Cognitive Style Affecting Visual Ontology Design
KOMET Project Results
Tatiana A. Gavrilova and Irina A. Leshcheva
Graduate School of Management, Saint Petersburg University, Volkhovskiy Pereulok, Saint Petersburg, Russia
Keywords: Ontologies, Knowledge Engineering, Cognitive Style, Conceptual Structuring, Collaborative Design,
Cognitive Ergonomics.
Abstract: The paper presents the main results of research performed within the KOMET (Knowledge and cOntent
structuring via METhods of collaborative ontology design) project that was aimed at developing a novel
paradigm for knowledge structuring. By knowledge structure we define the main domain concepts and
relations between them in a form of graph, map or diagram. Knowledge structures represent conceptual
models. This approach considers individual cognitive styles and uses recent advances in knowledge
engineering and conceptual structuring; it aims at creating new consistent and structurally holistic
knowledge bases for various areas of science and technology. Research into correlations between the
expert’s individual cognitive style and the peculiarities of expert’s subject domain ontology development
has been completed. Implications for practice are briefly delineated.
1 INTRODUCTION
One of the main objectives of the research process is
achieving maximal effectiveness from the creation,
transfer and dissemination of new knowledge. This
effectiveness can be measured by the quality and
speed of memorization of the principal concepts of a
particular domain and of the relationship between
these concepts. Wide evidence exists that the use
visual thinking to address the subject of study is
positively connected with the quality and speed of
memorization, and thus with the effectiveness of
knowledge dissemination. Visualization working as
a cognitive tool also facilitates communication
within research communities.
Special interest in such forms of knowledge
codification can be observed in education science,
especially within learning where the students are
engaged in group knowledge sharing and co-creation
processes with continuous feedback.
This paper presents the main results of the
KOMET (Knowledge and cOntent structuring via
METhods of collaborative ontology design) ) project
which was devoted to developing methods of using
visual ontology design in research and education
with regards to the respondents’ individual cognitive
styles. All the 79 respondents were graduate students
of the School of Computer Science of Saint
Petersburg Polytechnic University. Almost all the
students had 1-2 years’ experience of research in
computer science, and were in their fifth year of
study on the Masters programme. The preliminary
results of this study were partly discussed in
Gavrilova et al. (2013). The domain “informatics”
was chosen as all the students are young
professionals in this area. We use the term as
synonym to “computer science”.
During the last decade, visual knowledge
representation has become one of the key
considerations in knowledge engineering
methodology, and it is strongly associated with
ontology design and development. These ontologies,
which form a conceptual skeleton of the modelled
domain, might serve various purposes such as better
understanding, knowledge creation, knowledge
sharing, and collaborative learning, problem solving,
seeking advice, or developing competences by
learning from peers. Recently, the ontological
engineering perspective has gained interest in many
research domains, such as medicine, business and
computer science (Schnotz, Kurschner, 2008;
Pfister, Eppler, 2012; Oltramari, Ferrario, 2009;
Brochhausen et al., 2011). These studies rely heavily
on theory and tools from knowledge engineering
analysis that already has a long-standing tradition in
the knowledge-based systems domain (Mizoguchi,
Bordeau, 2000).
207
A. Gavrilova T. and A. Leshcheva I..
Cognitive Style Affecting Visual Ontology Design - KOMET Project Results.
DOI: 10.5220/0005037302070214
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2014), pages 207-214
ISBN: 978-989-758-049-9
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
2 RELATED WORK ON
ONTOLOGY ENGINEERING
AND MAPPING
This project was targeted at developing a paradigm
of data and knowledge structuring with regard to
individual cognitive styles, using recent advances in
knowledge engineering and conceptual structuring,
aimed at creating structurally holistic knowledge
bases for various areas of science and technology.
This aim was decomposed into such objectives
as:
research of correlations between the expert’s
individual cognitive style and the peculiarities
of the expert’s subject domain ontology
development,
research of formal ontology evaluation
methods from the cognitive ergonomics point
of view.
The idea of using visual structuring of
information to improve the quality of understanding
and mentalization among research colleagues is not
new (Shneiderman, 1996). For more than twenty
years, concept mapping (Grosslight et al., 1991;
Sowa, 1984; Jonassen, 1998; Conlon, 1997) has
been used to compile maps and mental models that
support the process of knowledge sharing.
Many scholars, especially those who also teach
sciences courses, operate as knowledge analysts or
knowledge engineers by making visible the skeleton
of the studied discipline and showing the domain’s
conceptual structure (Kinchin, De-Leij, Hay, 2005).
This structure is frequently represented by a so-
called “ontology”.
From a philosophical viewpoint, “ontology” is
the branch of philosophy which deals with the nature
and organization of reality. Ontologies aim at
capturing domain knowledge in a generic way and
providing a commonly agreed understanding of a
domain, which may be reused and shared across
applications and groups (Chandrasekaran,
Josephson, Benjamins, 1999). Neches and
colleagues (Neches et al., 1991) gave the classical
definition as follows “An ontology defines the basic
terms and relations comprising the vocabulary of a
topic area as well as the rules for combining terms
and relations to define extensions to the
vocabulary”.
The visual approach to presenting ontologies is
not only compact but also comprehensive. It makes
ontology a powerful mind tool (Jonassen, 1998;
Gavrilova, Voinov, 1996). Ontologies are also
widely and effectively used in education, and many
learning ontologies have been developed for a
number of disciplines (Barros et al., 2002; Gaeta,
Orciuoli, Ritrovato, 2009; Gavrilova, Leshcheva,
Bolotnikova, 2012).
However, the ontology-based approach to
knowledge representation in research and pedagogy
is a relatively new development. There are numerous
definitions of this milestone term (Neches et al.,
1991; Gruber, 1993; Guarino, Giaretta, 1998;
Gómez-Pérez, Fernández-López, Corcho, 2004).
Many researchers and practitioners have argued
about the distinctions between ontology and a
conceptual model. We propose that ontology
corresponds to the analyst’s view of the conceptual
model, but is not de facto the formal model itself.
There are more than a hundred techniques and
notations that help to define and visualize conceptual
models. Ontologies are now considered as the most
universal
Ontologies are useful structuring tools, in that
they provide an organizing axis along which every
researcher (or student) can mentally mark his/her
vision in the information hyper-space of domain
knowledge. Frequently, it is impossible to express
all the information as a single ontology.
Accordingly, subject knowledge storage consists of
a set of related ontologies.
Of course, the ontologies are inevitably
subjective to a certain extent, as knowledge by
definition includes a component of personal
subjective perception; however, using the ontologies
developed by others is a convenient and compact
means of acquiring new knowledge. At the same
time, collective ontology development experience
allows the participants in the process to gain the
fullest possible understanding of the subject area.
Meta-ontology provides a more general
description dealing with higher-level abstractions.
Figure 1 illustrates different ontology classifications
in the form of the mind map. This representation
may be called the knowledge map. Such maps are
graphical tools for organizing and representing
knowledge. Later in this paper and in our study we
will use two particularly appropriate forms of
knowledge maps: mind maps (Buzan, 2005) and
concept maps (Novak, 1998; Novak & Canas, 2006).
Knowledge maps are now widely used for
visualizing ontologies at the design stage, while
ontology editors (like Protégé) facilitate the
development stage. Research on knowledge
mapping in the last 12 years has produced a number
of consistent findings (O’Donnell, Dansereau, Hall,
2002). People recall more central ideas when they
learn from a concept map than when they learn from
KEOD2014-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment
208
Figure 1: Summarizing the ontology classifications in a mind-map.
text, and those with low verbal ability or low prior
knowledge often benefit the most. It seems that
knowledge maps reduce cognitive load.
3 COGNITIVE STYLES
FEATURES
The cognitive-styles view has acquired great
influence within the education field, and is
frequently encountered at levels ranging from
kindergarten to graduate school. There is a thriving
industry devoted to publishing cognitive-style tests
and guidebooks for teachers and educationalists
(Peterson, Rayner, Armstrong, 2009).
However, we will use the concept of cognitive
style only for the predefined aim. As the aim of the
KOMET project was to develop a paradigm of
structuring data and knowledge with regard to
individual cognitive styles, we had to choose the
appropriate features of cognitive style.
The cognitive styles explain and describe how an
individual acquires knowledge and processes
information. The cognitive styles are related to
problem solving, and generally to the way that
information is acquired, structured and used.
Among the main features of cognitive style
(Hayes, Allinson, 1998) we can name:
field dependence versus field independence
impulsivity versus reflection
narrowness versus width of the categories
rigidity versus flexibility
levelling versus sharpening
scope of cognitive equivalence
visual/audio/kinesthetic preferences.
Three characteristics have been chosen from the
plethora of cognitive style characteristics described
in the literature (Kholodnaya, 2004): field
dependence/field independence (FD/FID),
impulsivity/reflection, and narrowness/width of the
category.
According to the definition by Witkin (Witkin
et al., 1977), FD/FID is “a structuring ability of
perception”. The field-independent style is defined
by a tendency to separate details from the
surrounding context. It can be compared to the field-
dependent style, which is defined as a relative
inability to distinguish detail from other information
around it. The FD/FID characteristic can be
interpreted as a proxy of the structuring capability of
an individual mind. The characteristic of this style
does influence the structuring process as a whole
(e.g. ontology development “from scratch”), and
even more it affects the restructuring process (the
merging of individual ontologies). FD/FID exerts
considerable influence on the collective problem-
solving process. In dyads where members have
cognitive styles differentiated by the FD/FID
characteristic, the final solution is usually closer to
the variant suggested by the FID participant. The
FID dyads experience difficulty in developing
common decisions on arguable points, while the FD
dyads are more successful in coming to agreement in
collective problem solving.
CognitiveStyleAffectingVisualOntologyDesign-KOMETProjectResults
209
Psychologists in our research group were used to
working with on-line text based on the popular
modification of Witkin’s method of “embedded
figures” which is aimed at the search for a simple
figure hidden within the complicated one (Witkin,
1971).
The impulsivity/reflection characteristic
considers the amount of information collected prior
to making a decision: impulsive individuals are able
to make decisions on a considerably bounded
information basis, while the reflective individuals
are more inclined to make decisions considering
completely full information on the respective
situation. For assessing the respondents’
impulsivity/reflection features, the “similar pictures
comparison” (Kagan, 1966) method has been used.
As for the narrowness/width of the category, the
main difference between the extreme poles of this
characteristic is that narrowly categorizing
individuals are inclined to restrict the area of
application of a certain category, while the broad
categorizers are, conversely, inclined to include a
plethora of more-or-less related examples into a
single category. The psychologists that help us with
the experimental part advised us to use a
modification by Pettigrew (1958) and Fillenbaum
(1959): their method of so-called “average
judgment”. The procedure is based on respondents’
opinion on the minimum, average and maximum
evaluations of a concept or category. It is advisable
to keep all the given values.
4 METHODOLOGY
The research into an expert’s individual cognitive
style and ontology development objective was
divided into four consecutive phases:
1. Identifying the significant individual cognitive
style characteristics on the basis of the on-line
testing results, using the software ONTOmaster-
TECOS (http://ontomaster.ru) developed in
PHP and Java Script by Elena Kotova and
Andrew Pisarev (Kotova, 2013).
2. Creating the “informatics” research domain
ontologies using the Protégé tool (Protégé).
3. Informal assessment and formal automatic
estimating the ontology metrics using the
COAT software environment (Gavrilova,
Bolotnikova, Gorovoy, 2012).
4. Performing statistical analysis in order to find
out significant relationships between the young
researchers’ (experts’) individual cognitive style
characteristics and the ontology metrics.
The second phase of the research was performed
using the same test sample of students and included
ontology development in/with the use of the Protégé
tool. All the tested students were given the task of
developing an ontology for the informatics domain.
They did it by using visual mapping approach.
The quality of the developed ontologies was
assessed by two methods:
An expert method, where the ontology analyst
and domain experts (both professors in
computer science) assessed the quality by
such criteria as simplicity, completeness,
imbalance, relevance and some other.
A formalized method, where any ontology
was assessed by a set of quantitative metrics
using COAT software.
The formalized method was preferable as it was
free from experts’ and analysts’ subjective
interpretations and had the potential to be
automated.
5 COGNITIVE ERGONOMIC
METRICS
In our research the developed ontologies were
assessed by an augmented set of metrics (e.g.
minimal depth, absolute width, etc.) suggested in
Bolotnikova, Gavrilova and Gorovoy (2011). In
evaluating the quality of the designed ontologies, the
following two aspects are most important: (1)
correctness and depth of reflection of the subject
domain, and (2) ergonomic aspect of the ontology
representation from the point of view of quality and
human speed of perception.
The notation used to describe the metrics is the
following:
“g”, a graph representing an ontology; the
concepts (classes and exemplars) of the ontology are
the graph nodes, the relationships between the
concepts are the graph edges;
“G”, a set of all the nodes g;
“E”, a set of all the edges g.
A minimal depth:
mN
∈
, ∀iN
∈
N
∈
(1)
where N
∈
and N
∈
are the path lengths j and i
from the set of paths P of the graph g.
90% line depth:
mP
N
∈
(2)
where P
N
∈
is a 90% percentile of the graph
depth (possible value of the graph path length, not
KEOD2014-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment
210
exceeding the length of 90% of the graph paths).
An absolute width:
mN
∈
(3)
where N
∈
is a number of nodes of degree j from the
set of nodes L of the graph g.
Complexity metric:
A number of nodes with multiple inheritance to
the set of all the graph nodes:
m
N
∈
n
(4)
where MI
v∈G
|
∃a
,
a
isav,a
∧isav,a
is a set of all the graph nodes with more than one
“is-a” relationship arc, N
∈
is a number of all the
elements of this set, n
is a number of the graph
nodes.
Figure 2: Example of narrow and deep ontology structure.
Figure 3: Example of wide ontology structure.
Many aspects affect the quality of ontology from
the cognitive point of view. The COAT software
environment provides calculation of more than 20
metrics. Metrics of this kind were first proposed by
the research group of Aldo Gangemi (Gangemi et
al., 2006). The ontology evaluation based on these
metrics is formal but it helps to assess the ontology
quality. The complete list of metrics was presented
in detail in two works (Bolotnikova, Gavrilova,
Gorovoy, 2011; Gavrilova, Bolotnikova, Gorovoy,
2012).
These metrics can help to understand what
should be corrected in the description of the subject
domain in order to improve it from the point of view
of cognitive ergonomics or better perception. Thus it
is supposed that each next version of the ontology
will be better and it can be perceived faster by users.
The metrics can also be used in evaluating
ontologies of the same subject domain produced by
different people/teams. The calculated metrics help
to estimate which of them is better from the point of
view of cognitive ergonomics and to choose the best
of them if the evaluations of other important criteria
differ insignificantly. Figures 2 and 3 show different
types of ontology structures from described
perspective point of view.
6 MAIN HYPOTHESES
As mentioned in the introduction, the research
sample consisted of 79 students, enrolled in the
intelligent systems development course. All the
tested students were given the task of developing an
ontology for the informatics research domain. Due
to the professional specificity of the sample, a bias
toward narrow, reflective and field-independent test
persons was found in the sample. However, a
statistically significant Spearman’s negative
correlation between the FID score and the time of
the first answer in the Kagan was calculated,
showing that the sample was dominated by the fast
FID and slow FD respondents.
On the basis of the literature review and the
practical ontology development experience, the
following hypotheses are suggested:
Hypothesis 1. Individuals belonging to the FID
extreme point of the FD/FID cognitive style
characteristic tend to have highly developed
cognitive structuring capabilities; thus, the quality of
ontologies developed by the FID individuals would
be higher.
Hypothesis 2. Impulsive individuals tend to
develop superficial ontologies lacking sufficient
categorization in the upper level, while the reflective
individuals tend to develop deeper ontologies.
Hypothesis 3. Ontologies developed by the
individuals described as “imprecise” in the Kagan
impulsivity/reflectivity test results tend to be more
complex.
Hypothesis 4. The “narrowness/ width of the
category” cognitive style characteristic exerts
significant influence on the ontology width: the
“wide categorizers” tend to develop broader
ontologies.
Table 1 presents a part of the two series of
testing results. It describes the correlation
coefficients for several metrics and main parameters
CognitiveStyleAffectingVisualOntologyDesign-KOMETProjectResults
211
of cognitive style:
I/R — impulsiveness/reflexivity and
NС/WC — narrowness/width of category.
The correlation between the cognitive style and
ontology metrics values was assessed by Spearman’s
coefficient (rank correlation). The significant
correlation between the metrics and such features as
field dependence/ field independence was not found,
that is why it is not presented in the table. Empty
cells in the table mean that no correlation was found.
Hypothesis 1 was not confirmed, as no
significant correlation between the FD/FID metric
and the quality of the ontologies was found; this
result gave rise to optimistic feelings about the
whole project, as it shows that it is possible to teach
any individual to develop ontologies of a high
quality.
Hypothesis 2 was partially confirmed: the “90%
line depth” metric demonstrated significant positive
correlations with the time of the first answer in the
Kagan test, thus showing that reflective test persons
tend to develop deeper ontologies; however, no
significant negative correlation between the time of
the first answer and the ontology width was found.
Hypothesis 3 was confirmed, as the number of
mistakes in the Kagan test demonstrated a
significant positive correlation with the values of the
“Average number of parents of a graph node” metric
that characterizes the ontology complexity.
Furthermore, the number of mistakes in the
Kagan test demonstrated significant positive
correlations with the metrics of the “Minimal depth
of the ontology” and the “Families branching
coefficient” and significant negative correlation with
the weighted leaves branching coefficient.
Hypothesis 4 was fully supported: the broad
categorizers developed bigger ontologies in terms of
the number of concepts, achieved mainly by the
greater number of “children” of each parent concept.
Respectively, the results of the “Average
judgments” test demonstrated significant
correlations with such metrics as the “Average
ontology width”, “Number of leaves”, “Absolute
cardinality of families”. These results also
demonstrated significant correlation with the root-
mean-square deviation of the average ontology
width. This result shows that the number of concepts
belonging to the neighbouring levels and to different
branches is significantly different, indicating
imbalance in the ontologies developed by the wide
categorizers.
Despite the objectivity of the quantitative
metrics-based method of ontology assessment, this
method has the significant drawback of being too
formalized and lacking semantic analysis elements.
Having augmented the quantitative metrics-based
analysis by a semantic analysis performed manually,
we found that the ontologies developed by the field-
independent test persons tend to have simpler and
clearer structure. However, this simplicity and
clarity tends to be achieved by truncating the
concepts that do not fit into the developed ontology,
thus sacrificing the ontology’s completeness and
integrity for formal logical consistency.
So, the following relationships between the
respondent’ individual cognitive styles and the
peculiarities of respondents’ subject domain
ontology development have been identified as a
result of the research:
Table 1: Correlation matrix illustrating the correspondence between ontology metrics and the cognitive styles’ indices.
Metrics
Test results
I/R NC/WC
The time of the
first answer
The number of
mistakes
The size of the
category
Number of classes 0,44
Number of leaves 0,46
Absolute depth 0,39
Minimum depth 0,54
90th percentile depth 0,34
The average width 0,48
The standard deviation of the relative width 0,48
Average number of parents of a graph node 0,47
The absolute cardinality of families 0,44
Branching factor 0,50
The absolute cardinality of leaves 0,46
The weighting factor branching leaves -0,39
KEOD2014-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment
212
Considering the “impulsivity/reflection” scale,
the reflective individuals tend to develop
deeper ontologies;
The ontologies developed by the imprecise
individuals (as defined in the Kagan test) tend
to be more complex;
The “narrowness/ width of the category”
cognitive style affects the ontology branching
coefficient, i.e. the ontology width.
7 CONCLUSIONS
This study deals with the conceptual limitations of
traditional research communication and proposes
using a visual metaphor for illustration and
presentation of the research state-of-the art and main
findings.
Using visual inspection of the ontology it is
possible to detect gaps and misunderstandings in the
state-of-the-art knowledge level and cognitive model
of the domain knowledge. However, there is as yet
little consensus on the useful design and
orchestration of such structures. Furthermore, in
many cases it is not known what the structure of
socially legitimate knowledge patterns looks like, or
how a particular instance of a knowledge model
deviates from that “ideal” state (e.g. guru’s view)
(Cross et al., 2001). However, researchers are
individuals, and they may disagree among
themselves.
The authors made only a first step in the
interdisciplinary research field dedicated to the
inquiry into the affect of the expert’s individual
cognitive style parameters on the group structuring
design activity. Our results are therefore of a
preliminary nature.
Our work presents a novel perspective on
ontology development from the psychological point
of view. Using recent advances in knowledge
engineering and a human factors approach, we aim
at creating new consistent knowledge bases for
various areas of science and education.
ACKNOWLEDGEMENTS
Thanks to Elene Kotova and Andrey Pisarev for
using their software tool ONTOmaster-TECOS for
testing of cognitive style parameters.
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