volume 4, Issue 5(2), September - October 2015
ISSN 2278-6856
 Indexing system is used to organize the database permits faster searching
 Returns a list of hit -includes relevant (as well as
irrelevant) pages
 This keyword based search technique gives rise to
several problems listed as follows:
 The Web is growing much faster than any presenttechnology search engine can possibly index. In 2006,
some users found major search-engines became
slower to index new Web-pages.
 Keyword searches have a tough time distinguishing
between words that are spelled the same way, but
mean something different. This often results in hits
that are completely irrelevant to the query.
 Some search engines also have trouble with
stemming, i.e., if the word "big," is entered, should it
return a hit on the word, "bigger?" What about
singular and plural words? What about verb tenses
that differ from the word someone entered by only an
"s," or an "ed"?
 Search engines also cannot return hits on keywords
that mean the same, but are not actually entered in the
query. A query on heart disease would not return a
document that used the word "cardiac" instead of
"heart."
 Users are returned thousands to millions of Web
pages in return of their queries, of which majority
prove to be irrelevant to the query submitted and is
impossible for any user to go through.
In view of the above mentioned problems, come up the
concept of semantic Web and semantic Web search
engines.
2.2 Semantic Web and Semantic Search Engine
“The Semantic Web is the representation of data on the
World Wide Web. It is a collaborative effort led by W3C
with participation from a large number of researchers and
industrial partners. It is based on the Resource Description
Framework (RDF), which integrates a variety of
applications using XML for syntax and URIs for naming.”
– W3C Semantic Web. The Semantic Web is a framework
that allows publishing, sharing, and reusing data and
knowledge on the Web and across applications,
enterprises, and community boundaries [4]. Currently, the
Semantic Web, consisting of Semantic Web documents
typically encoded in the languages RDF and OWL, is
essentially a Web universe parallel to the Web of HTML
documents [5]. Knowledge encoded in Semantic Web
languages such as RDF differs from both the largely
unstructured free text found on most Web pages and the
highly structured information found in databases. Such
semi-structured information requires using a combination
of techniques for effective indexing and retrieval. RDF

Volume 4, Issue 5(2), September – October 2015

and the Web Ontology Language (OWL) which are
ontology based procedures or representing knowledge on
the Web, introduce aspects beyond those used in ordinary
XML, allowing users to define terms
(for example, classes and properties), express
relationships among them, and assert constraints and
axioms that hold for well-formed data. An application of
the emerging Semantic Web is a Semantic Web search
engine which searches the Semantic Web documents
against a user query for accurate results. Our work uses
RDF encoded Semantic Web documents which are
searched in response to a user query for exact results.
3. ONTOLOGY
An ontology is an explicit specification of some topic. For
our purposes, it is a formal and declarative representation
which includes the vocabulary (or names) for referring to
the terms in that subject area and the logical statements
that describe what the terms are, how they are related to
each other, and how they can or cannot be related to each
other. Therefore, Ontology provides a vocabulary for
representing and communicating knowledge about some
topic and a set of relationships that hold among the terms
in that vocabulary [2] [3].
Why develop an Ontology?
 To enable a machine to use the knowledge in some
application.
 To enable multiple machines to share their
knowledge.
 To help yourself understand some area of knowledge
better.
 To help other people understand some area of
knowledge.
 To help people reach a consensus in their
understanding of some area of knowledge.
In our project we used Resource Description Framework
or RDF to represent knowledge. For example, if we need
to describe a subject in terms of its classes and their
relationships using RDF, we are creating an Ontology.
As our project deals with the crops domain, the designed
ontology is shown in Figure 1. In Figure 2, the general
information ontology is depicted. A relational diagram is
shown in Figure 3 to depict some classes, instances, and
relations among them in the crops domain.

4. FUZZY ONTOLOGY BASED SEARCH
ENGINE
The paper [3] describes about providing an efficient
method using query refinement process. Once if the user
articulates an exact word for search, the search engine will
pull the correct result to the top of the page. The query
refinement process is implemented in the PASS
(Personalized Abstract Search Service) system. All the
time, the user cannot provide the correct word for search.
Hence to overcome this problem, in this paper, a method
of Fuzzy ontology has been implemented. This method
processing takes place in such a manner that it compares
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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Web Site: www.ijettcs.org Email: [email protected]
Volume 4, Issue 5(2), September - October 2015
ISSN 2278-6856
the search query based on keyword based retrieval and
also the results provided by the PASS method for that
particular user query. The final outcome will be purely
based on the comparison of those two results. The PASS
system will provide the abstract of the paper when the user
clicks on the link and also will provide the list of related
papers if they are available. To provide all the features
mentioned above, the PASS method is implemented in
two dimensions. One is using the structure of the domain
and other is using the knowledge of the user. For this
process WordNet dictionary is used. Document clustering
is the next function done here. And this is handled by
using the scatter gather algorithm. Also here, the cosine
technology has been used for constructing document
similarity networks. In the paper they have mainly
concentrated on the construction of fuzzy ontology and
query refinement process. The fuzzy ontology uses set of
terms with broader and narrower meaning. The broader
terms are the inverse of the narrower terms. This method
of construction is mainly carried out using the relation
between the broader and narrower terms in the query
given by the user. The literal motive is to bring out the
relationship of the terms.
Let C = (a1, a2, ¼ an) be a collection of articles ai, where
each article a = (t1, t2, ¼ tm) is represented by a set of
terms tj. Let occur (tj,a) denote the occurrence of tj in
article a. The membership degree of occur (tj,a) is defined
by moccur (tj,a) = f(|tj|), which in general is a function of
term's frequency of occurrence. In the information
retrieval community, the function f can be viewed as the
normalized within document term weighting method. Let
NT(ti, tj) denote that ti is narrower than tj. The
Membership
degree
of
NT
(ti,
tj),
represented
NT(ti, tj), is defined by
µNT(ti, tj) =
Σ µ occur (ti, a)
a ЄC
Σ

µ

µoccur(tj, a)

occur

(ti,

a)

(1)
a ЄC
In (1), Σ denotes a fuzzy conjunction operator. In current
implementation, we use a binary function for the f
function so that Σ occur(tj,a) = 1 if the occurrence
frequency of tj Σ 0, or Σ occur(tj,a) = 0 otherwise.
Using the binary function will turn Equation 1 into the
same equation regardless the selection of fuzzy
conjunction operator. Let BT(ti, tj) denote that ti is
broader than tj. Because the notion of broader term is
basically the inverse of narrower term notion, the
membership value of BT(ti, tj) is derived from the
membership value of
NT(ti, tj) BT(ti, tj) = Σ NT(tj, ti)

(2)

The fuzzy ontology construction is done in two major
steps. They are building fuzzy ontology from fuzzy

Volume 4, Issue 5(2), September – October 2015

narrower terms and by fuzzy ontology pruning. In the first
step, the membership values of two NT relations are
calculated. During this process the redundant terms,
meaningless terms and unrelated terms re found and
eliminated. In case of membership value being zero
indicates that the two terms are unrelated. tn the second
step of fuzzy ontology creation, next level of reducing the
relations is carried out by making an analysis on the set of
relations.
Table1:The Concatenation of query refinement
with Fuzzy Ontology
Sl.No

Ontology

Percentage

1

Related terms

37

2

Broader terms

47

3

Narrower terms

16

Finally, the experimental results show that this system is
built on the fuzzy ontology and automatic technique for
PASS system. The method collaboration is one of the idle
results provided in the paper. The efficiency of the system
can be improved even by combined use of PASS features.
Finally, the experimental results show that this system is
built on the fuzzy ontology and automatic technique for
PASS system. The method collaboration is one of the idle
results provided in the paper. The efficiency of the system
can be improved even by combined use of PASS features.

5. CRAWLING SEARCH
This method follows a crawler based search engine for
implementation and this architecture is called the PSSE
(Personalized Semantic Search Engine). The system
mainly concentrates on minimizing the processing time.
For this they have followed web page clustering.
Annotation agents and ontology matching are the concepts
utilized in this paper. Annotation is the process of just
assuming that the derived feature is correct the then
continue with the next level of processing.In the
architecture the processing phase is split up into two
different phases. One is working in online phase and other
is working in offline phase. In the offline phase the
crawling of web and pre-processing of pages takes place.
The first and foremost step in the architecture is the
crawling process. In the crawling process, as this
approach uses multi crawlers, they traverse the World
Wide Web and finds the web resources and finally stores
in their database. Here the crawler’s job is to find the
related links for the user query and provide them.
In the pre-processing stage the time consumption will be
less because the indexer will generate the graph for all
crawled pages. The graph will be acting as a special
cluster that holds similar data within each cluster. The
resultant cluster will be processed using link analysis
technique. This process is carried out for the authorization
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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Web Site: www.ijettcs.org Email: [email protected]
Volume 4, Issue 5(2), September - October 2015
ISSN 2278-6856
of each web documents. This can be done by using the
below given formula,

crawlers and fuzzy logic to form a new approach for an
efficient ontology based search engine.

REFERENCES

Also the annotation process can be done after the
measurement is carried out. The weight assignment for
each annotation can be done by using the calculation by
finding the relevancy of the document. This feature can be
concadinatedly. The calculation can be performed by
using the cosine function that is mentioned below.
Wij=tfij *log 2 (N/n)

(4)

COMPARISON BETWEEN THE METHODS:
Table 2: COMPARISON OF METHODS

Summary:
We have identify some of the techniques to be used in
developing the search engine. As these techniques are
different from one other and that the efficiency is also
different and also, these techniques form a special pattern
of accuracy as they are discussed. The normal search
engine is not satisfied by browsers. So we have compared
with Semantic web engine, Ontology search engine with
fuzzy Ontology, depending on the requirement. The paper
describes the comparison and analysis between various
methods involved in developing ontology based search
engines. It makes clear that the usage of ontology based
search engine will provide accurate results depending on
the literal meaning of the query and the semantic search
engines will produce results based on the query logic.
Future work can be implemented by combining the multi

Volume 4, Issue 5(2), September – October 2015

[1]. Jagendra Singh, Dr. Aditi Sharan,”A Comparative
Study between Keyword and Semantic Based Search
Engines” International Conference on Cloud, Big
Data and Trust 2013, Nov 13-15, RGPV School of
Computer & Systems Sciences Jawaharlal Nehru,
University New Delhi, India
[2]. Mrs. Rashmi A. Jolhe, Dr. Sudhir D. Sawarkar,” An
Ontology Based Personalised Mobile Search Engine”
Int. Journal of Engineering Research and
Applications ISSN : 2248-9622, Vol. 4,Issue 2(
Version 1), February 2014, pp.69-74 Department of
Information Technology, Datta Meghe College of
Engineering, Airoli, NaviMumbai
[3]. Dwi H. Widyantoro , John Yen, ” A Fuzzy Ontologybased Abstract Search Engine and Its User Studies”
Department of Computer Sciences Texas A&M
University College Station, TX
77843-3112, USA School of Information Sciences
and TechnologyPennsylvania State University
University Park, PA 16801-3857, USA, A. Gulli and
A. Signorini, “The Index able Web is more than11.5
,billion pages,” 2013.
[4]. Efrati and Amir, “Google Gives Search a Refresh,”
The Wall Street, Journal, 2012.
[5]. Guha, R. McCool and Miller, "Semantic Search,"
2011.
[6]. W. Roush, “Search beyond Google,” Technology
Review, 2004.
[7]. G. Antoniou and V. Harmelen, “A Semantic Web
Primer,” IT , Press Cambridge, Massachusetts, 2004.
[8]. Natalya F. Noy and Deborah L. McGuinness “A
Guide to Creating Your First Ontology”; Stanford U.
Report.
[9]. T. R. Gruber. “A translation approach to portable
ontologies. Knowledge Acquisition,”5(2):199-220,
1993.
[10]. T. Berners-Lee et al 2001. “The Semantic Web.
Scientific American”. May 2001.

AUTHOR
Vasantha Kavitha, MCA in 2005,
Mphil(Cs) in 2007, MBA in 20011 ,
MBADS in 2012. I earlier worked for
SJCIT, currently working in Maharani
Lakshmi Ammanni College.
Dr. M Hanumanthappa currently
working as Professor and Chairman,
in the departmetn of Computer
Sciecne and Applications, Bangalore
University, Bangalore. He is Having
more than 20 years of expereince in Teachin and
Research. He is publised More than 100 Papers in
Conferences and Journals
Page 97

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Web Site: www.ijettcs.org Email: [email protected]
Volume 4, Issue 5(2), September - October 2015
ISSN 2278-6856
Dr. B R Prakash currently working
as a Assistant Professor in the
Department of MCA, Sri Siddhartha
Institute of Technology, Tumkur. He
completed Ph.D from Bangalore
University, Bangalore. He is published more than 35
papers in Reputed Conference and Journals.

Volume 4, Issue 5(2), September – October 2015

Page 98

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