Adding ‘Sense’ to Conceptual Modeling: An Interdisciplinary
Approach
Veikko Halttunen
Faculty of Information Technology, University of Jyväskylä, Mattilanniemi 2, Jyväskylä, Finland
Keywords: Conceptual Modeling, Concept, Conception, Perception, Semantics, Consistency, Possible World.
Abstract: In this paper, our aim is to widen the prevailing foundations of conceptual modeling theories and practices,
particularly in the context of information systems development. The approach shifts the focus from the link
between a model and the modelled reality to the link between human cognition and the model. Our approach
combines theoretical issues of different disciplines relevant to conceptual modeling. We shall make an explicit
distinction between individual conceptions and interpersonal concepts and show how this distinction could
be utilized to have conceptual models of a better consistency. We wish that this article could also serve as a
starting point for a profound scientific discussion on the real sources of conceptual models, i.e. the human
mind.
1 INTRODUCTION
In this paper, our aim is a step towards the unification
of thought and language (see Almog, 2005) in the
context of conceptual modeling. Practically, the
target is to improve the logical consistency of
conceptual models by ensuring that the individual
interpretations of reality form a more consistent
whole.
It is widely acknowledged that conceptual
modeling (CM) is a crucial part of information
systems development (e.g. Clarke et al., 2016; Wand
& Weber, 2002). Especially, modern complex IT
applications, like big data systems, require solid CM
tools and practices (see Storey & Song, 2017).
Attempts to build a conceptual model of an enterprise
architecture, for example, may be an extremely
complex task (Halttunen et al.,2006).
While numerous models are needed before a
complex system is in action (see Wand, 1996), it can
be extremely difficult to have a shared view of what
all the models mean or even what they should
represent. So far, the researchers’ main concern has
been the accuracy and consistency of the modeling
language. Instead, human thinking and conception as
the starting point for, and the content of, a conceptual
model has gained less attention. This is surprising,
since the conflict not only between the modelers’
perceptions but also between modelers’ conceptions
is evident (see Easterbrook, 1991).
Considering conceptual modeling as semantic
modeling (e.g. Wand et al. 1999) implies that it is not
only a matter of detail-hiding or abstraction but also
of carrying meaning, or sense-making. While
formalization of conceptual models is an important
issue in the IS field (e.g. ter Hofstede & Proper,
1998), it is necessary to understand, how formalized
models could be produced from human cognition (see
Siau and Tan, 2005).
We suggest that the formalization of conceptual
models should start from the very first
communicative phases of an ISD process (e.g. see
Chen et al., 1999). Actually, there are several severe
attempts to take the social and communicative
perspectives into account, e.g. Soft Systems
Methodology by Checkland (1981), ETHICS by
Mumford (1983), speech act based methods like
SAMPO by Auramäki et al. (1988) as well as tools
and methods based on communicative genres (e.g.
Päivärinta, 2001). More philosophical discussions
can be found in Hirschheim et al. (1995), Lyytinen
(1985), and Hanseth and Monteiro (1994), for
example. Nevertheless, none of these contributions
deals thoroughly with the link between the informal
specifications and the more formal ones.
When considering the development of conceptual
modeling there are three relevant, interacting worlds:
the physical world, the individual world(s) and the
248
Halttunen, V.
Adding ‘Sense’ to Conceptual Modeling: An Interdisciplinary Approach.
DOI: 10.5220/0008069702480255
In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 248-255
ISBN: 978-989-758-382-7
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
interpersonal world(s). To describe and clarify the
three worlds and the linkages between them we shall
utilize both philosophy and linguistics. Especially, we
make use of the theories of meaning. We combine
semantics of ordinary language (e.g. Lyons, 1971)
with a more formal approach called possible worlds
semantics (e.g. see Copeland, 2002).
The rest of the paper is organized as follows. We
start by describing the problem. Next, we shall
provide the foundational concepts upon which we
build our approach. In Chapter 4, we consider human
consciousness as a philosophical-linguistic concern.
In Chapter 5, we present a simple conceptualization
model. In Chapter 6, we provide preliminary ideas
how traditional conceptual models can be extended
by features that help to link thinking and talking about
a domain to more formal specifications. Finally, we
sum up the ideas of the paper.
2 SEARCHING FOR THE
BUILDING BLOCKS OF THE
BRIDGE
In order to get semantically rich, yet logically
consistent models of a domain, the modeling process
should ensure (1) that the sense of the concepts is
understood and agreed by the relevant parties of the
domain, and (2) that the agreement is conceptually
consistent. Our assumption is that the easiness and
the quality of interpersonal target-oriented
communication is dependent on how consistent and
reasonable the individuals’ interpretations are. When
the worlds of individual conceptions are not logically
consistent, it is not possible to build a consistent
whole, while a consistent and inclusive individual
world can correct and complete other individual
worlds. Consider the following example.
Let there be four persons A, B, C, and D. Let there
also be two things with attributes: a red car and a
green book. One of the persons, say D, suffers from
red green color blindness. While perceptions of red
light for A, B and C are mapped to their conceptions
in a way that there is a correspondence between the
perceptions and the concept of ’red’, for D the
concept of ’red’ is purely abstract having no
particular content of perception. This is because he
cannot say, for example, whether the car is red or
green, what the color of the book is or, how the colors
differ from each other. This knowledge must be
communicated to him. However, D knows through
language that there are red and green things and that
these colors can be used, for example, to define other
concepts. Thus, he can build even a large conceptual
net in which part of the concepts has no
correspondence to his experiential world. In this case,
the shared concept of ‘red’ has referent in the real
world, while the conception of ‘red’ for D does not
have. The D’s conception of ‘red’ is based on the
others’ descriptions of ‘red’, and has the reference
only in the interpersonal mental world.
Even when the world of individual conceptions
has a direct link to the world we can hear, see, and
feel physical world the individual world is more
or less ambiguous, since it is based on (1) physical
phenomena, (2) internal cognition, and (3) social
construction. The last viewpoint is extremely
important (e.g. von Braun et al., 2000). The social
construction means that our internal mental states are
strongly affected by communicating and
compromising the individual conceptions of fellow
creatures.
3 FOUNDATIONAL CONCEPTS
Communication among human beings is a result of a
sequence of cognitive and linguistic actions.
Falkenberg et al. (1998) and von Braun et al. (2000)
talk about perceptions, conceptions, and
representations. Perceptions can be seen as patterns of
visual, auditory or other sensations of one’s mind,
whereas conception is a result from the process
through which a human actor interprets a perception
in his mind. In order to communicate these
conceptions one needs communicative constructions
(representations).
Our foundational concepts in this paper include
perception, conception, concept, meaning, and sense.
In the rest of the article, we use them in the following
way.
Perception is a product of human mind that is
limited to a single event or, more strictly, to a physical
phenomenon that can be realized by human senses.
Observation could be used as a synonym for
perception.
Conception is a product of human mind that could
also be called an idea or a thought that has conceptual
content. When a perception starts to be
communicative, it “turns into” a conception in the
human cognitive system.
While a conception is a mental thing that can be
communicated with other people, it can be
distinguished from a concept (see Macià, 1998).
Whereas a concept can be seen as a carrier of shared
meaning for a group, a conception is a carrier of
meaning of this particular concept for an individual.
Adding ‘Sense’ to Conceptual Modeling: An Interdisciplinary Approach
249
Thus, a conception can also be called an individual
interpretation of a concept, and, vice versa, a concept
can be seen as a consensus on individual conceptions
at a certain point of time. Both conceptions and
concepts are “named things”. They are referred to by
their names, which we call words. The meaning and
the name together form a sign that is used in
communication (see Lyons, 1971).
The difference between a conception and a
concept is in the meaning of the sign. Hereafter, when
we refer to the meaning at individual level, we use the
word meaning. Instead, when we talk about meaning
at the interpersonal level, we use the word sense.
Getting closer to the meaning helps us to get more
stable, accurate and consistent sense for concepts, and
thus, conceptual models of a higher quality. The
better quality could benefit ontology engineering, for
example, since ontologies can be seen as explicit and
formal specification of shared conceptualization
(Studer et al., 1998).
Like abilities to perceive are dependent on the
observer’s senses, “interpreting abilities” are
dependent on the interpreter’s whole history as an
intellectual being. Therefore, conceptions cannot be
considered static but sensible to all chances in
circumstances.
4 HUMAN CONSCIOUSNESS – A
PHILOSOPHICAL-LINGUISTIC
PROBLEM
People differ from other creatures in that we are
aware communicators implying that can use symbols
to discuss symbols (Stacks et al. 1991). Furthermore,
we can refer to different things that are far from the
situation: we can talk about what happened in the past
or what would possibly happen in the future, we can
talk about abstract things that nobody has ever
observed, we can talk about meanings or meanings of
meanings etc. People have the ability of thinking not
anchored to a particular perceptual context
(Katzafanas, 2005, p. 9).
Indeed, language is a core feature of humankind.
As Brand (2004, p. 317) puts it: “Language
interpenetrates the human; it is not distinct from
either thought or world, but it is thanks to language
we have on one hand a subject capable of thought, and
on the other, a meaningful world.”
Popper’s thesis of three worlds provide a useful
tool for our purpose. The three worlds are: (1) the
physical world or the world of physical states, (2) the
mental world or the world of mental states, and (3)
the world of intelligibles or of ideas in the objective
sense (Popper 1972, p. 154). The last one is what
Popper calls the third world, which he also describes
as “world of possible objects of thought”.
The physical world is very much what we, in
ordinary speaking, call reality. The second world is
subjective. It consists of individual consciousness.
The third world is populated by human products,
which are purified by critical argumentation. Popper
(1972, p. 107) says that “…inmates of this world are
critical arguments, and what may be called… the state
of a discussion…” The first and the third worlds
cannot interact directly but only through the second
world.
In the following, we consider human
communication from the viewpoint of semantics, in
general, and possible worlds semantics, in particular.
4.1 Theories of Meaning
Meaning as a linguistic issue has interested both
philosophers and linguists. In linguistics, it is
primarily seen as a matter of semantics, but also
pragmatics deals with meanings (Hudson 1984, p. 4).
Semantics as a term is quite new. It can be traced
back to the late 19th century (Lyons 1971, p. 400).
Around that time a German philosopher and
mathematician Gottlob Frege made remarkable
philosophical work (Frege, 1892) that helped to
consider ‘meaning’ as both an ontological and a
linguistic issue. His aim was to unify language and
thought, but it is not clear how four-square he
believed in his own ideal (Almog, 2005)
In short, Frege (1982) made the well-known
distinction between reference (Bedeutung) and sense
(Sinn). Frege talks about references and senses of
signs (i.e. names, combinations of words, letters). The
reference of a sign is the thing the sign refers to.
Defining the sense of a sign is a more complex task.
The easiest way may be to make use of the notion of
‘idea’ (Vorstellung). Frege says that the idea is an
internal image of the reference. It is always
subjective. According to Frege the sense is something
that is between an idea and the reference: it is not
subjective like the idea, but it is not the object of the
sign (= the reference) itself either.
It is obvious that without a notion similar to
Frege’s ‘idea’, “the human factor” of information
systems design is limited to, and by, lingual elements
that can be identified and (pre-)defined (those that
carry senses). Although CM is about modeling the
senses of concepts of a domain, and we therefore need
pre-defined concepts, a high quality model of a non-
KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development
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trivial domain require good understanding of
individual conceptions on the domain.
Lyons (1971, p. 427) says that “by the sense of a
word we mean its place in a system of relationships
which it contracts with other words in the
vocabulary” (also see May, 2006). This way of
thinking, in turn, means that since the relationships
are between the vocabulary-items, there are no
presuppositions about the existence of objects or
properties outside the vocabulary of the language.
This makes it possible to discuss abstract things to the
extent of the coverage of the vocabulary. The
vocabulary is, however, changing all the time and, as
an artifact, it is totally dependent on (1) individual
cognitive processes, and (2) communicative
processes between individuals (see Katzafanas,
2005).
So far, we have discussed the meanings/senses of
single signs or symbols, which in an ordinary
language are written or uttered words. Pragmatics is
a branch of semiotics that deals with the relation of
signs to interpreters (Levinson, 1983). Its emphasis is
on language usage (Levinson, 1983, p. 5). Utterances
and speaker’s intentions are important notions in
pragmatics. Communication is seen as “a complex
kind of intention that is achieved and satisfied just by
being recognized” (Levinson 1983, p. 16).
Pragmatics goes further from knowing the
meanings of words. In doing so, pragmatics can be
seen as complement to semantics as a theory of
meaning. As Lehrer (1980, p. 6) puts it, “[T]he
meaning of the words is one thing and their use
another.” Furthermore, ”understanding an utterance
involves the making of inferences that will connect
what is said to what is mutually assumed or what has
been said before.” (Levinson 1983, p. 21)
Based on the views of Wittgenstein and Dewey,
Medina (2004) state, that “the meaning of words is
not whatever is agreed upon their users”, and, “[t]he
relation between meaning and agreement is more
indirect: agreement is the background condition for
the emergence of meaning.” In other words, while
agreement cannot change the facts around us, they are
a necessary means to human interpretations on the
facts. This idea is very important when trying to
bridge the conceptual gap discussed before.
Higginbotham (1998) distinguishes three levels
of meaning (of a word):
(1) merely possessing a word, or having it in
one’s repertoire, and so being able to use it with
its meaning,
(2) knowing the meaning of the word, and
(3) having an adequate conscious view of its
meaning.
In terms of our thinking, all these levels consider the
meaning at the individual level.
Higginbotham (1998) talks conceptual
competence, which can be seen as a result of a gradual
process in which the grasp of a concept one already
possesses can be perfected. Thinking in this way
makes it necessary to distinguish the concept itself
and the conception of the things that fall under the
concept. According to Martí (1998) the significance
of this distinction may be very radical, if it is
interpreted that there are “concepts which are such
that no one possessing them will ever be in position
to obtain an adequate conception of the kind of things
the concept applies to”.
Since concepts are essential to, and characteristic
of, communication and since they live in
communications, they have to be considered as being
partly outside individuals (e.g. Higginbotham, 1998).
Instead, as the meaning of an utterance is at least
slightly different to the sender and the receiver of a
message (see Levinson, 1983), it is reasonable to say
that both communicators have their own conceptions
of the concept. Concept are, thus, communicative
entities that are tied to language on one side (sense
carrying words defined by theirs relations in
vocabularies) and to mental states of individuals
(conceptions of concepts).
In the following sub-chapter we provide a brief
introduction to possible worlds semantics that could
help in building the bridge between informal and
(semi-)formal descriptions of a domain.
4.2 Possible Worlds Semantics
Possible worlds is an interesting notion that can be
used to describe different kinds of imaginary systems.
A possible world is a world with internal consistency
that makes it possible in logical sense (see Girle,
2014). Our reality, the physical world around us, is
one of the possible worlds, since it exists and is,
therefore, most obviously possible. According to
Hintikka (1982, p. 87) the basic idea of the notion
‘possible worlds’ is that it covers “everything” that is
possible.
Let us symbolize a real world domain, which is to
be modeled conceptually, by D, and the conceptual
model of D by M. Taken that D is one of the possible
worlds, in order to build a good model of D, we
should set the same requirement for M, too. In other
words, when M is logically consistent (a possible
world), it is possible that it is an exact model of D. If
M was not possible, how could it be a candidate for a
good model of D?
Adding ‘Sense’ to Conceptual Modeling: An Interdisciplinary Approach
251
Hintikka (1982) interprets that Frege’s ‘sense’
(Sinn) is a function the value of which is the
reference. He also argues (p. 86-87) that the
arguments of such functions of meaning are the
possible worlds. Concepts (their senses) are, thus,
functions from possible worlds to references (objects
the concepts refer to).
Since the world of individual conceptions may not
form a possible world, the world of concepts for a
group of individuals would remain more or less
ambiguous. However, the conceptions can be refined
to be more consistent, leading to more consistent use
of the corresponding concepts. This would make it
possible to have strict definitions for the concepts of
a domain.
If the meaning/sense of a particular lingual
element (concept/conception) is dependent on
individual interpretation, how much formality can be
applied, when trying to catch the meaning/sense?
Taken that so-called intentional concepts are
functions whose arguments the possible worlds are
(Hintikka, 1982, p. 20), an individual conception, in
fact, can be seen as a function that have “conceptual
state of things” as its arguments. In the first case, the
function returns the reference (the object in the
possible world referred to by the intentional concept)
as its value, whereas in the second case, the function
returns the concept as its value (the concept in a
conceptual network that is referred to by the
conception). In different conceptual states of things,
this function returns a different value.
When the conceptual state of things (i.e. how
conceptions are related to each other) is inconsistent,
it cannot represent reality that is consistent (possible)
by its nature. Therefore, the first step should be to
check and ensure that the individual worlds (of
modelers modeling the domain) are consistent. In
following, we shall provide a model dealing with the
issue.
5 A SIMPLE
CONCEPTUALIZATION
MODEL
According to Katzafanas (2005, p. 6) the shift from
an unconscious state to a conscious state is the
process of conceptualization. In other words, a state
becomes conscious once its content has been
conceptualized.
In the following, we present a simple
conceptualization model which consists of three
worlds: (1) the world of real entities which the
concepts refer to (in ordinary speaking: reality), (2)
the world of concepts that we call the interpersonal
world, and (3) the world of conceptions that we call
individual world.
To go further we make three basic assumptions:
Assumption 1: There is an objective world W
independent of the observer. This is the world of real
entities.
Assumption 2: The observer (individual) can gain
information about W directly through his/her senses
(perceptions) or indirectly though communication
with other people.
2.1. Information about W is always limited to
and/or biased by perceptual and cognitive abilities.
Therefore, part of information (theoretically)
available is lost during observation.
2.2. Concepts (words and their senses for capturing
perceived or abstract things), which are needed for
communication, are interpreted by individuals and
they relate to individual conceptions. When an
individual conception is “externalized” through
communication, part of its meaning remains
hidden. Similarly, when a concept is “internalized”
by an individual, part of its sense may not be
conceived by the individual.
Assumption 3: In interaction with each other, all the
three worlds change continuously.
On the basis of the above assumptions, we present
a simple model (Figure 1). There are three worlds in
the model: The Objective World, The Interpersonal
World and The Individual World. In terms of Frege’s
meaning concepts these worlds correspond to
reference (Bedeutung), sense (Sinn), and
idea/internal image (Vorstellung).
The interacting yet separate three worlds consist
of different things. The Objective World consists of
real entities. The Interpersonal World consists of
concepts. By concepts we do not mean only words
and their senses but also, and particularly, all complex
structures that are built upon them: ontologies,
taxonomies, vocabularies etc. And finally, the
Individual World consists of perceptions, conceptions
and tacit knowledge. Although tacit knowledge is out
of our scope, we have included it in our model to
remind that besides observations about the Objective
World (i.e. perceptions) and conceptions, there are
also third kinds of cognitive things of human mind.
They, on one hand, carry more complicated
information than perceptions do, but, as contrast to
conceptions, they remain unconscious to an
individual. For example, when an individual can
accomplish a complex procedure but cannot explain
KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development
252
(conceptualize) how she has done it, we talk about
tacit knowledge (e.g. see Nonaka et al., 1996).
Communications
Tacit knowledge
Concepts
Individual World
Interpersonal
World
Objective World
Conceptions
Perceptions
Figure 1: A conceptualization model.
Typically, conceptual modeling has dealt with
elements that fall into the categories of concepts and
their references. They are things that are named, often
rather well-defined, and purified from conflicting
material. Excluding a few exceptions, conceptual
modeling seems to ignore the fuzziest area of
modeling, human conceptions.
We argue that what needs to be done in
conceptual modeling is to build a bridge between
conceptions and concepts. An easy way to build such
a bridge is to combine informal elements and formal
elements in one picture. A good starting point could
be typical ER modeling. Modifications that need to be
done include:
1. The concepts must not be considered as symbols
for real world entities but rather as carriers of senses
that are linked to the individual conceptions (meaning
of concept to an individual). E (entity) in ER
modeling is replaced by C (concept) resulting in
Concept Relationship modeling (CR modeling).
2. According to (1), we are not interested in the
properties of real entities (i.e. attributes attached to
entities) at this stage but rather in properties of the
conceptions. We call these attributes conceptual
attribute.
3. Attributes attached to concepts need to clarify
individual conceptions on the concepts being
discussed. The most important one of such attributes
is definition (for expressing personal definition for a
concept, i.e. how the concept would relate to other
concepts in the individual’s mind).
4. Relationships between concepts are not those
that are observed, but those that are expressed by an
individual modeler (through the definitions of
concepts). In other words, a concept is related to
another concept, if someone says so. This is what
personal conceptions are all about. Inconsistencies
are identified and removed later.
A CR model combines two levels of formality in
one picture. On one hand, there are normal language
expressions (conceptual attributes), and on the other
hand, there are graphical expressions that follow a
certain formality (concepts and relationships). In the
next chapter, we present a modest example how a
modeling effort could proceed.
6 MODELLING
CONCEPTS/CONCEPTIONS IN
PRACTICE
Traditionally, there have been three phases in concept
modeling of a domain: (1) to find entities (concepts)
that cover the domain, (2) to analyze the relationships
between the entities, and (3) to add attributes to the
entities. This works fine when one aims at a model for
implementation. However, the procedure may not
work very well when trying to form a consistent
picture of the concepts that individuals use to describe
a domain (conceptions and their relationships). We
believe that the best way is to talk about the individual
views and conceptions. This, in turn, requires that we
can use natural language and that the natural language
is smoothly tied to a more formal representation like
a graphical model. In this part, we shall describe how
traditional ER modeling can be enhanced by a new
modeling level.
Conceptual models, like ER models, refer to the
real world domain, which they represent (Figure 2).
Thus, the concept CAR in an ER model refers to the
class of cars in the real world and the concept
PERSON refers to the class of persons in the real
world. The entities relate to each other through
relationships. Furthermore, attributes are attached to
entities. When we talk about attributes attached to
CAR, we actually talk about the properties that are
shared by the entities of the class of cars. Some key
attributes may explicitly identify an entity within the
class of similar entities.
But, what if we are primarily interested in the
consistency of the domain model? Who says that the
concepts CAR and PERSON are relevant concepts is
that domain? And further, if they are, how can we be
sure that everyone modeling the domain understand
the concepts CAR and PERSON in a similar way?
Adding ‘Sense’ to Conceptual Modeling: An Interdisciplinary Approach
253
Figure 2: A typical ER model.
In this simple case, it is obvious that no
misunderstandings will happen, but what if the
domain was a complex one, like an enterprise
architecture, or a model of climate change? While
such a modeling task would require several models
produced from different viewpoints, how can we be
sure they form a model, which would be both
covering, non-redundant and consistent at the same
time?
In Figure 3 we describe, how “CR” modeling
differs from ER modeling. When modeling a real
world domain, a modeler uses the concepts just like
in ER modeling. However, she does not define the
concept through the attributes of the entities referred
by the concept, but through the definition in which the
concept relates to other concepts. Each modeler has
finally her/his own view of the relevant concepts of
the domain. Before the individual models are
integrated into a common model (external
consistency), their internal consistency must be
checked by the rules of logics (e.g. possible world
logics). If the world of individual conceptions form a
possible world, it is a candidate for representing the
real world domain being modelled. It should be
reminded, however, that a consistent individual world
in not necessary a good model of the domain. This
must be evaluated in the later phases of conceptual
modeling.
Figure 3: A simple example of CR modeling.
7 CONCLUSIONS
In this article, we have provided a conceptual
modeling approach that puts emphasis on individual
conceptions and how they relate to interpersonal
concepts. The presented approach differs from the
prevailing conceptual modeling in a remarkable way,
since it sees concepts not only as abstractions of real
world entities, i.e. symbols that refer to the entities of
the domain being modelled, but also as references of
individual conceptions. Thus, the new approach
builds a bridge between individual thinking and
formal models of a domain, and helps to get the
models more consistent.
A main part of the article consisted of presenting
theoretical constituents of several disciplines relevant
to conceptual modeling. Based on these constituents,
we have built a conceptualization model, and
presented a modest application of it. We acknowledge
that the work is just at the beginning and that the
usefulness of the model and of the CR modeling is
still very much on a theoretical basis. Hence, a lot of
further work is required to show how the approach
works in practice.
Nevertheless, we are quite sure that the new
solutions for bridging the conceptual gap between
human thinking and formal modeling should be
searched for in the direction we have described in this
article. It is obvious that since the applications of
artificial intelligence improve as rapidly as they do
today, the “conceptual attributes”, especially the
definitions of concepts, can be quite easily analyzed
automatically. If the individual conceptions of
concepts could be presented in an ordinary language,
and their consistency checked automatically, utilizing
possible world semantics, for example, we would
have taken a big step towards higher quality
conceptual modeling.
REFERENCES
Almog J. (2005) Is a Unified Description of Language-and-
Thought Possible? The Journal of Philosophy, pp. 493-
531.
Auramäki E., Lehtinen E., Lyytinen K. (1988) A Speech-
Act-Based Office Modeling Approach, ACM
Transactions on Office Information Systems, Vol. 6, No.
2, pp. 126-152.
Brand R. (2004) Making Sense Speaking Nonsense, The
Philosophical Forum, Vol. XXXV, No. 3, pp. 311-339.
Checkland P. (1981) Systems thinking, systems practice,
Chichester: Wiley.
Chen P., Thalheim B., Wong L. (1999) Future Directions of
Conceptual Modeling, In: Selected Papers from
KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development
254
Symposium on Conceptual Modeling, Current Issues
and Future Directions, Lecture Notes in Computer
Science Vol. 1565, London: Springer-Verlag.
Clarke R., Burton-Jones A. & Weber R. (2016). On the
ontological quality and logical quality of conceptual-
modeling grammars: The need for a dual perspective.
Information Systems Research 27(2), pp. 365-382.
Copeland J. B. (2002) The Genesis of Possible worlds
semantics, Journal of Philosophical Logic, 31, pp. 99-
137.
Eastebrook S. (1991) Handling Conflict Between Domain
Descriptions With Computer-Supported Negotiation,
Knowledge Acquisition, Vol. 3, pp. 255-289.
Falkenberg E., Hesse W., Lindgreen P., Nilsson B., Oei J
L. H., Rolland C., Stamper R., van Asche F., Verrijn-
Stuart A., Voss K. (1998) A Framework of Information
Systems Concepts, The FRISCO Report (Web edition),
IFIP.
Frege G. (1892) Uber Sinn und Bedeutung, Zeitschrift fur
Philosophie un philosophische Kritik, NF 100, S. 25-50
(www.gavagai.de/HHP31.htm), English translation can
be found in http://wikisource.org/wiki/On _Sense_and_
Reference
Girle R. (2014). Possible worlds. Routledge.
Halttunen V., Lehtinen A. & Nykänen ´R. (2006) Building
a Conceptual Skeleton for Enterprise Architecture
Specifications, Information Modelling and Knowledge
Bases XVII, I. Kiyoki et al. (Eds.), IOS Press. pp. 219-
236.
Hanseth O., Monteiro E. (1994) Modeling and the
representation of reality: some implications of
philosophy on practical systems development,
Scandinavian Journal of Information Systems, 6(1), pp.
25-46.
Higginbotham J. (1998) Conceptual Competence,
Philosophical Issues, 9, Concepts. pp. 149-162.
Hintikka J. (1982) Kieli ja mieli: Katsauksia kielifilosofiaan
ja merkityksen teoriaan, Otava, Helsinki 1982.
(Language and Meaning. Surveys of the Philosophy of
Language and the Theory of Meaning.)
Hirschheim R., Klein H., Lyytinen K. (1995) Information
Systems Development and Data Modeling –
Conceptual and Philosophical Foundations,
Cambridge, UK: Cambridge University Press.
Hudson R. (1984) Introduction to linguistics, Re-printed by
Basil Blackwell Ltd, Oxford.
Katzafanas P. (2005) Nietzsche’s Theory of Mind:
Consciousness and Conceptualization, European
Journal of Philosophy, 13:1, pp. 1-31.
Lehrer K., (1980) Meaning in Philosophy, In: Theory of
Meaning, Prentice-Hall, New Jersey.
Levinson S.C. (1983) Pragmatics, London: Cambridge
University Press.
Lyons J. (1971) Introduction to theoretical linguistics,
Repr., London: Cambridge University Press.
Lyytinen K. (1985) Implications of theories of language for
information systems, MIS Quarterly, Vol. 9, No.1, pp.
61-74.
Macià J. (1998) On concepts and conceptions, Philosohical
Issues, 9, Concepts, pp. 175-185.
Martí G, The Significance of the Distinction between
Concept Mastery and Concept Possession,
Philosophical Issues, 9, Concepts, pp. 163-167.
May R. (2006) The Invariance of Sense, The Journal of
Philosophy, Vol. CIII, No. 3, pp. 111-144.
Medina J. (2004) In Defense of Pragmatic Contextualism:
Wittgenstein and Dewey on Meaning and Agreement,
The Philosophical Forum, Vol. XXXV, No. 3, pp. 341-
Mumford E. (1983) Designing Human Systems – The
ETHICS Method.
Nonaka l., Takeuchi H. & Umemoto K. (1996). A theory of
organizational knowledge creation. International
Journal of Technology Management 11(7-8), 833-845
Popper K. (1972) Objective Knowledge – An Evolutionary
Approach (Revised edition), New York: Oxford
University Press.
Päivärinta T. (2001) A Genre-Based Approach to
Developing Electronic Document Management in the
Organization, Jyväskylä Studien in Computing No. 11
(PhD Thesis), Jyväskylä.
Siau K., Tan X. (2005) Improving the quality of conceptual
modeling using cognitive mapping techniques, Data &
Knowledge Engineering, 55, pp. 343-365.
Stacks D., Hickson M. III, Hill S. R. Jr., 1991 Introduction
to Communication Theory, Holt, Rinehart and Winston
Inc. ISBN 0-03-033433-0.
Storey, W, Song, I-Y, (2017), Big data technologies and
management: What conceptual modeling can do, Data
& Knowledge Engineering, 108, pp. 50-67.
Studer R., Benjamins V. R. & Fensel D. (1998). Knowledge
engineering: Principles and methods. Data and
Knowledge Engineering 25(1), 161-198.
ter Hofstede A. H. & Proper H. A. (1998). How to formalize
it?: Formalization principles for information system
development methods. Information and Software
Technology 40(10), 519-540.
von Braun H., Hesse W., Andelfinger U., Kittlaus H.-B.,
Scheschonk G. (2000) Conceptions are social
constructs – Towards a solid foundation of the FRISCO
approach, In: Information Systems Concepts – An
Integrated Discipline Emerging, Proceedings of the
ISCO 4 Conference, Kluwer Puplishing Company.
Wand Y., (1996) Ontology as foundation for meta-
modelling and method engineering, Information and
Software Technology 38(4), 281-287..
Wand Y., Storey V. C., Weber R., (1999) An Ontological
Analysis of the Relationship Construct in Conceptual
Modeling, ACM Transactions on Database Systems,
Vol. 24, No. pp. 494-528.
Wand Y., Weber R. (2002) Research Commentary:
Information Systems and Conceptual Modeling – A
Research Agenda, Information Systems Research, Vol.
13, No. 4, pp. 363-376.
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