=Paper=
{{Paper
|id=Vol-1684/paper4
|storemode=property
|title=Knowledge Representation in Intelligent Collaborative Educational Systems
|pdfUrl=https://ceur-ws.org/Vol-1684/paper4.pdf
|volume=Vol-1684
|authors=Sabina Katalnikova,Leonids Novickis,Natalya Prokofyeva
|dblpUrl=https://dblp.org/rec/conf/bir/KatalnikovaNP16
}}
==Knowledge Representation in Intelligent Collaborative Educational Systems==
https://ceur-ws.org/Vol-1684/paper4.pdf
Knowledge Representation in Intelligent Collaborative
Educational Systems
Sabina Katalnikova, Leonids Novickis, Natalya Prokofyeva
Riga Technical University, Riga, Latvia
{sabina.katalnikova, leonids.novickis, natalija.prokofjeva}@rtu.lv
Abstract. In this paper, the concept of collaborative intelligent educational system is
presented. Different knowledge representation models are compared in the context
of their use in collaborative intelligent educational systems. Advantages of semantic
networks for knowledge representation in such systems are described. M ain
advantages of extended semantic networks are shown and a set of basic operations
regarding them is drawn up.
Keywords: intelligent collaborative educational system, knowledge worker,
extended semantic network.
1 Introduction
In today's world, a shift fro m technology based on energy investing to technologies based
on knowledge and informat ion has taken place. Knowledge p lays a special role in post-
industrial evolution. Leading experts in the field of pract ical imp lementation of the basic
principles of sustainable development strategy – Peter Drucker [1], Alvin Toffler [2],
James Brian Qu inn [3] – independently proclaimed hu man entry into a new economy
society – knowledge society, in which knowledge is the basic economic resource. Thus,
regeneration of this resource has great importance, which is impossible without
developing a conception of representation, acquisition, analysis and use of knowledge.
On the other hand, contemporary society faces new challenges – how to organize
educational process in such a way that graduates become so called knowledge workers in
the full sense of this term [4]?
The purpose of this article is to choose a possible model for knowledge representation
in intelligent collaborative educational system as a basis for preparation of knowledge
workers in today's society.
2 Knowledge Workers and Intelligent Collaborative Educational
Systems
The leading social groups of the knowledge society will be 'knowledge workers' –
knowledge executives who know how to allocate knowledge to productive use [5].
Knowledge wo rk includes the work of all participants of the production process to achieve
optimal results through combination of each employee’s individual skills. A knowledge
worker must constantly learn innovative knowledge to be co mpetitive, and on the other
hand – he or she cannot do without teaching. Thus, emp loyee training function is one of
priorities of staff management functions in modern organizat ion s. But in global virtual
organizations employees are scattered around the world.
Changes that occur each time and co mp lexity of electronic technology society that
uses a new type of electronic co mmunicat ion devices have resulted in continuing growth
in the amount, diversity and service activities carried out in all fields [6]. Thus, in today's
society, the concept of collaborative systems has emerged.
A collaborative system is a system in which many users or agents are engaged in a
shared activity, often in distanced locations. In the large family of d istributed applications,
collaborative systems are distinguished by the fact that the agents work together to
achieve a co mmon goal and have a great need to interact with each other in the sense that
they share information, change requests, etc. [7]
An educational system is a set of interrelated educational and innovative processes and
the management of these processes. There exist general laws of develop ment of
educational systems. These are: focus on the ultimate objective, development of measures
for precise system functioning, compliance of the sub-objectives with the ultimate
objective, availability of resources, consistency, and safety.
Collaborative systems are applied in the educational field and aimed at evaluating and
enhancing the performance of educational p rocess [8]. Collaborative learning helps
knowledge workers to carry on deeper conversations, create mult iple perspectives and
develop reliable argu ments. This is the main reason why collaborative groups facilitate
greater cognitive develop ment than the one that the same individuals can ach ieve while
studying alone [9].
An intelligent collaborative educational system applies methods of artificial
intelligence to provide better support for the users of educational systems and is based on
three elements: interconnection (a resource sharing technology education),
instrumentation (accumu lation of necessary data) and intelligence (making decisions that
enhance the learning process) [10]. The architecture of an intelligent collaborative
educational system can be described as is shown on Fig. 1. User Module effects interface
between the system and the user. Management Module collects informat ion fro m other
modules, analyses and processes it, supplies other modules with info rmation obtained
fro m analysis. Do main knowledge module manages a number of educational objects and
provides users with appropriate objects. Collaborative Educational Module procures
strategies in accordance with the co mmon goal o f education. Control Module affords user
tasks and tests and verifies their execution according to its model.
Interface
Collaborative
Educational User Module
Module Management Knowledge
Module
Base
Domain
Knowledge Control Module
Module
Fig.1. Structure of intelligent collaborative educational system
3 Related Works
Many research articles in the world and in Latvia are dedicated to the problem o f
intelligent collaborative educational system development. Though the idea of
“intelligence” has built a long tradition in learning systems, it has only relatively recently
entered the domain of collaborative learning.
For example, in [11] it is emphasized that co mputer-supported collaborative learning
is focused on how collaborative learning supported by technology can enhance peer
interaction and work in groups, and how collaboration and technology facilitate sharing
and distribution of knowledge and expertise among community members.
In [12] it is defined that an intelligent educational system aims to provide learner -
tailored support during the problem-solving process, as a human tutor would do. The
comparative analysis of a large amount of publications resulted in a classification scheme,
which is proposed as a comp rehensive tool for analy zing and interconnecting the majo r
design principles applied in the area of intelligent systems for collaborative learning
support. Intelligent systems for collaborative learning support can be classified and their
design and operation can be analyzed in the following dimensions:
• Pedagogical objective: the general pedagogical objective of the system.
• Target of intervention: the focus of the intelligent support.
• Modelling: modelling techniques implemented in the system.
• Technology: the kind of technology used to implement the intelligent operation of the
system.
• Design space: how the intelligence-based intervention is presented to the partners.
Similar problems are described in many articles : development of intelligent tutoring
systems based on knowledge workers ’ personal knowledge of management systems [4,
13], use of concept maps in adaptive and intelligent knowledge assessment [14-16],
mu ltiagent techniques, development of agent based intelligent tutoring systems [17-18],
possibilit ies of extending the agent oriented software engineering methodology to make it
usable for the development of other agent oriented systems [19]; develop ment of web-
based collaborative e-learn ing systems [20]. Paper [21] presents a method for agents’
knowledge representation by using semantic network; paper [22] surveys graph based
knowledge representation and reasoning, observations are presented highlighting
suitability of the surveyed graph models for contemporary scenarios; in [23, 24] various
tools and languages for knowledge representation using ontology are described, examples
of tasks are listed; paper [25] defines the structure and the main applications of ontology.
However, it should be noted that the problem of develop ment of direct ly collaborative
educational systems for knowledge workers considering a set of their attributes has not
been paid the attention it deserves in the scientific community.
4 Models of Knowledge Representation
Solving the p roblem of knowledge representation in general involves ensuring adequate
display of knowledge stored in a variety of knowledge sources as formalized
representations, automated processing of which will allow to effectively solve problem
tasks from the do main under research to obtain results that are not qualitatively inferio r to
results obtained by highly qualified specialists of this domain in dealing with such
problems [26].
At present, the most common models of knowledge representation in intelligent
systems are the following:
• logical models (knowledge is represented as a set of correctly formed formulas o f any
formal system);
• production models (central element of th is model is a set of products and rules of
inference, instead of logical inference characteristic of logical models the conclusion
based on knowledge is used);
• frame-based models (foundation of this knowledge model is the concept of frame – a
data structure that represents some conceptual object or standard situation );
• network models in which the do main is considered as a set of objects and their
relationships. Semantic networks are the most common network model of knowledge
representation.
In the applied systems of artificial intelligence in fields like medicine, b ioinformatics,
semantic web etc., ontologies are widely used for knowledge representation. In working
with ontologies, special languages of ontology representation (RDFS, OW L etc., [ 23-25,
27, 28]) are used to supplement traditional languages of knowledge representation.
Based on the set of general requirements traditionally imposed on models, it is
possible to formulate some requirements for models of knowledge representation in
intelligent collaborative educational systems [12, 26, 29, 30]:
• visuality of construction and display of logical connections and semantic
relationships of investigated domain, taking into account the main co mponents of an
intelligent educational system, i. e. interconnection, instrumentation and intelligence;
• knowledge representation in terms of natural language pertinent to the studied
domain, possibility to describe a set of the competences of each employee in the
language of the model, possibility of taking into account in the model the individual
attributes of each member of the cooperative learning group;
• obtaining holistic image of represented knowledge in the framework of hypotheses
accepted in the construction of models, which allows to take into account all
essential objects, their properties and relations involved in the problem to be solved,
as well as to neglect the insignificant ones;
• preservation of information contained in the original and obtain ment of new
information;
• accessibility of the model for research;
• representation of both declarative and procedural knowledge;
• conceptual structure consisting of concepts and relationships between concepts
should be unequivocal and unique.
Let us consider the comparison of properties of the basic models of knowledge
representation based on the aforementioned requirements.
Table 1. Comparison of Properties of Basic M odels of Knowledge Representation [26]
Knowledge Representation Models
Requirements for Knowledge
Logical Production Frame-Based S emantic
Representation Models
Model Model Model Network
Knowledge representation in terms of + + +
natural language
Declarative knowledge representation + + + +
Procedural knowledge representation + + + –
Representation of logical + + – –
connections in the domain
Representation of semantic relations + + +
in the domain
Visibility of knowledge description – – – +
Integrity of knowledge structure – – – +
representation
The data presented in Table 1 ("+" symbol indicates the presence of corresponding
properties in the representation model, “ ” symbol – partial presence, “–” symbol –
absence) shows that semantic network model meets the greatest number of requirements.
As a result, the model of knowledge representation to be developed should be based on
this model taking into account development of its logical and procedural properties.
Extended semantic network is one of the varieties of this improved semantic network
models. Extended semantic netwo rks have been developed to eliminate heterogeneity of
the usual semantic networks, which is caused by presence of the aggregates of objects
lin ked by t ies of relat ions, interconnected relations and other factors [31]. An impo rtant
aspect of extended semantic network is its ability to represent procedural knowledge and
also logical constructions.
5 Extended Semantic Networks
The concept of semantic networks of knowledge repres entation is based on the
idea that all knowledge can be represented as a set of objects (concepts) and links
(relations) between them.
The main advantages of semantic networks are as follows:
• proximity of the network structure to the semantic structure of phrases in natural
language;
• visuality of knowledge system represented graphically;
• versatility achieved by selecting an appropriate set of relations;
• possibility to connect different network fragments;
• definition of operations performed on objects;
• for each operation on data or knowledge, it is possible to allocate a certain part of
network that covers the essential characteristics of request.
• Semantic models also have some disadvantages :
• an arbitrary structure and different node and relat ion types complicate informat ion
processing;
• semantic networks have no special means to determine time dependencies;
• complexity of exception handling;
• representation, usage, and modification of knowledge and description of systems at the
level of complexity corresponding to that of reality is a time-consuming procedure;
• processing of network models requires a special apparatus of formal withdrawal and
planning.
In extended semantic networks , nodes can correspond not only to objects or concepts, but
also to relations, logical co mponents of information, co mp lex objects and others . To
everything that can be regarded as an independent unit, its own node must correspond.
Thus, nodes of different type are entered – nodes corresponding to names of relat ions, as
well as a special co mposite element called connection node. They are connected by
marked edges with nodes taken from the array of above-mentioned nodes. As a result, a
frag ment appears that corresponds to elementary situation, i.e. objects that are bound by
relation. Such a fragment is called an elementary one [31].
The basis of extended semantic networks is a set of nodes fro m which elementary
frag ments D0 (D1 , D2 , ... , Dk / Dk+1 ) are co mp iled, where D0 stands for relation name; D1 ,
D2 , ... , Dk – for the objects participating in relation; Dk+1 – for the connection node
denoting the whole array of objects participating in the relat ion; this node is also called c-
node of elementary fragment; D0 , D1 , D2 , ... , Dk+1 D, к>0 [32].
Extended semantic networks are considered as a finite set of elementary frag ments .
Names of relat ions play the role of objects and can enter into relations . This defines high
homogeneity of the model. Connection node of elementary frag ment can be part of other
elementary fragments but not as a c-node.
The set of nodes is partitioned into three disjoint subsets [33]:
D = G∪X∪E ,
where G stands for detected nodes (definite co mponents ); X – for undetected nodes
(variable co mponents, their roles are defined in further processing of the model; Е – fo r
special nodes (used in the production description).
In its turn, the G set consists of three subsets :
G = R∪A∪{t,f} ,
where R stands for an array of relation names (corresponding to D0 ); А – for an array of
concepts; {t,f} – for logical co mponent D pointing to the truth or falsity of the relations of
the represented relation, t – true, f – false.
Let us represent, for examp le, the most common view of intelligent collaborative
educational system in the form of a frag ment of extended semantic network. We shall use
a reduced image of the elementary frag ment. At first, we don’t draw the node {t,f} with
its edges. We assume that if this node is not connected to connection node by dint of
corresponding frag ment, true (t) relations are represented. It will simp lify the drawing .
Secondly, we put a special character of the empty space (_) in comp liance with
unimportant or unessential components . This symbol is required to represent relations for
which not all components are specified but only the necessary ones.
As already mentioned, a collaborative intelligent educational system (CIES) is based
on four elements: interconnection (IC), instrumentation (INST), intelligence (INT) and
shared activity (SA). Thus, we can write the following expression:
Based on (CIES, IC, INST, INT, SA /_):
Fig.2. A fragment of description of a collaborative intelligent educational system
Operations on elementary frag ments can be expressed by the objects themselves. The
following nodes are entered into R to represent operations [33]:
• for set-theoretic relations – {∩, ∪,∈, \} ⊂ R;
• for arithmetic expressions – {+, -, *, :} ⊂ R;
• for logical constructions – {∧, ∨, ¬} ⊂ R;
• for the language of predicate logic – {∀, ∃ } ⊂ R;
• for queries – node ? ∈ R;
• for representation of productions – nodes corresponding to cause-effect and part-whole
relations allowing to represent the dynamic of changes of objects and strategy of their
behavior.
For processing of knowledge represented by extended semantic networks , the princip le o f
matching pattern in the form of t wo network overlay method is used. It is based on
identification of rules allo wing to b ind nodes and compare networks in accordance with
laws of logic [33].
The authors think that it is necessary to modify a set of operations on elementary
frag ments to use extended semantic networks for solving the problem of collaborative
intelligent educational system development, because the whole domain of logical
connections cannot be represented in existing extended semantic network models. The
future work of the authors will be devoted to this issue.
6 Conclusions and Future work
In this paper, concepts of knowledge worker and collaborative intelligent educational
systems have been considered, basic models of knowledge representation have been
reviewed. The authors have briefly described advantages of extended semantic networks
for knowledge representation in these systems and have shown that these networks meet
the requirements more fully. Thus, the authors think that use of extended semantic
networks in their future work is expedient. Future work will focus on the evolution of
extended semantic network models, the general scheme of the imp rovement and detailed
elaboration of collaborative intelligent educational systems, their p ractical implementation
as a prototype and approbation in real conditions of modern higher education institution s.
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