1. 1
Subontology Assisted Web-Based e-Learning For
Resource Management
S.Jahnavi
jahnavi.tec@gmail.com.
Mrs.P.Selvi Rajendran. M.E(Ph.d)
Associate Professor and Head,
Department Of Information Technology,
Tagore Engineering College.
p_selvirajendran@yahoo.co.uk.
Department of Information Technology,
Tagore Engineering College.
S.M.Kalaivani
kalaivani.sm89@gmail.com.
Department of Information Technology,
Tagore Engineering College.
Aditi Jayaraj
aditijayaraj18@gmail.com.
Department of Information Technology,
Tagore Engineering College.
Abstract Web and Information Technology development has
Revolutionized the concept of E-Learning Systems and the
resources available based on it are very vast. Ontology is the key
technology for enabling Semantic driven resource management. In
this project, we suggest a semantic mapping technique to combine
heterogeneous Databases by using the semantics of ontology and
mediated ontology. The concept known as locality of resource
reuse is proposed to represent context specific portions from the
whole ontology as sub ontologies. A sub ontology-based approach
is obtained by using SubO Evolution Mechanism is used to achieve
adaptive and efficient resource management
.
Keywords--E learning, Semantic mapping, SubOntology,
Resource Management, Resource Reuse
1. INTRODUCTION
Recent advances in technologies and
increasing decentralization of organizational structures
resulted in massive amount of e-learning resources in
many disciplines. There are many related resources
grouped into learning materials on various topics,
which are accessible on the web. This vast amount of
e-learning resources is distributed among many
specialized databases and websites. This leads to
heterogeneous representation problem.
An e-learning system [1] need to compose
these relevant resources together in order to achieve
on-demand and collaborative e-learning. And another
problem in the existing system is the semantic gap
between user requirement and e-learning resources. To
overcome these problems ontology which is the
backbone of Semantic Web [2] is used. Subontologies
(SubOs) are evolved using past experience and
dynamic SubO mechanism is used for efficient
resource management.
With respect to other works, this paper defines
SubO in a elaborate manner and makes use of a
Genetic algorithm (GA) to achieve the evolution. The
need to use Semantic Web lies in the fact that the
content is effectively reused based on the content
present on the web. The key points of the paper are
based on the following:
1. A semantic mapping Mechanism is used to
mediate databases present in the e-learning
sites.
2. SubO operations are defined which takes
locality of reference into account.
3. Resource reuse is effectively implemented for
content management and adaptive resource
management.
2. RELATED WORK
Related work on ontology [3], [4] has been
going on for a while now and the advances have lead
to different areas of interest and development. Robert
Farrell et al. [5] concentrated on Dynamic Assembly
of Learning Objects which gives solution to the
problem of how to select sequence and link web
resources into coherent, focused organization for
instruction that addresses the user’s immediate and
focused learning need. Dynamic assembly uses both
learning object metadata XML and cross object
relationship expressed in RDF (Resource Description
Framework) to assemble modular learning objects.
The setback of the system lies with the fact that it
relies on static ontology for support and the assembly
of learning objects. Ljilljiana Stojanavic et al . [6]
Focuses on E Learning Based on Semantic Web, in
their work eLearning scenario is implemented using
Semantic web technology. It is based only on
ontology-based descriptions of content, context and
structure of learning materials. The drawback of this
method lies in the definition of the content, context
and structure of all the e-learning materials. Dragan et
al. [7] proposed an Ontology Mapping to improve
learning resource search. This system uses multiple

2. 2
ontology and provides solution to interoperate e-
learning systems based on different ontologies.
The disadvantage lies in mapping relation between
query arguments. And moreover the target
ontology is not defined. S. Yang et al. [8] devised
an ontology enabled annotation and knowledge
management for collaborative e-learning in virtual
learning community. In which ontology enabled
with annotations and knowledge management is
developed to provide semantic web services in
three perspectives, personalized annotations, real-
time discussion and semantic content retrieval.
The problem is that Universal access through
various devices is not possible. N.F Noy et al. [9]
Suggested specifying Ontology views by traversal.
It basically limits the views traversed by the user.
Traversal view is used where a user specifies the
concept of interest, and the relationships to
traverse to find other concepts to include in the
view. Upon the view development the depth of the
traversal is evaluated. Here a strategy is proposed
to maintain the view through ontology evolution
and tools are defined to extract Traversal views.
Traversal views that extract classes and instances
may contain a complex definition. In our work we
implement a method to integrate large scale
domain ontology which is a mediator for
heterogeneous databases in the relational schemata
format.
3. PROPOSED METHOD
3.1 SEMANTIC WEB
E-learning resource can be a paper,
database or a website. The information in these
resources should be potentially related to each
other from the aspect of e-learning, and it is
necessary for users to reuse them in global scope.
We can integrate a massive amount of e-learning
resources based on the semantics of large scale
domain ontology in a distributed environment. In
our approach, large scale domain ontology acts as
a mediator for integrating distributed and
heterogeneous e-learning resources.
Relational schemata of e-learning databases [10]
that contain learning materials are mapped to a
global ontology according to their intrinsic
relationships. Tables in different databases are
mapped to ontology classes and table fields are
mapped to ontology properties. When a table is
mapped to a class, the records in the table are
treated as the instance of the class. Implicit
relationships between databases are interpreted as
semantic relationships in the ontology. Semantic
mapping information is stored in a semantic
registry and can be reused by applications.
3.2 SUB ONTOLOGY
Ontology play the role of mediator in our
e-learning resource management approach. Large
scale mediated ontology contains relatively
complete knowledge about the domain of e-
learning. But the activities of Web based e-
learning need only particular collection of
resources. An e-learning system needs to extract
from the ontology specific portions and keeps
evolving them to satisfy the requirements of users.
Different portions of the ontology with related e-
learning resources are reused by users or
applications. The context specific portions that are
derived from the whole ontology based on the
locality of reference are SubO. These SubO are
reused for efficient search
Definition 1. Given an ontology O with n classes,
a concept set C= { ci|ci € O, i=1,2,..,k}, kKn, a
knowledge set K is a subset of O
3.3 SubO BASED RESOURCE REUSE
The users of e-learning systems need to
reuse various e-learning resources to achieve a
goal or to solve a problem. As e-learning resources
are mapped to a mediated ontology, a user request
for e-learning resources can be transformed to a
query on the ontology. As we retrieve a portion of
the ontology, we also retrieve the e-learning
resources mapped to that portion of the ontology.
An e-learning system needs to coordinate and
reuse e-learning resources to satisfy resource
reuse. A SubO [11] groups a collection of e-
learning resources together with semantics. To
simplify the process of reuse we match the classes
in resource request with the ones in SubOs. The
matching degree between a resource request and a
SubO can be one of the following:
Assume that A and B are two ontology classes:
Exact: A is equal to B, or A and Bare just the
same class.
Plug-in: A is a super class of B, or A subsumes B.
Subsume: A is a sub class of B, or B subsumes A.
Fig.1. Sample ontology and a SubO
Representation

3. 3
4.ARCHITECTURE
The depicted system architecture
represents a high level model of the
Heterogeneous Databases under a Sub Ontology
technique.
Browser: The input is given by the learner for a
certain domain in which the learner possesses an
account.
Ontology: The basic search procedure relates to
ontology process which is present in the existing
system. Here the system Admin is responsible for
upload of relevant text in any file format.
Sub Ontology: The given input is actually
processed through a Sub O Mechanism, which
extracts only relevant data from the specific
domain and eliminates unwanted results and pages
of information.
Semantic Registry: The heterogeneous databases
are mediated under a single Sub O request by the
use of the Semantic mapping technique which is
carried out by the semantic registry. It mediates
data related to various databases.
Relational Database: The relational database [12]
is in the format of a Base Class, its Properties and
Instances, which yields the search query output
based on the domain.
Fig.2. Architecture of proposed system
5. ALGORITHM
5.1. Extract (C, n, O)
Extract operation is to obtain a SubO from
the source ontology.
Fig.3. Pseudo Code for Extract
Input: ontology O, concept set C = {c1, c2, …
ck}and traversal depth n.
Output: SubO B.
Steps:
for i := 1 to k
perform breadth first traversal from ci in O
terminate traversal up to a depth n;
add the result set Ri to the set K
end loop
get the corresponding SubO B= <C, K, O>
return B
5.2. Store (B, R)
Store operation is to save a SubO in the
SubO repository.
Fig.4. Pseudo Code for Store
Input: SubO B = <C, K, O>, SubO repository R.
Output: Boolean return value (ret).
Steps:
n := size Of ( R )
for i := 1 to n
get ith
object in R
assign the object to Bi

4. 4
if B equals to Bi then
return false
end if
end loop
store C, K, O in the three fields respectively
return true
5.3. Compare (B1, B2,O)
Compare operation is used to estimate two
SubOs and to give a similarity degree.
Fig.5. Pseudo Code for Compare
Inpu ontology O, two SubOs B1= <C1, K1, O>
and B2= <C2, K2, O> .
Outpu similarity degree sim.
Steps:
compute the Levenshtein distance between C1
and C2
assign the result to LD (C1,C2)
compute the Levenshtein distance between K1
and K2
assign the result to LD (K1,K2)
RLD (B1,B2) := LD(C1,C2)/|C1| OR LD
(K1,K2)/|K1|
sim := 1/RLD(B1,B2)
return sim
5.4. Retrieve (D, R)
Retrieve operation is to bring back the
SubO from the SubO repository.
Fig.6. Pseudo Code for Retrieve
Input context description D, SubO repository R.
Output SubO B.
Steps:
n := size Of ( R )
for i := 1 to n
get ith
object in R
assign the object to Bi
sim := RLD (D, B, C)
if sim > Tsim then semantic similarity
return Bi
end if
end loop
return null
5.5. Merge (B1, B2, O)
Merge operation is to combine two SubOs
into a new one.
Fig.7. Pseudo Code for Merge
Input: ontology O, two SubOs B1= <C1, K1, O>
and B2= <C2, K2, O>.
Output: SubO
Steps:
merge C1 and C2 into a new set C
merge K1 and K2 into a new set K
check the completeness of K
extract additional properties from O
add the additional contents to K
get the corresponding SubO B = <C, K, O>
return B
5.6. Encode (B, O)
In this paper, GA is used to solve the
problem of SubO evolution by supporting dynamic
resource management and reuse. To solve this
problem GA need to be performed in two steps:
1. Problem encoding.
2. Determining the evaluation/fitness
function based on the ontology semantics.
A chromosome consists of list of genes
and a set of chromosomes groups together to form
population. Problem encoding in GA is mapping
the problem space to the parameter space. In this
problem, it refers to how to represent SubO as
chromosomes. We take source ontology as
problem space and map it to an encoding space of
0’s and 1’s. The number of classes in source
ontology is equal to the length of chromosome.
Fig.8. Pseudo Code for Encode
Input: SubO B = <C, K, O>, ontology O
Output: chromosome S
Steps:
Convert O to class list L
n := sizeOf( L )
Create a chromosome S with alleles and each
allele is set to O
for i:= 1 to n
get ith
class in L
assign the class to ci
if ci appears in K
set ith
allele in S to 1
end if
end loop
return S
5.7. Decode (S, O)
Decode operation is to convert the
chromosome into a SubO.
Fig.9. Pseudo Code for Decode
Input: chromosome S, ontology O
Output: SubO B
Steps:
convert O to a class list L

5. 5
n := sizeOf (S)
for i := 1 to n
get allele in S
assign the allele to ai
if ai = 1 then
assign the allele to ai
end if
end loop
retrieve properties for the classes in K
cluster K into subgraphs
for each subgraph
pick out the root node
add the class of the root node to a set C
end loop
compose a triple B = <C, K, O>
return B
5.8. SubO Evolve (K, R, O)
The fitness function is the measure of
performance of performance of SubOs. We can
evaluate the SubO by computing the clustering
degree of its knowledge sets since the SubO
contains several components.
Definition 3: Given a SubO B = <C, K, O>, the
extent to which the knowledge in B forms clusters
can be measured as knowledge clustering
coefficient (KCC), given as
KCC (B) = ?(ni / n)2
, i =1,2, . . . , k
Let P be a population with n
chromosomes, then the fitness value of ith
chromosome in P is
Fi =KCC (Si)/? KCC (Sj),j=1, 2, . . . , n
Fig.10. Pseudo Code for SubO Evolve
Input: set of resources request K, SubO repository
R, ontology O
Steps:
n := size Of ( K )
for i := 1 to n
b := retrieve(k[i], R)
repository for a request
if b = null
b := extract(K[i], d.O)
depth d from an ontology for a request
else
remove b from R
end if
P[i] := encode (b, O)
chromosome of the population P
end loop
m := size Of ( P )
repeat
GAevolve (P
P := 0
for j := 0 to m-2
for k := j+1 to m-1
sim := compare (p[j], p[k], O)
chromosomes in the population
if sim >= Tsim
for semantic similarity
p[k] := merge( P[j], P[k], O)
chromosomes with high similarity
break
end If
end loop
if k >= m-1
Pt[p] := P[j]
p := p+1
end if
end loop
Pt[p] :=P[m-1]
p := p+1
for j :=0 to p-1
P[j] :=Pt[j]
end loop
for j := p to m-1
P[j] := null
end loop
f := evaluate(P)
population
until f >= Tf
for j := 0 m-1
b := decode (P[j],O)
SubO
Store(b,R)
end loop
6. EXPERIMENT AND EVALUATION
6.1 EXPERIMENT DESIGN
The Engineering College repository is a
collection of various institutes which offer courses
to students with each institution offering various
facilities and courses of study. A huge number of
technical databases are present and they have been
constructed over the past years. We have selected
our institution as one of the domain and named it
as TEC DESIGN as the construction of the
database in the e-learning system involves one
institution and the content present in it. We have
mapped it using the ontology tool and each class is
related to at least one table present. The
experiment setting is as follows:
1. Size of the population=10 i.e., only 10
chromosomes being allowed to enter the
next generation.

6. 6
2. Size of the resource request set=50, each
request being represented as a list of
classes.
3. The depth of extracting SubO is 2.
4. Crossover and mutation rates for GA are
0.1 and 0.001.
5. Set S to 0.6, T to 0.3 and U to 0.1 for
KMC.
The experiment proceeds as follows:
1. Initially, there is no SubO in the SubO
repository of the e-learning system.
Randomly extract some SubOs from the
ontology and store them in the repository.
2. Generate a collection of resource requests
based on the ontology. Select resource
requests randomly and submit them to the
system.
3. The system is forced to satisfy the
resource requests by reusing the SubOs.
When a request does not relate to any
SubO in the repository, it means that the
request cannot be satisfied and hence the
KMC value is zero for the system. The
result of each request is recorded.
Encode the SubOs in the repository as a
population of chromosomes
5. The system evolves the population based
on GA. Get a new set of SubOs.
6. The system satisfies the resource requests
generated in step 2 again by reusing the
evolved SubOs. The result of each request
is recorded.
7. Compare the results got in step 3 and 6.
8. Analyze the knowledge structure of the
SubOs of the system.
Thus the SubO output which is analyzed
and retrieved is more of context related and
extracts only specific information.
7. CONCLUSION
In this paper, we propose SubO based
dynamically used resource management technique
to improve the query output efficiency. The
dynamically used Sub O Evolutionary Algorithm
converts the input into a Query and relates it to all
the heterogeneous databases present in the
Relational Form. The relational Database schema
has a base class, its properties and the related
instances and uses Semantic Mapping Mechanism
to relate the data. This is mainly proposed for E-
Learning systems were the content to be managed
is huge and the relevance is low. Hence future
research is on in the proposed system which
ensures
1) More accuracy
2) Time efficient
3) Result relevance is high
4) Content is efficiently managed
5) Resource reuse scheme is adopted
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