Eighth IEEE International Conference on Advanced Learning Technologies
Towards a Knowledge Portal for E-Learning based on Semantic Web*
Yanyan Li1, Mingkai Dong2
1
Knowledge Science & Engineering Institute, School of Education Technology,
Beijing Normal University, 100875, Beijing, China
2
Knowledge Management, Siemens Corporate Technology China
Liyy1114@gmail.com, mingkai.dong@siemens.com
Web encompasses efforts to build a new WWW
architecture that supports content with formal
semantics, enabling better searching and navigating
through the cyberspace. Thus, the Semantic Web can
be used as a technology for realizing sophisticated elearning scenarios [5].
Based on Semantic Web technologies, this paper
proposes a knowledge portal to effectively support elearning, enabling flexible knowledge acquisition,
knowledge refinement and maintenance, as well as
knowledge retrieval and accessing.
Abstract
E-Learning aims to deliver individualized,
comprehensive, dynamic learning content in real time,
aiding the development of communities of knowledge.
By incorporating Semantic Web technologies, this
paper proposes a knowledge portal with underlying
distributed learning repositories to effectively support
the knowledge process, empowering flexible knowledge
acquiring and collecting, knowledge refinement and
maintenance, as well as thematic knowledge retrieval.
In this way, the knowledge portal serves as an entry
point for learners to access and utilize the semantic
related knowledge to meet their diverse learning needs.
2. Architecture
The architecture of the knowledge portal for elearning is illustrated in figure 1. As the figure shows,
the instructors and learners can access and maintain the
learning resources through the user interface.
Reference ontology indicates the common terminology
with respect to the specific domains, which lays the
foundation for semantically interconnecting the
distributed learning repositories. Other core modules
includes: knowledge acquiring, knowledge refinement,
and knowledge retrieval.
1. Introduction
With the popularity of the e-Learning, Learning
Content Management System (LCMS) emerged
expecting to provide standard-based content
repositories that allow learners to capture, store, deliver,
and manage learning resources. But the substantial
impediment to the destination is the fact that the
resources are disordered, isolated, and heterogeneous,
and there is no common overarching context for the
available resources. Additionally, finding the precise
information is very difficult because of the lack of
semantic description of learning resources. Thus,
navigation through a large set of independent resources
often leads to users’ being lost. The literature in elearning research emphasizes the value adding learning
from the knowledge management perspective [2] [3].
Therefore, how to acquire and provide the suitable
knowledge to meet learners’ diverse learning needs is a
key issue.
Knowledge portals are portals that focus
specifically on
the
production,
acquisition,
transmission or the management of knowledge, which
emphasizes the consideration of the relevant context of
information providing and accessing [4]. The Semantic
*
Figure 1. Architecture of e-learning knowledge portal.
This research work is supported by National Natural Science Foundation of China (NSFC: 60705023)
978-0-7695-3167-0/08 $25.00 © 2008 IEEE
DOI 10.1109/ICALT.2008.83
910
reasoning rule: R(K1, K2), R(K2, K3), …, R(Kn-1, Kn), ⇒
R(K1, Kn), where R ∈ Ω, Ω is a set of primitive
semantic relationships in a given domain [7].
3. Semantic Resource Organization Model
The model is composed of knowledge algebra and
knowledge base. The knowledge algebra defines a set
of basic elements (vocabularies), which provides a
foundation for knowledge base. The knowledge base
comprises two parts: knowledge schema and fact base.
The knowledge schema describes the agreed abstract
domain knowledge (e.g. axioms, rules), while the fact
base describes the facts of instances (e.g. types,
properties).
Actually, the knowledge base is modeled as a
directed labeled graph, wherein the nodes and arcs
respectively represent the entities (e.g. concepts,
instances) and semantic relationships between entities,
encoded as RDF entities and properties. An example
works best to explain our proposed idea. Suppose we
are trying to establish one semantic-based model in
“research” domain, which roughly explains how we
can organize resources for research purpose. Figure 2
is an excerpt from a simplified resource model. As the
figure illustrates, the knowledge schema is composed
of abstract concepts with various semantic
relationships while each concept has its specific
properties. The instances can be dynamically linked to
the concepts in the knowledge schema.
4. Knowledge Process for E-Learning
Adoption
4.1 Knowledge Acquiring Mechanism
Two modes are provided to acquire the knowledge
in a certain context. Through the authoring interface,
instructors and experts are able to define the generic
instructional strategies and metadata (e.g. content,
structure, and context) of learning materials based on
templates. Through this annotation process, users can
create metadata that conform to the domain ontology.
On the other hand, interaction in an e-learning
environment implies a lot of knowledge creation and
exchange. Thus, data mining technologies are adopted
for recognizing and tracking the topics in the
interaction process. Discussion forums allow learners
to ask and answer questions on a variety of topics,
which is especially useful for discussion involving
multiple viewpoints or multiple problem solutions, or
for straightforward questions requiring a quick
response. RDF and XML are used to represent and
save the acquired knowledge and it’s linking resources.
4.2 Knowledge Refinement
Knowledge refinement comprises relevance
checking and redundancy elimination. The relevance
checking module determines whether the knowledge
items (e.g. questions, metadata) link to the appropriate
topics in domain ontology. We adopt the SVM [6] to
classify the texts into appropriate classes.
Simultaneously, the Knowledge Engineer checks the
relevance of knowledge so as to guarantee the correct
classification of knowledge items.
There are two types of knowledge redundancy:
explicit repetition, and implicit redundancy, i.e.,
knowledge can be derived from other existing
knowledge. As some implicit redundant knowledge
may enhance the efficiency of a system, we currently
only deal with the explicit case. As for the questions in
discussion forums, we design many question templates
and group into clusters according to their meaning. A
thesaurus consisting of noun-abbreviation, composite
word, and verb-synonym, etc. is also used. For
example, define, describe and introduce concern the
explanation or definition of concepts. We currently
consider two factors (i.e. keyword focus and sentence
structure) in determining whether there have redundant
questions or not. If redundant questions exist then
mark the appearance times whilst deleting the
duplicates. The Knowledge Engineer will assist
checking the knowledge redundancy and consistency.
Figure 2. A schematic resource organization model.
Especially, the instances belonging to the same
concept may be correlated with typical semantic
relationships, such as Sequential, SubtypeOf, CauseOf,
SimilarTo, Reference, etc. More types of semantic
relationships can be defined according to the
application domains. The reasoning rules can be used
for chaining the semantic relationships and obtaining
the reasoning result from the chaining. A simple case
of the reasoning is that all the semantic relationships
have the same type, which is called single-type
reasoning. According to the transitive characteristic of
the semantic relationships, we have the following
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the paths. By utilizing domain knowledge and
context, the algorithm Get_AssociationPath is to
discover the possible semantic association paths
between two entities.
As a result, the semantic related knowledge items
are structured and return to learners, providing an
overall view on a topic along with the navigation guide
for learners to explore more related knowledge.
4.3 Thematic Knowledge Retrieval
Thematic knowledge retrieval enables learner to
express his information needs in terms of keywords,
but at the same time uses the semantic information
regarding the domain of the application to obtain
results that are not possible in traditional searches. It
mainly comprises the following two steps.
Get_AssociationPath(entity a, entity b, Integer h) {
//h denotes the pre-defined constraint of maximum searching depth, A
denotes the set of semantic association paths, L denotes the search depth.
Qa = {a}; Qb ={b}; // Qa and Qb denote the searching sequence of a
and b, respectively
Va={a}; Vb={b}; // Va and Vb denote the set of the visited entities,
respectively
A=∅；L = 1;
While (L<h) {
While (Qa ≠ ∅){ // Pop all the elements in Qa to the temp sequence
Ta
tmp = pop(Qa);
Push(Ta, tmp);
}
While (Ta ≠ ∅){ //repeat the following steps till Ta is null
k = pop(Ta);
if (k Va) break; //skip to the next node if k has been visited
For i=1 to m { //m denotes the number of properties of k, pi
denotes the ith property of k
Vb) then { // vi denotes the value of the
If (pi(k, vi)∩vi
property pi
If (vi = b) then{
There is an association path originated from a to b and
add it into A.
}Else {
There is a joint association path between a and b and add
it into A.
}
}
If (pi(k, vi)∩(pi is ObjectProperty)) Qa= Qa + vi;
Va = Va + v i ;
}
}
The same operation on Qb;
L = L + 1;
} // end of “While (L<h)”
Return A;
} //end of algorithm
5. Conclusion
The kernel idea of this paper is to semantic
modeling of heterogeneous learning resources in a
coherent and meaningful scheme. With the effective
support of knowledge acquiring, knowledge refinement
as well as knowledge retrieval mechanism, the
proposed knowledge portal aims to provide an
integrated, scalable and easy-to-use interface for
diverse learners to manage and exploit the dynamically
growing learning resources on the Web.
The proposed knowledge portal is still in its early
stage and more work needs to be done in the future,
such as (1) Use semantic link network (SLN) to
organize the semantic-associated learning resources
supported with semantic mapping; (2) Investigate the
topic recognition and tracking by considering semantic
context; (3) Implement the knowledge portal prototype
and apply in practical settings.
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z
6. References
[1]
[2]
[3]
Query annotation. According to the learner profile,
the inputted query is firstly annotated to denote
the intended meaning of the learners by dealing
with the linguistics ambiguity [1].
Select target knowledge items. Taken the
matching entities as the anchor ones, this step is
to find semantic relevant entities. The simple
approach for selecting the target entities for the
one matching entity, purely based on the structure
of the graph, is to collect the first N triples
originated from the anchor entity, where N is the
pre-defined traversal constraints. As for the case
of two matching entities corresponding to the
query, it is the key problem to find all the
semantic association paths between the two
entities so as to select the relevant instances on
[4]
[5]
[6]
[7]
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