THE ORM MODEL AS A KNOWLEDGE REPRESENTATION
FOR E-TUTORIAL SYSTEMS
Thanaporn Leelawatananon
Faculty of Information Technology,
King Mongkut’s Institute of Technology, Ladkrabang, Bangkok 10520, Thailand,
Suphamit Chittayasothorn
Faculty of Engineering,
King Mongkut’s Institute of Technology, Ladkrabang, Bangkok 10520, Thailand,
Keywords: Object Role Model, Knowledge-based System, Knowledge Representation, e-Tutorial.
Abstract: At present information technology plays important roles in teaching and learning activities. E-learning
systems have the potential to reduce operating costs and train more people. Teachers and students do not
have to be in the same place at the same time and the students have the opportunity to perform self-studies
and self-evaluation using e-tutorial systems. E-learning systems could be considered expert systems in the
sense that they provide expert advice in particular subjects of studies to students. The exploitation of
knowledge base and knowledge representation techniques is therefore vital to the development of e-learning
systems. This paper presents the development of a knowledge-based e-tutorial system that uses the Object
Role Model (ORM) as its knowledge representation. The system provides Physics tutorials. It was
implemented in Prolog and the knowledge base is on a relational database server.
1 INTRODUCTION
Nowadays, the most common underlying data
management facilities of e-learning systems are still
the conventional files and record structures. Most e-
learning systems interact directly with files and
tables without knowledge representations. Recently,
the use of ontology and knowledge representation
models in e-learning systems appears in literatures.
Resource Definition Framework (RDF) ) (Brickley
and Guha, 2000) and Ontology Interchange
Language (OIL) (Studer, 2000) are used to
represent knowledge in e-learning systems based on
the Semantic Web (Stojanovic et al., 2001).
This paper presents an e-tutorial system which
uses the Object Role Model (ORM) (Halpin, 2003)
as a knowledge representation. The ORM model is a
well-established conceptual schema model
originated in Europe during the 1970s and was
originally called NIAM (Verheijen and van Bekkum,
1982). It is a popular conceptual schema model for
relational database design. The transformation from
an ORM conceptual schema diagram to relational
database schemas guarantees the project/join normal
form (5NF).
Conventional knowledge-based systems that
implement the knowledge base on a relational
database use relational database schemas as
predicate structures structures (Norrie et al., 1995;
Albernethy and Altman, 1998; Maier et al., 2002). A
tuple is perceived as a predicate instance. The
problems of this approach are that a tuple of a
relation may contain several facts only one of which
is relevant and a tuple may have null values. Using
the ORM conceptual schema as a knowledge
representation solves these problems. An ORM fact
type is a predicate structure and a fact instance
becomes a predicate instance. Fact instances always
have the truth value “true” following the 2-value
logic and null value is not permitted in ORM. These
properties make ORM a suitable knowledge
representation for knowledge base systems
implemented on large shared relational databases.
479
Leelawatananon T. and Chittayasothorn S. (2004).
THE ORM MODEL AS A KNOWLEDGE REPRESENTATION FOR E-TUTORIAL SYSTEMS.
In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 479-484
DOI: 10.5220/0002655504790484
Copyright
c
SciTePress
2 OBJECT ROLE MODEL
ORM is a fact-based conceptual schema model. Its
main constructs are entity types, label types, fact
types, reference types, subtype hierarchies and some
static integrity constraints. It demonstrates clear
distinction between the concept of entity types and
the naming of the entity types (label types).
An entity type is an object of interest. An entity
type may have several label types (value types)
associate with it via reference types. Label types are
naming of entity types and there is no concept of
attributes in ORM. A one-to-one reference type is
chosen to be the unique identifier of the entity type.
Other reference types remain alternative identifiers
of the entity type. Figure 1a shows an entity type
STUDENT together with its label types SNAME
and ID#. Figure 1b shows that ID# is chosen to be
the unique identifier of STUDENT. The unique
identifier is shown in brackets inside of the entity
type. SNAME remains in a reference type.
Relationships between entity types are called fact
types. These fact types must be elementary (cannot
be further decomposed). N-ary fact types are also
allowed. A uniqueness constraint enforces
uniqueness of entity instances participated in the fact
type. A transformation algorithm from an ORM
allowed. A uniqueness constraint enforces
uniqueness of entity instances participated in the fact
type. A transformation algorithm from an ORM
conceptual schema to relational database schemas
takes advantage of this non-decomposable property.
Each fact type is perceived as a relational database
schema in 5NF. Fact types associated with a
common entity type and have the uniqueness
constraint enforced on the entity type form a
relational schema in 5NF (Halpin, 2003). Figure 1c
shows ORM fact types and corresponding relational
database schemas.
3 ORM AS A KNOWLEDGE
REPRESENTATION MODEL
From the previous section, the ORM model is
perceived as a relational database design tool that
guarantees the 5NF relational database schemas. In
this project, we use it as the logical structure that our
e-learning system works on. ORM is the knowledge
representation of our system.
Figure 1: a) An entity type and label types. b) An entity type together with its unique identifier and a
reference type. c) An ORM conceptual schema diagram and corresponding relation schemas.
ICEIS 2004 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS
480
ELECTRIC_FIELD
CHARGE_DISTRIBUTION
(CDISTRIBUTION)
DISTRIBUTION_SYMBOL
(SSYMBOL)
CONDUCTOR
(CID#)
TOTALCHARGE_FORMULAR
(TCFORMULAR)
GAUSSIAN_SHAPE
(GSHAPE)
SURFACE_SIDE
(SSIDE)
SURFACE_AREA
(SID#)
VARIABLE
(VCODE#)
VARIABLE_CONSTAN T
(VCONSTANT)
VARIABLE_DIRECTION
(VDIRECTION)
VARIABLE_UNIT
(VUNIT)
MULTIPLE_VECTOR_SYMBOL
(MVSYMBOL)
MULTIPLE_VECTOR_TYPE
(MVTYPE)
MULTIPLE_VECTOR_FO RMULAR
(MVFORMULAR)
VARIABLE_SYMBO L
(VSYMBOL)
Figure 2: ORM diagram for the Gaussian electric field calculation
THE ORM MODEL AS A KNOWLEDGE REPRESENTATION FOR E-TUTORIAL SYSTEMS
481
This approach has the advantage over the use of
conventional knowledge representations such as
Frames and Semantic Networks (Waterman, 1999)
when implemented in relational database environ-
ments. There are no transformation algorithms that
guarantee minimum redundancies and the 5NF
(project/join normal form) from Frames and
Semantic Networks. This is due to the fact that they
are pointer-based representations. Most artificial
intelligence systems implement them as Lisp
programs. ORM, on the other hand, was developed
for database modeling. The implementation of an
ORM knowledge base using commercially available
relational DBMS enables the knowledge base to be
shared by many user applications. such as recovery
control, concurrency control, indexing and query
optimizations are readily available for our
knowledge-based e-tutorial system. Figure 2 shows
the ORM diagram for the Gaussian electric field
calculation topics of the e-tutorial system. Another
important point of using the ORM model as a
knowledge representation is that each predicate
instance corresponds with an ORM fact instance, not
the entire tuple of a relation.
This is Figure 2:
ORM diagram for the Gaussian electric field
calculation very useful since each tuple could
contain many facts. Classical and more recent
interfaces between expert systems and database
systems refer to a relation as a predicate instance
(Wang, 2000; Nick et al., 2001). This is not realistic
in practice because there could be irrelevant facts on
each tuple. It is proposed that a predicate instance
refers to a fact instance of an ORM fact type.
4 ORM META CONCEPTUAL
SCHEMA
An ORM meta conceptual schema is a conceptual
schema that describes the ORM conceptual schema
model. Since the users’ ORM schema for domain
knowledge must be stored on the database, a set of
system tables is required to keep the information
about the users’ conceptual schemas. The meta
conceptual schema is transformed into relational
schemas for the system tables that keep information
about the users’ ORM schemas. Figure 3 shows the
ORM meta conceptual schema which is used by our
e-tutorial system.
5 AN E-TUTORIAL SYSTEM FOR
PHYSICS
The prototype e-tutorial system presented in this
paper gives Physics tutorials. It assists student’s
work on Physics exercise questions and evaluates
students’ understanding of the topic. The exercises
are grouped in chapters. For each exercise, the
system asks questions to guide the student to the
solution of the problem. The questions are
sequenced in the following order: questions on the
formulae used for the given exercise, questions
about relevant variables, questions about the main
knowledge of the exercise and the application of
formulae to obtain the result.
During a working session the system analyses
the answer to each question to evaluate the student’s
understanding of the topic and shows the marks and
evaluation result to the student. The system interacts
with its ORM knowledge base to obtain related
knowledge for the guidance and evaluations.
The system is implemented in WinProlog
(Steel, 2000) and the underling DBMS is MS
SQL*Server (Rebecca, 2000). ORM fact types are
implemented as views and Prolog retrieves the
content of the views when it consults the database.
This means that the Prolog program is not aware of
the underlying tables and refers to fact type views
only. Each Prolog predicate instance an ORM fact
instance, not the entire tuple of a relation. Figure 4
shows data flow diagrams that describe the e-
tutorial system. Figure 5 shows a sample study
session in Gaussian electric field calculation. The
feature of our prototype e-tutorial system is
comparable to other systems such as ANDES system
(Joel, 2000), and another web-based tutorial system
for engineering, mathematics and science subjects
(Scott and Stone, 2000). Our prototype system
guides the students step by step while some of these
systems such as ANDES give the full guidance first
and then let the students solve the problem
afterwards. However, the features of the e tutorial
system is not the main issue here. It is the ability to
store Physics knowledge on relational database
using the ORM model as the knowledge
representation.
6 CONCLUSION
The e-tutorial system presented in this paper uses the
ORM conceptual schema model as its knowledge
representation. The system refers to fact instances of
fact types when it analyzes student’s answers and
evaluates the level of understanding of the topic.
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This is a new approach in knowledge-based system
design and implementation on large shared
databases. Developers use SQL to access the
knowledge base. So, this approach is easy to
Figure 3: An ORM Meta Conceptual Schema
Rol e
(Nr)
Pl ays
Pl ays
Ent i ty Type
(Name)
Label Type
(Name)
Spans
has
has
Constrai nt
(Nr)
KOC
(Code)
has
has
Constrai ntType
(Code)
NOR
(Name)
{Unary,Binar y,N_ary}
{UI,UE,MR}
has
AOR
(Code)
{ MN, N M, MM, N N}
UniqueIdenti find
(Name)
has
Relati onshi p
(Nr)
KOC=Kind of Cadinality
NOR=Name of Relationship
AOR=Amount of Role
UI=Uniqueness Internal
UE=Uniqueness External
MR=Mandatory Constraint
MN=Many-to-Many
NM=One-to-Many
NN=One-to-One
MN=Many-to-One
Figure 4: A data flow diagram for the e
-
tutorial system
THE ORM MODEL AS A KNOWLEDGE REPRESENTATION FOR E-TUTORIAL SYSTEMS
483
implement from the developer’s point of view. The
knowledge can be shared, reuse, and manage in
multi-user environment by database servers.
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Figure 5: A sample study session in Gaussian electric field calculation
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