=Paper=
{{Paper
|id=Vol-294/paper-3
|storemode=property
|title=From Concepts to Design Ontology
|pdfUrl=https://ceur-ws.org/Vol-294/paper03.pdf
|volume=Vol-294
|authors=Pertti Saariluoma and Kalevi Nevela
|dblpUrl=https://dblp.org/rec/conf/semweb/SaariluomaN07
}}
==From Concepts to Design Ontology==
https://ceur-ws.org/Vol-294/paper03.pdf
From Concepts to Design Ontologies
Pertti Saariluoma and Kalevi Nevala
Cognitive Science, University of Jyväskylä, Finland
Abstract. The research on engineering knowledge systems is continually
evolving. Knowledge is conveyed through the thought processes of engineers.
In order to provide adequate support for engineering design the thought
processes must be understood. The aim of this paper is to discuss how to
transform conceptual knowledge to design ontologies. We suggest a procedure
termed Cartesian reasoning to explicate some intuitive steps in engineering
thinking and to put them under scrutiny.
Keywords: Conceptual contents, Cartesian reasoning, content-based analysis,
design engineering thinking, engineering ontologies.
1 Introduction
Engineering ontologies are knowledge systems referring to objects, actions and events
relevant in engineering. There are many other ways of defining these knowledge
systems, but the above seems to capture the essence of the ways in which the problem
has been defined elsewhere [1], [2]. The main property of ontologies is that they give
a description about the contents of their references [3].
Ontologies are vital because they provide both means for systematizing and
transferring organizational information [4] and tools for collaborative design and the
use of everyday knowledge in organizations [5], [6], [7]. Moreover, they improve
knowledge reuse and enable us, to some degree, apply algorithmic problem solving in
design processes [8]. Finally, they can be seen as theories of contents [3]. This means
that research on ontologies shall have vital role in future engineering design thinking.
So far, the research into ontologies has mainly concentrated on the form of actual
knowledge systems, and much less attention has been paid to the dynamics of the
thought processes creating them. Content-based design analysis is specifically
targeted when investigating thought processes in design [9], [10], [11]. Consequently,
it is natural in its context to ask about the nature of thought processes relevant in
building new ontologies. Content-based design analysis assumes that thought
processes are based on transforming the information contents of mental
representations. Consequently, understanding design as thinking, we have to be able
to explicate the way information contents behave when designers think. Succeeding in
this would make us better capable in controlling engineering design. We would be
able to answer questions such as how to organize design knowledge, how to organize
design groups, what happens in creative thinking and how organizations could be
evaluated according to their knowledge management processes. This means that we
would be able to manage the information and data, as well as human processes.
For a content-based ontology research a natural stating point is to investigate the
thought processes emerging from ontology design. Concepts form perhaps the most
fundamental knowledge element in ontologies. Here we analyse the tacit aspects of
concept processing in designing ontologies as well as in engineering design in
general. Our focus is on how to use knowledge packed in our concepts when building
ontologies. We apply and essentially extend the earlier discussions to a form of
conceptual inference, which can also be called Cartesian reasoning [12], [13], [14].
2 On Knowledge in Concepts
It is not possible to present infinitely long chains of arguments. For this reason
scientific knowledge is eventually intuitive [15], [16], [12]. The idea of axiomatic
system represents perhaps the best-known recognition of the ultimate intuitive
character of scientific knowledge. Even conceptual systems in science have their
intuitive foundations, the contents of which are no longer analyzed but assumed.
These basic theoretical notions could be called conceptual postulates [12]. Typical
examples of such postulates might be ‘system’, ‘function’ or ‘process’. These
concepts are constantly used in building scientific knowledge, but they are seldom
defined or individuated in any detail [12]. Of course, one could easily present
numerous other examples.
In this paper, the notion of ‘concept’ itself is put under scrutiny. The concept of
concept is also a conceptual postulate. It is commonly seen as a classifier in so-called
classic theories of concepts. In these theories the definition and the defining
characteristics are aimed to provide us with a criterion of determining which objects,
events or ideas can be subsumed under a particular concept (cf. [17]). However, in so-
called prototypical theories and even in theory-theory of concepts, the main function
of a concept is also seen to be that of a classifying entity [17], [18]. Finally, the
whole tradition of mathematical theories of concepts from logic to neural networks
and learning machines mainly concentrates on classification. This means that
classification or categorisation provides the dominant intuition in the way we see
concepts [13].
However, we could view concepts from a very different intuitive standpoint, where
they serve functions other than classification. Concepts are used to construct
propositions and representations and could thus be seen as constructors [13]. The
constructor view is not contradictory with the classificatory view, and it opens us new
possibilities in considering the nature of conceptual knowledge.
A crucial difference between the two intuitions, i.e., concepts as classifiers and
concepts as constructors, is in the way we think about their contents. In the
classificatory view, the content of a concept is a set of objects which belong to its
scope. In the constructor view, on the other hand, the contents of a concept are what
they add to the information contents of representations. Thus, the content of
‘possible’, for example, is what it adds to the propositions within which it is
incorporated. 'Possible' considerably modifies the propositions it is inserted into. The
constructor role is made evident in the modification of the sentence ‘the belt cannot
tolerate so high temperatures’ to ‘possibly the belt cannot tolerate so high
temperatures’. 'Possible' adds some content element to the second proposition. The
constructor perspective is important, because it opens us new possibilities when using
conceptual knowledge in investigating representations and inferences.
3 How Concepts are used in Designing Ontologies?
If we put the work with ontologies under a careful scrutiny, it is possible to find in it a
tacit thought operation, which people regularly make, but which they never explicate.
This process somehow connects concepts with their attributes. Chandrasaekaran,
Josephson and Benjamins [3] discuss four very common ontologies. They all use the
same root concept, i.e., thing. CYC gives it three major attributes: individual object,
intangible and represented [6]. Wordnet presents a twofold division into living and
non-living, Sowa [20] differentiates between concrete, process, object and abstract.
Borst and Akkermans [1] present an analysis of a mechanism which firstly
allocates three major attributes: connectivity, effort and domain. After that the major
attributes are given different values. These values are very clearly attributes of the
major attributes. Two-port, for example, is an attribute of connectivity. This way of
creating attributes is obviously the same as the one in the previous example.
Let us look finally into a third example. Spyns, Meersman and Jarrar’s analyse the
structure of book [21]. They give their ontology attributes such as author, title, price,
and product type. If necessary, one could add some other attributes such as size and
weight. Perhaps the last one would be problematic with e-books, though we might be
willing to think that they have weight. Anyone familiar with ontology literature can
see that there is nothing strange in this construction of the attribute system. One could
easily find more examples.
Designing ontologies is a thought process, which enables us to generate the
attributes. How do we come to the conclusion that the concept of thing has an
attribute such as living? How can we deal with the problem of having numerous
different types of attribute systems related to a thing? How can we verify that e-books
have weight? How do we know that the attributes are correct, as the systems of
attributes can be very different even within the same concept? Are these systems of
attributes actually arbitrary? At least they are incommensurable. These questions have
one important implication. When working with ontologies we have to consider the
process of deriving attributes of concepts. Today, this process is conceived all too
intuitively, and therefore we have to explicate it in order to be able to investigate it.
4 Conceptual Inferences
Concepts can operate in some of the human inferential processes, but it is not always
clear what are the main inferential functions that concepts have. On hearing the words
"New York", it is quite possible without any accurate knowledge to generate quite a
lot of information about a place of that name. You may be able to infer that it is on the
coast of the Atlantic Ocean. What precisely is this kind of inference requires some
consideration.
Firstly, we must ask what makes it possible to infer something from a single
concept. Animates, objects, artefacts and events have properties. This means that the
concepts, which represent them or stand for them, must represent these properties.
Otherwise, the concepts could not make distinctions between objects. The
representations of properties of objects are called here the attributes of concepts [12],
[13]. Consequently, one can see concepts as integrated systems of attributes [12].
The attributes of concepts are important, because it provides us with a form of
conceptual reasoning, which can be termed Cartesian or conceptual inference to
honour the famous French philosopher, Rene Descartes, who produced perhaps the
most famous example of this kind of inferential process in his "Cogito" argument. His
claim "I think, therefore I exist" means that he inferred the notion of existence from
the notion of thinking. This inference is possible only if a thinking thing has the
attribute of existence. Without taking any position with respect to the correctness of
the Descartes' inference, one can accept his schema as a general mode of inference for
inferring conceptual attributes from a concept.
Cartesian inference can be explicated by the following abstract schema:
(1) C(A1 ,...,An) A1 |, ....,| An
In this formula C is a concept and A1… An represent its attributes and the sign ‘|’
corresponds to ‘or’. Hence, the schema (1) should be read: if the concept C has
attributes A1,.… ,An then any or all of the attributes A1,.… ,An can be inferred from it.
One can also use another schema:
(2) C An
This schema naturally contains the same information as that in (1), which is that from
a concept one can infer any of its attributes.
Cartesian inference has been relatively little discussed apart from historical
contexts, however, it is surprisingly common in ordinary thinking. If I say that Joan is
a grandmother, you know immediately that she is a female most probably in her late
middle age or older. Joan cannot be a baby or a teenager, for example. From the
notion of a paper machine, one can infer its motors or from the notion of a roll its
radius etc. These kinds of inferential examples are easy to generate. Cartesian
inference is thus an important inferential tool when the contents of concepts are
considered.
There are several forms of inferences, which are close to the Cartesian inference.
Typical examples are entailment or lexical inferences, psychological, case-based
reasoning and meaning postulates [17]. Here our individuating of Cartesian reasoning
is quite logical, because the differences between it and other forms are clear. We do
not infer only a priori properties with Cartesian inference as we do when using
meaning postulates. Meaning postulates follow necessarily from a concept without
any empirical analysis, but this is not true with all attributes of concepts. Some of the
attributes of concepts are logically contingent, and therefore, they are not meaning
postulates. The same criterion differentiates Cartesian inference also from inferential
schemes of a psychological type [17]. The explication of necessary attributes is thus
only a special case of the general Cartesian analysis of conceptual contents. In
Cartesian inference all types of attributes can be inferred from a concept. No
difference is made between a priori and empirical attributes. Any attribute of a
concept can be inferred when defined as above. Cartesian reasoning is also different
from case-based reasoning, because instead of relying on previous cases, it relies on
conceptual contents (cf. [22]).
The next problem in developing Cartesian inference is its validity. One must
determine under which conditions it is valid and what is the criterion for its
correctness. Its validity is difficult to determine, because attributes, which are
inferred, can be empirical.
1) A paper machine has dryers of paper web.
2) A paper machine has brains.
The two examples make the point clear. An attribute can validly be inferred from a
concept if and only if it really is an attribute of a concept. This means that it is
empirically true that the referenced object has the respective property. Inferential
processes are thus objective in the empirical sense and there is no room for
subjectivity. The first proposition is true and the second one is false. Paper machines
have brains only in a metaphorical sense, and therefore it is incorrect to infer that they
have them. Empirical validation guarantees thus the objectivity of Cartesian
inferences.
Empirical validation and the content-based structure of Cartesian inference make it
different from formal ontologies. Content-based analysis of ontologies has different
goals from formal investigations. Instead of producing formal grammars for
ontologies (cf [23]). Cartesian reasoning is relevant in content-level analysis and in
the justification of ontological structures.
5 Analyzing Concepts
One important application of Cartesian reasoning is the analysis of the contents of
concepts. By means of Cartesian inference, we generate the attributes of a concept
and can subsequently investigate their content. In this way, the attributes may be
explicated and their properties investigated. The goal of conceptual analysis is to
determine the correct use and interpretations, conceptual relations, and structures of
concepts.
The analysis of concepts is vital, because the information contents in human
mental representations are dependent on their conceptual contents. Concepts are the
basic construction elements of any mental representation. These representations can
entail only what is expressed by the means of the concepts. For several reasons, we
need to be able to analyze the contents of concepts to understand the information
contents of the representations.
In analyzing concepts in representations such as design discourses or plans, it is
important that we firstly differentiate between three analytical aspects of concepts,
because we need them in clarifying the conceptual analysis. These aspects are total
contents, definition, and use of a concept. If we generate all the attributes of a
concept, i.e., all the known properties of its reference, we speak about total contents.
Of course, this is in practice a kind of a theoretical value, which does have practical
significance only in very few cases, since references have an unlimited number of
properties. For example, it would be rather futile to explicate all the spatio-temporal
locations of all the electrons in an object. However, having an unlimited number of
attributes does not mean that objects could have any kinds of attributes. Paper
machines, for example, are not living beings.
The second aspect is the definition of a concept. Very often we need to get clarity
to concepts we use, and then we generate some characteristic attributes of a concept
arguing that it defines the concept. For example, one definition of a human being is
‘homo sapiens’ ("intelligent/wise human"). However, we should not rely too blindly
on definitions, because they are seldom absolute but relative to some use contexts
[23]. In geometry, we can say that circle is round, but in practical engineering, the
notion of round is much less clear.
The elusive nature of definitions makes it necessary to consider also the uses of
concepts, in an attempt to find out what are the actually relevant attributes of a
concept in a particular representation. The ‘use of concepts’ is an important notion,
because it sheds light on the fact that apparently similar content elements in various
representations may have very different actual contents. The meaning of words, for
example, varies from one context to another [23]. The notion of use expresses how we
abstract different aspects of concepts in different contexts. The speed of a car may
play an important role in many occasions, but it has no relevance when we need to
ship the vehicle in question. Then such attributes as weight or size are more relevant
[12], [13], [14].
The analysis of concepts is important, when we need to get clarity to the ways we
think. Only a precise understanding of the contents of thoughts can make our thinking
explicit to us [23]. This is why conceptual analysis is needed in many practical
situations, for example in design processes and design thinking.
6 Cartesian Reasoning and Design Engineering Ontologies
A way towards accurate thinking is to explicate our thoughts in an ontological form.
This method enables us to socially analyze the structures and assumptions of our
thoughts. In its most basic sense, ontology means a theory of being qua being, as
Aristotle in his Metaphysics formulated this field of research. In a narrower sense, it
has become to mean an aspect of data modeling in information systems development.
In investigating design processes, this ontological stance is important both in the
general sense of investigating the function and structure of being as well as in the
sense of developing data analysis for information systems [25], [3]. From our point of
view, the ontological stance is important because it allows a clarification of the design
thinking processes. When we explicitly know the structure of our conceptual system,
the foundational assumptions which we make about reality and about the appropriate
ways to present it, make it much easier to guide and control innovative design
processes.
The connection between ontological research and Cartesian reasoning is very
natural. Our concepts reflect the way we are able to represent a being and its aspects
and subparts. This is why conceptual analysis based on Cartesian reasoning offers us
the means to investigate conceptual knowledge for the development of design
ontologies [14].
The idea is to define the core ontological concepts and to generate their attribute
structures. It is possible to explicate conceptual structures by iteration; the analytical
process proceeds from the core concept to their attributes and from the generated
attributes to their attributes until terminal attributes have been explicated. These
structures are naturally expressions of ontologies for a particular domain of design.
An example of the procedure can be found in Saariluoma and Maarttola, 2005 [14]. In
it, the design process of a family house is described by iteratively applying Cartesian
reasoning. In this paper, we have chosen the machine engineering domain to further
develop this way of building ontologies and to demonstrate the function of Cartesian
reasoning when working with design ontologies.
7 Extended Nip Press: a Test Example
Although the basic framework of Cartesian reasoning is quite clear, it cannot be
applied in generating engineering ontologies automatically. It is necessary to test how
it works and how it should be used in analysing engineering design. Therefore, we
decided to take a reasonably advanced product of design engineering in order to
investigate how we can apply the principles of Cartesian reasoning in generating a
conceptual ontology for the object. This object is the extended nip press (ENP), which
is one of the crucial breakthrough innovations in paper technology during the last two
decades.
ENP provides a wider contact zone than the earlier conventional press nip between
two rolls and consequently a longer press impulse on the fast running paper. The
lower roll has a flexible mantle, which is pressed by the upper roll against a contoured
“press shoe” inside the lower roll.
Figure 1 Schematic illustration of an extended nip press, ENP (Valmet SymBelt Press,
1995). (1) Hard counter roll, (2), (4) dewatering felts, (3) paper web (5) flexible
mantle, (6) press shoe, (7) supporting beam inside the flexible roll, (8) loading
cylinders and lubrication hydraulics. Reprinted by the permission of Metso
Paper Inc. and the inventors; edited by Kalevi Nevala.
One practical realization of ENP is SymPress B (Figure 2).
1984: SymPress II 2004: SymPress B
Figure 2 An example of twenty years development of the press section; a renovation of
press section for better efficiency by utilizing ENP (SymBelt Press). Source
Metso Paper Inc., edited by Kalevi Nevala.
The idea of an extended press zone in the dewatering presses of board and paper
making machines is old, and actually a very natural proposal in order to increase the
press impulse for a better water removal.
However, there have been many obstacles in the way of utilizing the idea. The
problems have been mainly due to facts and beliefs of techno-economical kind. First
of all, up until the end of the 1970s the technology was lacking for reliable means for
flexible support of the wet paper web through the extended nip zone. Secondly, an
exhaustive “patent jungle” was believed to prevent the industrial use of the general
idea of extending the press zone. The culmination point in the history of ENP was the
delivery of the first production scale open belt “shoe press” for a board making
machine in Springfield, USA 1981 by Beloit Corporation (USA). This breakthrough
alerted other paper and board machine producers. This is also the starting point of our
inquiry, which concerns the development of press section at Valmet/ Metso Paper Inc.
1983 – 2003. The following chapter describes one outcome of this extensive research
project.
8 Conceptual Ontology of a Machine
An extended nip press is a part of a paper machine. However, as is the case with all
sophisticated machines, also ENP is an assembly of assemblies. Therefore, we
decided to develop the basic ontology for the extended nip press beginning from the
concept of machine and not from the concept of a machine part or element.
Cartesian reasoning is useful in developing conceptual ontologies. It is a tool for
explicating conceptual structures in our minds and making our thoughts more explicit
to ourselves. The first step in the procedure is to find the major attributes of the basic
concept. Here, this means the basic attributes of a machine.
An attribute must correspond with some property of the entity, i.e., event, person,
object or artefact. This attribute must be true, which means that the reference really
has the property. Thus, an “oven” has a colour but an “idea” does not. In searching for
a good ontology for machines, we have to find such general attributes of machines
that are true with most of them, if not with all.
An attribute is a basic or a first level attribute if it cannot be reduced, i.e., inferred
from, the other basic level attributes. Here, we have generated four basic level
attributes for any machine: need, function, structure and operation. By need, we refer
to the human dimension of the requirement for a machine. All machines are made for
serving some human need. Of course, human need itself can be further analysed into
attributes. Here we generated, for example, intention, i.e., corporate goals, demands,
i.e. the expectations of the markets, and usability, which entails, among other things,
process efficiency and operational efficiency.
Table 1. Ontology with concrete attributes
Machine ontology Examples from ENP in paper machines
1. Need
1.1 Intention Higher production rate for paper machines
1.2 Demand Reduction of cost per produced paper ton
1.3 Usability Easy and efficient utilization of the process
1.3.1 Process efficiency Running time of ENP must be maximized
1.3.2 Operational efficiency User-friendly control and driving systems
2. Function
2.1 Use Applies adjustable press load on paper web
2.2 Role Second stage of water removal from paper web
3. Structure
3.1 Materials Steel, aluminium, reinforced polyurethane, fabrics
3.2 Forms Supporting enclosed beam or I-beam inside the roll,
functionally contoured “press shoe”-beam against the
flexible mantle
3.3 Energy
3.3.1 Source Electric drives, pumps, compressors
3.3.2 Transmission Gears, hydraulic tubes, pneumatic hoses, friction
between flexible mantle and a hard roll
3.4 Information
3.4.1 Control Human controller
3.4.2 Steering Automation
4. Operation
4.1 Machine processes
4.1.1 Load conditions
- Static Ten million N pressing force
- Dynamics Vibrations of massive fast rolling machine elements
- Kinematics Dragging flexible roll-mantle driven by a hard roll
4.1.2 Strength Support beam of steel, reinforced flexible mantle
4.1.3 Loss of energy Friction and deformations between the press shoe, the
press fabrics and the dragging flexible roll mantle
4.1.4 Wear Life-cycle of the flexible mantle
4.2 Product process Dewatering the paper web by pressing
By function, we refer to the traditional functional analysis of a machine. This main
attribute refers to why a machine has been designed and built, what it produces, and
what is its goal. This knowledge is expressed in the concept of use. A machine has
always a role in its contextual environment. The extended nip press, for example, is
designed to improve water removal in a paper machine. This is its role.
The third main attribute is structure. This attribute refers to numerous structural
properties of a machine, including its materials, forms, energy, and information
aspects. Materials entail what the machine is built of, and forms how the whole and
the components are designed. Energy is the attribute of all power supplying aspects of
the machine. Finally, information explicates the steering and control attributes.
The last of the four main attributes is operation. This refers to those aspects of a
machine which emerge when it is used. This means, on the one hand, machine
processes and, on the other, product processes. Machine processes refer here to
various load conditions, i.e., static, dynamics and kinematics in use. Strength
attributes tell us how the machine stands the load conditions. Product process
explicates everything that happens in the actual use of the product. In the extended nip
press, the question is about removing water from the paper web.
Ontology for a machine is presented in Table 1. Examples of the concrete
properties of the extended nip press are also incorporated to the ontology.
The presented ontology is not meant to be complete and exhaustive, because that
would be outside the scope of this paper, which deals with the role of Cartesian
reasoning in generating conceptual ontologies for engineering purposes. However, the
ontology should give some insight about the way Cartesian reasoning can be used in
making ontologies.
9 Criteria for Attributes
Ontologies are systems of concepts. For the sake of conceptual clarity it is important
that the generated attributes are independent. This means that we cannot infer the
attributes of the same conceptual level from each other. They offer thus different
points of view to the reference. In our example, human need naturally cannot be
reduced to the machine attributes nor can function or structure be reduced to that
need. All the attributes are mutually independent.
However, this does not mean that they would not refer to the same reference. They
are all attributes of machines and for this reason they can describe or represent many
exemplars. Our ontology is not valid only for the extended nip press but it can be
applied to a bicycle or a car as well. The power of ontological work is in the content-
based abstract generality, which enables people to transfer general level knowledge
from one exemplar to another and from one person to another.
Because attributes are themselves concepts, Cartesian reasoning can be used
iteratively. In this way, it is possible to build consistent conceptual systems for
various purposes. One may model, for example, design processes, design products or
organizations [14]. This helps in getting clarity for conceptual systems used in
engineering.
Although the attributes in ontologies are independent, they provide, as a whole, a
holistic representation of the reference. The attributes may be contradictory but
complementary. Each of them provides us with one perspective to the reference and
the system as a whole entails all its important properties. Each of them opens a
perspective to the reference, but they operate as a whole.
A couple of concrete examples may clarify how ontologies serve holistic thinking.
Intention calls our attention to the interest of the industry, its owners, and corporations
and demand reminds us that markets have their own interest. Need may have an
important role to play in structural solutions. Markets may want to reduce costs, and
consequently, engineers must look for new technical solutions. They may have to
reduce the power consumption. However, having interconnections between the
various aspects of a reference expressed in different attributes does not mean that the
attributes are dependent on each other.
10 Conclusions
The significance of knowledge management and ontologies is rising in the world of
engineering. There are many obvious reasons for this development. It is necessary to
decrease the design time, because in current competitive situations there is the need to
make the investments pay back swiftly. The complexity of designs is often also
increasing, because it is one of the very few ways to improve performance and satisfy
the needs of the markets.
In the current situation, it is obligatory for the designers to get a better
understanding of the design process. Ultimately, this means that they have to have a
better understanding of their own thinking, human thinking still being the main
enigma in all design. Building ontologies is one way of explicating the structure of
designers’thinking. In this way they can get a clearer view to their own work.
Ontology is like a grammar. We unavoidably follow some principles in our design,
but we do not necessarily understand them any better than a non-linguist understands
the regularities of grammar in our speech. A grammar explicates the underlying
regularities of speech and makes it possible to consider them in socially shared
thinking. Similarly, design ontologies aim to open up what happens in designers’
minds to enable them to improve their practices.
A problem in generating ontologies is that the methodologies so far have been very
intuitive. Only few aspects of this highly important form of thinking have been
explicated so far. The Cartesian model opens us a way of analysing conceptual
structures involved in designers’ thinking. Developing methodology for generating
conceptual ontologies or for conceptual engineering is vital.
The importance of knowledge management is constantly increasing. Progress in
artificial intelligence, knowledge management, information systems development,
design analysis and Semantic Web make it necessary for us to be able to effectively
analyse knowledge in concepts [22], [3]. One problem in this work is the lack of
systematic methods. Ontological work is still very intuitive [23]. The need is
recognized among specialists of ontologies.
In this situation, Cartesian reasoning might give some rigour to the way conceptual
knowledge is analyzed and explicated. It enables us to see the most important
conceptual points in the view which guides our thinking. It enables us to look for
conceptual clarity, which is often an important presupposition for accurate thinking
and communication. If we do not understand how other people represent key
concepts, we can hardly follow their thinking either. Most importantly, Cartesian
reasoning as empirically validated process offers a possibility to get rid of the inherent
subjectivity in developing ontologies. Thus, Cartesian reasoning is important in the
search for more efficient design processes.
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