International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 1 - Mar 2014
Ontology Learning for Semantic Web using
Lexical-Semantic Method
Senduru Srinivasulu1
Research Scholar, Dept of IT,
Sathyabama University,
Chennai, Tamil Nadu, India.
P. Sakthivel2
Associate Professor,Dept of ECE,
Anna University,
Chennai, Tamil Nadu, India.
Abstract - In recent years, web has evolved from global
information space where data has linked well. Linking Open
Data project has enabled a large number of semantic datasets
to be published on the web. Due to the open and distributed
nature of the web both schema and instances of published
datasets may have heterogeneity problems. In order to
overcome these problems we use semantic technologies such
as Ontology, RDF, Xml and OWL. This paper proposes how
to learn an ontology and solve these heterogeneous problem.
We also use taxonomic and partonomic relations to learn the
ontology. This project also uses lexical semantic analysis to
identify relationship. Lexical method retrieves the words
which are having multiple meaning.
Keywords: Ontology, semantic web, Lexical semantic
I. INTRODUCTION
The Semantic Web is a community oriented
development headed by global measures form the World
Wide Web Consortium (W3c). The standard pushes regular
information organizes on the World Wide Web. By
empowering the incorporation of semantic substance in
web pages, the Semantic Web(fig. 1) points at changing
over the present web, commanded by unstructured and
semi-organized records into a "web of information". The
Semantic Web stack expands the W3c's Resource
Description Framework. The idea of the Semantic Network
Model was structured in the unanticipated 1960s by the
cognitive researcher Allan M. Collins, etymologist M. Ross
Quillian and analyst Elizabeth F. Loftus in different
productions as a structure to speak to semantically
organized learning. It expands the system of hyperlinked
comprehensible web pages by embeddings machineintelligible meta information about pages and how they are
identified with one another, empowering computerized
operators to enter the Web all the more adroitly and
perform errands for the benefit of clients. The expression
"Semantic Web" was begat by Tim Berners-Lee,[3] the
creator of the World Wide Web and executive of the World
Wide Web Consortium ("W3c"), which supervises the
advancement of proposed Semantic Web norms. He
characterizes the Semantic Web as "a web of information
that could be transformed straightforwardly and in a
roundabout way by machines." As web is moving towards
Web 2.0 (Semantic Web), it is moving towards speaking to
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Amala.T3
PG Students, Dept of IT,
Sathyabama University,
Chennai, Tamil Nadu, India,
things according to their importance (semantic
representation). In the meantime semantic web is
additionally a developing zone to increase human thinking.
Resource Description Framework (RDF) which is semantic
web innovation that could be used to manufacture
productive and adaptable frameworks for Cloud .Semantic
web gives a schema for control of cloud utilization. The
brought together RDF can displace the dissimilar cloud
database models [6]. By utilizing ontologies, semantic web
points at gathering organized data from web pages and
redirect the data to customer side to provide food
necessities and inclination of the unique clients. Semantic
web likewise decreases expense and unpredictability of
distributed computing by the utilization of tenets set down
in the issue of security, one of the significant barricades in
the accomplishment of distributed computing, is resolvable
by an extensive variety of security components that the
semantic web gives. Web mining is currently an actuality
and the test is to complete semantic web mining[6]. It is
about machine justifiable web pages to make the web more
sagacious and fit to furnish administrations to the client.
This methods data on the web must be mined so the
machine can comprehend the substance . When all is said
in done, ontology (claimed ahn-TAH-luh-djee ) is the study
or worry about what sorts of things exist - what elements
there are in the universe. It determines from the Greek
onto(being) and logia (composed or spoken talk). It is a
limb of mysticism , the investigation of first standards or
the quintessence of things. In data engineering, an ontology
is the working model of substances and associations in
some specific realm of learning or practices, for example,
electronic business or "the action of arranging." In
counterfeit consciousness ( AI ), an ontology is, as per Tom
Gruber, an AI authority at Stanford University, "the detail
of conceptualizations, used to help projects and people
offer information." In this use, an ontology is a situated of
notions -, for example, things, occasions, and relations that are specified somehow, (for example, particular
regular dialect) to make a concurred upon vocabulary for
trading data Ontology’s are viewed as one of the mainstays
of the Semantic Web, in spite of the fact that they don't
have a generally acknowledged definition. A (Semantic
Web) vocabulary could be recognized as an uncommon
type of (typically light-weight) ontology,[7] or now and
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again additionally just as a gathering of Uris with a
(generally casually) portrayed importance. Ontology's on
semanticweb.org are typically thought to be joined by some
record in a formal ontology dialect, however some
ontology's don't use institutionalized configurations for that
reason.
Figure 1: Example for semantic web
II. RELATED WORK:
[1] Using learner model the attributes are
specified. While matching that, the materials are
customized to show the presentation adaptively. For the
requisition of media diverse or page substitution, the word
supplanting can change the word granularity. Substance
can be redone for holding extra data, essential data or near
descriptions exploration is nearly identified with the forces
of QBLS framework. It is web-based wise taking in
framework that totally depends on upcoming web advances
and norms. Protus 2.0 framework actualizes suggestion
methods in personalization process. In Jia et al. (2011),
ontology is taken and model framework has created and
utilized to build for the concept of justify the machine and
formal execution situated taking nature's turf. Creators in
Fernandez-Breis et al. (2012), a framework is presented
that produced the secondary school instructors that used
ontology's for backing advancement and administration
program of education instruction. It portrayed the Gesur
programming stage that executes the form of
administration it permits the arranging execution and the
educative educational program has been controlled. SWRL
(Sicilia et al., 2010) is a dialect particularly focusing the
present deduction leads in the models of information spoke
to in OWL. Semantic web Rule Language (SWRL, 2012)
contains most of the formal prominent in Web group to
communicate learning as tenets. Particularly, SWRL is
dependent upon a fusion of Web Ontology Language
(OWL, 2012) and Rule Markup Dialect (The Rule Markup
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Initiative, 2012) and has proposed as W3c hopeful standard
to formalize the declaration of principles in the Web
connection. An open source ontology manager Portage and
system based on information (Protégé, 2012). A SWRL
editor manager furnished by the protégé OWL plug-in, it
empowers the SWRL formalization it manages with OWL
ontology in the conjunction. [2] The Semantic web gives
diverse scanning chances for the distinctive Search
Engines. For Instance, we have recognized the Hospital
Search Engine where the Patient needs to scan for dental
medicine process. The Search Engine offering penetrate
down to the deepest level of inquiry, from area of the
Hospitals to the portions of every last one of Doctors.
Conceivable perusing knowledge. As the Patient looks for
the agenda of Hospitals, he gets to pick between diverse
areas. In the wake of selecting the Hospital area, the whole
rundown of diverse Hospitals in the region is shown, he
can select and view the data identified with his investment.
By utilizing the Relational Graph between all the assets the
seeking might be made simple and proficient. "Web" has
discriminatingly changed the point of view of how the
associations extricate data from the accessible information
in today's universe of element business. In this way, the
most paramount differentiator between a fruitful and an
unsuccessful business is the way an association oversees its
information. The discriminating perspective in today's
business situation is the way information is changed over
into Information and in this way how data is changed over
into learning. It is extremely significant to concentrate
information from un-organized information which is
accessible in different organizations and created by
heterogeneous sources over an enormous association. The
greater part of the business data exists as unstructured
information – normally showing up in messages, web
journals, discourse discussions, wikis, official updates,
news, client assemblies, talking scripts on interpersonal
interaction locales, undertaking reports, business
recommendations, open overviews, research and white
paper .[3] work keeps tabs on demonstrating executor
based system for mining semantic web substance utilizing
grouping systems. Grouping will assist furnish client with
inquiry pertinent bunch of web substance, which will better
fulfill client necessity and will give optimal usage of web
surfing time [3] proposes operator based Semantic Web
Mining System (SWMS) which will furnish arrangement
and bunching of the web substance, subsequently
encouraging information based reaction to the client and
will highlight overall unnoticed examples. Given beneath
furnishes the elevated amount perspective of SWMS. It
mostly includes Interface executor, gathering operator
backed with ontology database, substance mining executor
and grouping operator. Substance mining executor works
in a joint effort with clear metadata operator and semantic
metadata executor. Nature of the operators held is as
accompanies. Interface agent (IA) wit functions as an
interface between the web crawler and the SWMS. It gains
question given by the client and passes it on to the
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accumulation executor for match of pertinent outcomes. On
appropriating outcomes from the accumulation executor, it
passes it onto web index for giving yield to the client.
Collection agent Accumulation (CLA) operator accepts
data question from the interface executor and investigates
ontology database for the importance of the catchphrases or
the connection based significance of the expression. When
it is clear in which connection client is looking for the data,
substance mining executor and the bunching operator are
conjured to get suitable effects .Content Mining agent
(CMA)This operator works in a joint effort with files
upheld by the internet searchers, further refining the data
recorded in lists to concentrate information from it. It
concentrates on the metadata held in every report, which
holds depiction of the substance regarded as illustrative
metadata and data representing meaning/context of the
substance reputed to be semantic metadata. It visits server
files occasionally to investigate new substance and passes
this data to enlightening metadata operator and semantic
metadata executor for further taking care of. Descriptive
Metadata agent (DMA). DMA is answerable for
concentrating the spellbinding data, for example, title, date,
size, sort of the index and so forth. It upholds a table
recording this data, on which content mining systems are
connected by CMA to concentrate helpful information, for
example, what number of new web pages/files have been
transferred in a specific zone in a particular year. Semantic
Metadata agent (SMA) SMA keeps tabs on recording
semantic characteristics of an archive, for example, creator
name, setting of record, association concerned (if any) or
dominion of work. This data is recorded in semantic
metadata table and is mined to get suitable
knowledge/pattern, for example, more expansion of records
in a particular setting shows more research/development
slant of clients around there. Likewise, slightest went to
territory can additionally be found. Clustering Agent
(CUA) Bunching executor deals with the tables supported
by the DMA and SMA. It makes different bunches of the
filed records such that bury bunch similitude is minimized
and intra group closeness is augmented. Grouping is unique
in relation to content classification or arrangement in the
path that there are predefined classes in which archives
must be set. Grouping does not take after any predefined
scientific classification rather bunches rise up out of the
aspects of the records on their own. Bunching executor
makes utilization of various level grouping calculation for
this reason. Separated from these operators, ontology
database is an essential segment that backings the in
general target of returning setting applicable information to
the clients. Ontology Database Ontology is characterized
also sorted out learning plan that speaks to large amount
foundation information with thoughts and relations.
Ontology based creeping disposes of straightforward
decisive word based slithering strategy as it presents
semantics/context in which a catchphrase is continuously
looked subsequently enhancing creep effectiveness. Most
existing ontology centre crawlers use ontology as
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foundation information and apply weights of thoughts in
the ontology to figure the importance score (spectator
intrigued by configuration portions of ontology database
may as well allude. The extensiveness of the ontology
database can guarantee connection based data recovery.[4]
Our technique of D-network development comprises of the
accompanying building squares archive annotation, term
extraction, and expression uniting. At first, the repair
verbatim information focuses are gathered by recovering
them from the OEM's database, which are recorded
throughout field FD. In the first stage, the terms, for
example, part, manifestation, and disappointment mode,
significant for the D-network are expounded from each one
repair verbatim by improving the record annotation
calculation. A repair verbatim comprises of a few parts,
indications, disappointment modes and activities and the
right affiliations must be made between the significant
terms dependent upon their vicinity with one another. Here,
a repair verbatim is first part in diverse sentences by
utilizing the sentence limit recognition tenets and the terms
showing up in the same sentence are co-related with one
another. At long last, Naive Bayes likelihood model is
created to disambiguate the curtailed terms by recognizing
the connection in which they are specified. From each one
expounded repair verbatim the tuples, for example, parts Pa ∈ {p1, P2, . . . , Pi}, side effects - Sb ∈ {s1, S2, . . . ,
Sj}, disappointment modes - fc ∈ {f1, f2, . . . , fk}, and (Sb
Pafc) ∈ {s1p1-f1, S2p2-f2, . . . , Sjpi-fi, Sjpj-fj} are built by
utilizing the term extraction calculation to populate a Dnetwork. At the finish of this step, some tuples are built
however every last one of them are not discriminating to
diagnose the shortcomings watched in companionship with
a particular framework. The accumulated standardized
recurrence of the tuples is figured and the tuples with their
recurrence above a particular edge are kept as the
legitimate tuples. Next, the expression blending is utilized
to stay away from vague references of the disappointment
mode phrases, where the disappointment mode expresses
that are composed by utilizing a conflicting vocabulary,
e.g., Tank Pressure Sensor−short, or FTP Sensor−internal
Short, or Fuel Tank Pressure Sensor−internal Short
Observed are combined into a solitary, reliable
disappointment mode phrase, e.g., Fuel Tank Pressure
Sensor−internal Short to administer the homogeneity. The
relevant data co-happening with the expressions, i.e., parts,
side effects, disappointment mode, and movements is
utilized to gauge the contingent probabilities and the
expressions with their likelihood score above the particular
edge are combined. At last, the recently developed
DMatrix is reviewed by topic masters (Smes) to distinguish
the disclosure of new manifestations and disappointment
modes [5] that of speaking to, as passing of time, singular
time interims e in A Box, by unequivocally expressing a
time of beginning t of every interim e through part
declarations of sort has Begins At(e, t), the states of sensor
s it held in every interim e through sort part attestations has
Fluent(e, s), priority relationships in-between sets of people
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 1 - Mar 2014
ec, ec−1 through part affirmations of sort has
Predecessor(ec, ec−1), and different lands. As the onlooker
may suspect, since the framework does not depend on an
outer principle based motor for connection surmising, the
intricacy of thinking is part of the way it moves to the
calculation it does the ontology upgrade. It essentially
executes an arrangement for making affirmations in the
Abox around then venture (and insignificant overhauls in
the Tbox). The calculation is depicted in parts, which are
essential to comprehend inward systems to the closure of
re-actuali. Propose of a convention for secure mining of
affiliation administers in on a level plane conveyed
databases. The convention, for example that, is dependent
upon the Fast Distributed Mining (FDM) calculation of
Cheung et al. It is not an secured dispersed adaptation of
the Apriori calculation. Convention UNIFI-KC safely
processes of the union of private subsets of some openly
known ground set (Ap(fk−1s )). Such an issue is
proportionate to the issue of processing the OR of private
vectors. To be sure, if the ground set is Ω = {ω1, . . . ,ωn},
then any subset B of Ω may be depicted by the trademark
binary vector b = (b1, . . . , bn) ∈ Zn 2 where bi = 1 if and
just if ωi∈ B. Let bm be the double vector that portrays the
private subset held by player Pm, 1 ≤ m ≤ M. At that point
the union of the private subsets is depicted by the OR of
those private vectors, b: =∨m m=1 bm. Such a
straightforward capacity might be assessed safely by the
non specific results inferred in. We show here a convention
for processing that capacity which is much more
straightforward to comprehend and program and a great
deal more productive than those nonexclusive results. It is
additionally much more straightforward than Protocol
UNIFIKC and utilizes less cryptographic primitives. Our
(Protocol 2) figures a more extensive extent of capacities,
in which, from the decision-making and system
perspectives, an Actionable Knowledge Discovery (AKD)
formal view has been called. AKD is a closed optimization
problem solving process from problem definition,
framework/model design to actionable pattern discovery,
and is designed to deliver operable business rules that can
be seamlessly associated or integrated with business
processes and systems.
III. PRELIMINARY
Information saved in the Web has been made and
intended for human utilization, then again, lately the
pattern has changed and the data of the Web should
additionally be prepared immediately by workstations.
Since, despite the fact that utilization is still human, data
recuperation, extraction and handling ought to be carried
out by machines to free ourselves from the shackles that
speak to securing substantial data. In this manner to make
the data machine-lucid, manual work is needed. It can
repetitive, troublesome, and drawn out. The process of
Concept extraction is expected for concentrating the best
notions in the document collection. That is, the ideas
structured by a few words, they are shaped by a statement,
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for doing this, two routines are actualized: the C-value/ ncesteem calculation (Ochoa, Almela, Ruiz-Martínez, &
Valencia-García, 2010), it permits for acquiring the multi
word terms hopefuls for speaking to the best thoughts, and
Term Frequency-Inverse Document Frequency(TF/IDF)
method(knoth, Schmidt, Smrz, &Zdráhal, 2009;
Popescul&Ungar, 2000), it has been utilized for getting the
best thoughts shaped using one expressing.
A. Linguistic patterns
Half breed system method recognizes the
Competitor terms it utilizes an arrangement of semantic
examples. It portrays the terms in morphosyntactic
structure. It Gives the different tests that made to date in a
few realms it can be said that, the logical examples rely
upon the area being used. In the stage of Multiword
concept extraction, When a multiword competitor term
rundown obtaines, the record has been separated out by
using the algorithm of C-value/nc-esteem. The framework
organizes the terms for that, the record consistent with the
words held measure in each figures and one term the
parameters qualities, to be specific, the hopeful term event
recurrence inside more applicants, the event recurrence of
the applicant term, the applicant term length and the sum
occurrence candidate term frequency in the document
collection. Ontology is manufactured from the components
long ago concentrated. Particularly, the point is the classes
location, subclasses and lands of the cosmology. In an
OWL cosmology, a property might be the property of data
type or an article property. Right now, in first stage the
framework end favors to recognize the notion subclasses
separated and in second stage it embeds the distinctive
sorts of caught relations.
B. Subclasses identification:
Taxonomic relationships are additionally removed
here and subclass of relations is located by method of the
class name. On the off chance that a class name may be a
subclass by made up of different classes names,.
C. Identification of relations:
In this, connection extraction stage releases an
outcome which is connected. To recognize the lands name,
the utilization has been processed for lemmatized verb
incorporated in the diverse sorts of relation.
IV. PROPOSED WORK
Proposed technique for better preservation
ontology is the philosophical investigation of the way of
being, getting to be, presence, or actuality, and the
fundamental classes of being and their relations. Generally
recorded as a piece of the real extension of rationality
reputed to be mysticism, ontology manages inquiries
concerning what substances exist or might be said to exist,
and how such elements could be gathered, related inside a
chain of command, and subdivided as per likenesses and
contrasts. In the broadest sense, cosmologists explore what
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 1 - Mar 2014
makes a human, depending on institutional, social, and
specialized meetings speaking to a nexus of erudite
exercises. The proposed work contains the new technique
that is, ontology learning for semantic web using lexicalsemantic methodology.
(ontology word language) first detect the class and the sub
class. then it check for the object and the data types after
this insert the detected relation in to it and finally check for
the consistency after consistency check for the analysis of
the result of complete hierarchy then answer for the queries
that is in the form of statistical and lexical form finally
analysis the result .The text is given as a query which is
used to identify the domain and too extract the concept in
order to create the candidate list with this extract all the
possible multiple word from the text. The preprocessing is
done to remove the articles. Then group all the sets of two
or more words from the extracted term to form the
candidate sets extract the taxonomic .partonomic from the
candidate list.
The proposed system has five modules they are as follows.
1) Identification of Domain and concept extraction
2) Identification of relation and extraction
3) Ontology construction process
4) Consistency checking
5) Statistical and lexical-semantic
Figure 2: System Architecture for Lexical semantic
method
A. Identification of domain and concept extraction:
Which means the single word gives the multiple meaning
here we are going to represent the multiple semantic
relations such as: Taxonomic express the part-whole
relations of classes Partonomic express the part-of relations
of classes Statistical focus on the meaning of common
words from the whole document. In standardized OWL 2.0,
the multiple semantic relations are represented especially
taxonomic and Partonomic relations In existing manual
work is need for the computer to understand the
information. But in the proposed system we introduced the
new methodology as the ontology learning. which reduce
the time consuming task for the manually construction of
the information and also provide the valuable knowledge to
the human .The main advantage of this methodology is to
provide the high quality ontology with the automatic
consistency checking mechanism and also helps to find
domain independent .the relationship can be identified with
the c-value/N/c. Then it is calculated with the term
frequency and the document frequent as the TF-IDF.
Where each term in the document term is calculated for the
multi word it is calculated with C-value/N/C and for the
single value it is calculated with the TF-IDF after
calculating the single and multiple value we have to
identify the relationship between each words this
relationship can be taxonomic, Partonomic and semantic all
these three form the relationship between them. The OWL
and partonomic relationship. Semantic parts and
semantic class enrolment for the verbs are utilized within
request to concentrate and distinguish these relationships.
In this, the semantic is the connection between a subject
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This modules explain that which is used to find
the relationship between the identification and the
extraction this can be done with the help of the ontology
constriction in this process the consistency is checked and
the statistical and lexical semantic is also done with the
help of ontology. This technique is planned to concentrate
the best thoughts in the document collection, Subsequently
the ideas are structured by some words, that are structured
by one saying, for doing this, two techniques are executed:
the C-value/nc-esteem calculation, which permits to
acquire the multiword terms competitors to speak to the
best ideas, and TF-IDF term, which has been utilized to
acquire the best ideas structured by one saying. This
procedure could be demonstrated into some stages as it is
depicted.
B. Identification of relationship and extraction:
This method is used to obtain the semantic
relations which has the two types of relation in order to
identify the taxonomic and the Partonomic. In the past
stage the ideas have been distinguished and at that point,
the semantic relations of these ideas must be gotten. In
characteristic dialect, relations between thoughts are
normally co-partnered with verbs . Various frameworks for
taking seeing someone have been proposed dependent upon
the extraction also recognizable proof of verb. Three sorts
of relations are recognized they are semantic, taxonomic
and also a predicate. It characterizes the part of a verbal
contention in the depicted next. Initially, the principle verb
of the present sentence is distinguished. At that point,
where the quest is for the kind of semantic connection
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 1 - Mar 2014
.connected with that verb in dictionary. Keeping in mind
the end goal to discover ontological semantic relations
around the substances. The sample demonstrates how the
semantic part relates Then again, for locating taxonomic
relations, a set of phonetic examples dependent upon the
Hearst ones have At long last, the partonomic is described
as ''some piece of'' or “has-an” or ''have-a''. A few examples
has been intended for acquiring the partonomic relationship
which has been acquired in the wake of investigating an
extensive number of partonomic relations in the document
collection.
C. Ontology construction process:
Detect the class, sub-class and properties of the
ontology. Concepts extracted subclasses are identified.
Different types of relations are detected and inserted. OWL
2 provides high expressive power for properties. At this
stage the ontology is fabricated from the components a
while ago removed. Particularly, the point of the classes are
discovered, subclasses and lands for the metaphysics.
Property of the OWL ontology, could be a property of the
data type or the property of article.
D. Consistency checking:
Description Logic derivation administrations sets are
allowed for the execution by OWL ontology. It could be
underpinned by DL reasoners. It guarantees that a
cosmology should not hold any opposing certainties.
Concept satisfaction, checks whether this is
conceivable for a class to have any occurrences. In the
event that a class is un-satisfiable, then characterizing an
occurrence of the class will cause the entire philosophy to
be conflicting
E. Statistical and lexical-semantic:
To develop high-quality ontology. We need Statistical
focus on the meanings and the relation of common words
Lexical (Polysemy) single word having multiple meaning.
Taxonomic relationships evaluation: this method has been
expressed some time recently these sorts of relationships
are the more upheld by cosmology taking in systems and
speak to the most paramount relationship in ontology’s. In
this document collection the specialists has recognized the
taxonomic relations as 769. The outcomes of accuracy got
better than implying that practically all the relationships as
taxonomic examples extricated are right. Partonomic
relations evaluation: which are distinguished by utilization
of examples it had a large portion of them incorporate verb
''has-an''. An equivalent word of the examples verbs are
characterized as acquired from Ontology. Specialist
distinguishes an aggregate sum of partonomic relationships
as 488 and just about every last one of them had
unequivocal content. The extraction technique got a review
of 98.35% that shows all the partonomic relationships are
practically has been recognized by the characterized
examples. Then again, the technique concentrated wrong
relationships from the content acquiring an exactness of
valuation for other relationships in semantic: As specified
previously, in ontology the semantic parts are characterized
and acquired these relationships.
Figure 3: Lexical-Semantic Comparison
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 1 - Mar 2014
V. CONCLUSION
This paper propose “Ontology Learning for Semantic
Web using Lexical Semantic Method” is to retrieve the
relevant data . It retrieves the data or word which having
multiple meanings that is more relevant when compare to
existing methodology. In this, the taxonomic and partonomic
relationships are analyzed. So i conclude Ontology Learning
for Semantic Web using Lexical Semantic Method is best
method for retrieving data in semantic web.
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