COE NEWSJAPANADVANCED INSTITUTE OFSCIENCE AND TECHNOLOGY
1990

Newsletter of the Center for Strategic Development of Science andTechnology, Japan Advanced Institute of Science and Technology (JAIST)

Vol. 4 No. 1

Spring 2007

Researcher Column 1:

Researcher Column 2:

Intuition and the Emergence of new
Concepts in Science

Constructing Ontology for Exploring
the Triple Helix of Academia-IndustryGovernment

Zbigniew Krol
Institute of Philospphy and Sociology of the Polish
Academiy of Sciences
The new civilization era (global
knowledge civilization) can be described as theperiod where the temporal distance between
scientific and technological macro-changes isreduced and the connections between them are
more direct. It is not because straightforward
common sense possibilities have been exhausted –there still remain many others – but rather as ispointed out in the Creative Space theory, due tothe connection between creative processes with the
many-level structure of the environment forknowledge. It seems, however, that the intuitive
level is also sub-structured, being hierarchicalenvironment in the hermeneutical horizon.
It is not true that macro-changes in science
consist only in the conceptual changes of the basic
scientific terms or in the emergence of newconcepts – many other factors are involved here.
Furthermore, the description of the emergence of
new concepts in science depends on the employed
theory of scientific change. There are manydifferent theories of scientific change and not inevery one of them are concepts the most importantfactor.
The differentiation between the higher
level (or new) concepts and implicit basic level (orold) concepts, connected with the emergence
principle suggests a kind of hierarchical structureof scientific theories, incommensurable and nonreducible to each other. Obviously, there are many
possible theories explaining the emergence of newscientific concepts, but without some kind of test
these theories would be useless.
Continued on page 2

Jie Yan, Tieju Ma, Yoshiteru Nakamori
School of Knowledge Science, Japan Advanced
Institute of Science and Technology
tieju@jaist.ac.jp

Introduction
When academic researchers developing theirresearch roadmaps (or strategic research plans)(see Ma et al. 2006), it is necessary to have a wideview also on industry and government since futuretechnology systems are more and more shaped by
the evolutionary interactions among industry,
government and academia, or what is called, thetriple helix of academic-industry-government (seeEtzkowitz 2002). But researchers sometimes findthey are very much constrained by budget, timeand opportunities to communicate with people
from industry and government, or even fromacademia. A supplement (and maybe a main) way
for many researchers is to get knowledge about the
triple helix from publications, Internet, and otherpublic medias.

Inside this Issue
Page 8:♦ Research Column: Constructing

Ontology for the 21th Century COE
Program Technology Creation Based on
Knowledge Science
Page 12:♦ COE Center News
Page 13:♦ Call for Papers
IJCKS 2007 (KSS2007/KICSS2007)
Continued on page 5

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It is very important to note that the historicalchangeability of the concepts does not support any
kind of a social or historical variabilism or
relativism. The possibility of the creation andunderstanding of mathematics is based on thepossibility of thinking exactly the same sense bydifferent subjects of cognition and the same senseby the same subject of cognition in different times.
The historical changeability of concepts means
only that the grasp of the same content is not
automatic and sometimes one needs to resort toreconstruction. For mathematicians living inalmost the same historical epoch, the possibility tothink in the same way is almost automatic. But thepossibility of understanding ancient mathematics is
not so straightforward and needs the reconstructionof the hermeneutical horizon.

Institution and the Emergence of New… (con’t)
The best known and widely accepted test is ahistorical case study. At first, we need information
on what science really is, and without historicalconsiderations we can speak only of what scienceshould be. Scientific change is not a point-likeevent in space-time, but rather a temporal,
historical process - it is simply impossible to see
the changes in science without seeing this sciencein its historical time-interval – if the interval isshort we can detect only micro-changes.
What does mathematical intuition consist in?
Every act of mathematical intuition e.g. the guessof a proof, informal reasoning, the use of a nonformal meta-environment, the intuitive analysis ofconcepts etc., relies on the implicit recognition of
the existence of something as given and ready-touse, and equally implicit recognition of intuitive
content of that “something”. One can observe thatthe intuitive environment is previously given,
present and has some implicit i.e. presupposedcontent and qualities preceding any kind of
possible construction or decision.

The received view on mathematical platonism
define it as a conviction of the existence of someideal, timeless, eternal and unchangeable
mathematical entities. This kind of platonism isexternal to mathematics because it is not necessary
for the creation of mathematical knowledge.
In contrast to this platonism, we connect
mathematical intuition with the internalmathematical platonism which manifests itself inthe strict methods of mathematical inquiry such as
the use of classical negation, the use of the law of
the excluded middle or the use of informal
mathematical meta-level. Mathematical intuition is
the fundamental mode of the platonism as themethod of mathematical enquiry.

It is possible to reconstruct such actively felt andgrasped qualities because they manifest themselves
as hidden assumptions, tacit knowledge, prejudicesetc. It is because of their implicit character thatthey are intuitive. They absolutely determine every
informal step in the intuitive mathematicalreasoning which means they are active. In the
moments when they “disclose themselves” during
the creation of mathematical knowledge, theworking mathematician feels that something isevident, and sometimes, apodictically evident i.e.
“it can not be any other way”.

Theories of scientific development andmathematics ignoring platonism in micro- andmacro change of science are unrealistic.
Hemeneutics makes it possible to determine and tofind out such unconscious but fundamental
conditions which are quite independent from the“formal” viewpoint sustained by a philosopher.
The platonism as the method of mathematicalinquiry is such a hermeneutical condition for thecreation of mathematical knowledge. The
reconstruction of the hermeneutical horizon is theeffect of the detection and description of suchhermeneutical conditions. New and revolutionary
concepts in science can emerge after the change ofhermeneutical conditions is done. As mostly
unconscious and not explicit, the hermeneuticalconditions are the base for intuition and tacitknowledge.

So, it is possible and reasonable to reconstructwhat is assumed intuitively i.e. as a tacit or ahidden assumption in the informal holes always
present during creation of “all mathematics”, be it
formal or not. Let us call this reconstruction thereconstruction of the hermeneutical horizon.
The reconstruction of the hermeneutical horizon ispossible for different mathematical theories in thesame historical epoch and in different epochs, forexample, the reconstruction of the horizon ofancient mathematics needs purely historicalapparatus i.e. should be based on historical sources.
Sometimes the reconstruction needs some newmathematical methods and theories.

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Spring, 2007sections etc. We have to ask once more: how
is it possible?

To avoid long discussions, we can demonstrate theexistence of the hermeneutical horizon and its
distinctness in antiquity by showing the results of along historical case study. We can even make a“thought experiment” showing the aforementioned
active character of the horizon.

At the starting point we have no infinite space,but only so much “place” as to performpermitted operations. So, “the space” extends
with the constructions performed.

The so called Euclidean (elementary) geometry
with some basic constructions e.g. translations,drawings of circles, triangles, straight lines,
sections etc., and the theorems such as the
Pythagoras’ theorem, are known from elementary
school. It is also possible to read Elements from
where geometry began.

There is some historical evidence also outside
Elements explaining the absence of the notion of
Euclidean space there. For example, Heron from
Alexandria (1st century B. C.), a commentator,
tried to improve some of the proofs so when
Euclid put a point on the other side of a construed
figure, Heron changed the place of that point sothat the figure contained it. It is because “there isno place [outspread] out of the figure”.

The experiment relies on the possibility of reading
the text of the translation of Elements with enoughunderstanding. We can do it ourselves or observe
the understanding of the text by a pupil or even achild. It sometimes happens that pupil can state
many properties (e.g. “the diameter divides a circle
into two equal parts”1) without any proof, or caneven formulate some simple proofs.

Our infinite Euclidean space is “ready-to-use”: allplaces are already present. This space isconsidered from the point of view of eternity. On
the other hand, ancient mathematicians had tochoose only some “parts” of the continuum: they
considered only those parts which they could grasp
by strictly determined methods of construction.
Their continuum was viewed from the point of ahuman being and was temporal.

We can reconstruct the hermeneutical conditions
determining our understanding of Euclidean
geometry and we will see that we create andunderstand the geometry in the determined
intuitive model, which is a “part” of thehermeneutical horizon. In our example, the basis is
the infinite, rigid, unchangeable or in the
Newtonian sense, absolute “Euclidean space”,
treated as a container or an arena for geometry toplay itself out, “the same” in every place andmoment of time. When in Elements one reads theword “line”, “surface” etc., it is understood as“infinite straight line”, “infinite surface” injectedin presupposed infinite space.

The classification of the book X forms a nonEuclidean model of geometry. In this model it ispossible to construct an infinite number of lines(i.e. sections) parallel to a given one and different
from it. We can describe a connection with themodel Λ used in the proof of independence of theaxiom of continuity in Hilbert’s version of
Euclidean geometry; cf. Z. Król Plato and theFoundations of Contemporary Mathematics.
Plato’s Concept of Number, Wydawnictwo
Rolewski, Toruń 2005 (Król 2005).

What are the main differences between
intuitive ancient and modern models for
Euclidean geometry?

We see the possibility of different “intuitivemodels” of understanding Euclid’s geometry
which are not uniquely determined. For more than2000 years, Euclid’s Elements were understood ina non-genuine way within variable intuitive frames.
It does not mean, of course, that the Greeks did nothave the notion of infinite, “vacuous” space orinfinite straight line, cf. historical evidence from
Aristotle’s De generatione et corruptione or
Democritus in (Król 2005). It only means that such
notions were left out from Elements as unclear, notevident and not constructive. The infinite concepts
were present in antiquity but outside the scientific

The main difference is the absence of theconcept of absolute space and general lack of
any infinite notions: infinite surface, infinite
straight line, infinite line, asymptote etc. The
concept of absolute space does not appear in
Elements nor the other infinite notions. Other
differences are non-continuity and the nonmetrical character of geometrical figures,

1 We know that this is the theorem discovered by Thales ofMiletus (VI B.C.).

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frames and as the significant part of scientifictheory they emerge much later.2

Spring, 2007
and his absolute space is the interpretation of theearlier and implicit horizontal change. Newton
studied Elements in depth and wrote many works
concerning some basic geometrical problems in
Euclidean geometry. However, also he explicitly
works within the infinite space and explicitly (aswell as implicitly) assumes that this space creates
the base of Elements not only for him, but also for
Euclid; cf. Newton’s works such as Analysis
Geometrica, Inventio Porismatum, Geometrie
Libri Tres etc. The horizontal change at first canappear only as an intuitive change, it is a kind of
Gestalt.

Now we are ready to see the horizontal
conditions for the emergence of concretescientific concepts. First of all, we detect by
the reconstruction of the hermeneuticalhorizon, the two different intuitive models for
Euclidean geometry. It means that there was atime during which the absolute and infinite
notions such as absolute space were absent
from mathematics and science. However, weknow that absolute space was the fundamental
concept of Newtonian physics and all modern
science till the emergence of the special
Theory of Relativity.

We can see that the rise of modern science basedon mathematics was connected with the horizontaltransition from the ancient constructive models for
Euclidean geometry to the model of absolute space.
It is necessary to change the received views on
development schemes of geometry. The received
view describes geometry at the starting point asalmost the same till the 19th century when nonEuclidean geometry was discovered. Of course,
the received view allows description of many
micro-changes in Euclidean geometry, but ingeneral they all appear in almost the same intuitive
frames. Without the reconstruction (even partial)of the hermeneutical horizon, it is impossible tonotice the change of the intuitive model for
Euclidean geometry.

The emergence of the concept of absolute space
was not the discovery of a single man, Newton or
Descartes. This concept emerged as a result of along historical process. It was connected with thegradual enrichment of geometrical methods,
coming from constructions using a ruler andcompasses (e.g. translations), studies of new typesof curves where infinite lines (e.g. asymptotes)
appeared and theories of proportion.
The accumulation of new results (Galileo Galilei,
Giordano Bruno etc.) led to the revolutionary
emergence of a new concept of absolute space inthe Descartes’ Geometry. However, Descartes did
not see that he was using a new concept. All he
was trying to do was to free geometry from theconstraints of construction confined to the use of aruler and compasses. But new Cartesian concepts
were created within the new horizon: infinite“Euclidean” space. Descartes did not know thiswas a new conceptual frame because heunderstood Euclid’s Elements in the new horizon,
convinced (it was obvious to him) that this frame
was the same as Euclid’s. Descartes’ convictions
were intuitive i.e. horizontal. Descartes wasworking with a new horizon before any definitionof absolute space appeared.

New concepts emerge in the hermeneuticalhorizon and its content is initiallydetermined by the fundamental properties of
the horizon. So, to explain why and which
concepts can emerge it is necessary toreconstruct the hermeneutical horizon. The
reconstruction
reveals
newinternal
mathematical possibilities and makes it
possible to stimulate not only the creationand emergence of the concepts that are new,
but also really useful.

The first explicit use of absolute space together
with the corresponding definition is by Newton
2 Cf. E. Grant Much ado about nothing. Theories ofspace and vacuum from the Middle Ages to the
Scientific Revolution, Cambridge University Press,
Cambridge, London, New York, New Rochelle,
Melbourne, Sydney 1981. Grant’s presentation avoids
the problem of the introduction of infinite space into
geometry and mathematics.

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is commonly available in some government
agencies’ websites.

Constructing Ontology for Exploring… (con’t)
We put forward a computer-based approach whichaims to help academic researchers to explore thetriple helix of academia-industry-government
based on three kinds of databases, namely,
scientific databases, patent databases, and project
databases. These databases can be used as
knowledge source for exploring the triple helix ofacademia-industry-government. But they are built
and maintained by different agencies and for
different purposes, in other words, they are
distributed and not linked with each other. Thus it
is not convenient and easy for researchers to get
knowledge about the triple helix from them.
Constructing ontology based on these three kindsof databases for a specific domain links those
databases in the domain, and thus it offers an
optional way for scientific researchers to explore
the triple helix of academia-industry-government,
which is budget and time-saving and can provide alot of useful information for them to make theirstrategic research plans, or what we called researchroadmaps.

After getting these three data sets, we need to build
relations among them for further analysis, andontology is used for this purpose. We put forwarda hybrid method for constructing ontology fromthe three data sets. In terms of hybrid, we mean themethod is composed of both a bottom-up process
and a top-down process. Top-down and bottom-up
are strategies of information processing andknowledge ordering, mostly involving software,and by extension other humanistic and scientificsystem theories (see Wolfe 2003). After discussion
with some academic researchers, we found a fourlevel ontology for a domain is appropriate forlinking and analyzing the three datasets.
•••

A Hybrid Method of Constructing Ontology
For academic researchers, when they develop theirresearch roadmaps, they commonly consider aspecific research field, for example, biotechnology,
or nanotechnology. Here we use the term domain
to denote the field that researchers are interested in.
A domain can be simply defined by one or several
keywords, for example, a domain can be definedby fuel cell and vehicle as (fuel cell, vehicle).
Researchers can specify a domain according totheir preferences. They can specify a quite wide
domain, for example, nanotechnology; or specify arelatively narrow domain, for example, compound
semiconductor crystal devices. After a domain is
specified, three kinds of data sets corresponding toeach dimension of the triple helix in the domain
are collected.

•

Keyword level: keywords are gathered fromthe three data sets (bottom-up).
Sub-topic level: subtopics are mainly
summarized from keywords (bottom-up), anda sub-topic can also be added into this levelwith expertise from a more general level –topic level (top-down).
Topic level: topics are more general thansubtopics. They can be mainly summarizedfrom sub-topics (bottom-up), and also could be
generated with expertise (top-down).
Sub-domain level: Sub-domains are mainly
generated with expertise or from authorized
agencies (top-down), and of courses, a subdomain can be summarized from sub-topics(bottom-up).

As shown in Fig. 1, when identifying elements ineach level, from keyword level to sub-domain
level, less and less bottom-up, and more and more
top-down is involved. Most of relations among
elements can be identified during the hybrid
process. For example, one subtopic wassummarized from several keywords, and then it isnaturally that those keywords are affiliated withthis subtopic. New affiliation can also be added
according to expertise. The structure of the
ontology is a network, instead of a tree, whichmeans a keyword can be related to several
subtopics, a subtopic can be related to several main
topics, a main topic can be related to several
technologies, and vice versa.

1. Data set in academia dimension. This data set
contains mainly the information about
academic publications in the domain. Suchdata can be obtained from scientific databases.
2. Data set in industry dimension. This data set
contains the information about the patents inthe domain. This data set can be obtained frompatent databases.
3. Data set in government dimension. This data
set contains the information about the projects
supported by government in the domain, and it
can be obtained from project databases which

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Sub-domains

Subtopics
Keywords

Bottom-up

TopicsBService), and the government dataset was
obtained from NII (National Institute ofInformatics) Scholarly and Academic Information
Portal, Japan (http://ge.nii.ac.jp/genii/jsp/index.jsp).
After getting these three data sets, by applying the
hybrid method, a four-level ontology wasconstructed which includes 144 keywords, 106
sub-topics, 25 topics, and 10 sub-domains. A webbased support system was developed for helping
researchers to explore the triple helix of academiaindustry-government in this domain. Mainfunctions of this system include:

Top-down

A domain

Spring, 2007

Three data sets

Searching. Researchers can search the three data
sets with any element in the ontology. Searching
function will provide researchers an overlook
about the triple helix of academia-industrygovernment for each sub-domain, topic, subtopic
or keyword: who are working on it in academia;
what projects are related to it, what patents havebeen granted, and so on.

collected in he domain
Fig. 1 A hybrid method of constructing ontology
With the ontology, the triple helix of academiaindustry-government can be analyzed with eachelement from different level of the ontology.
Information extracted from the three data sets is
helpful for answering questions such as• What projects were, are, or will be supportedbygovernments
in
this
subdomain/topic/subtopic/keyword?
Who(persons and intuitions) were, are or will be incharge of the projects? How much money was,is or will be invested from government tothose projects?
• How many patents have been issued, or havebeen applied in this sub-domain (/topic,
subtopic, or keyword)? Who hold or are
applying for those patents?
• Who from academia are doing research in thissub-domain (/topic, subtopic, or keyword), andwhat are their publications?
• Which sub-domain (/topic, subtopic, or
keyword) is often addressed by academiaindustry-government, and which is not?
• What are the relationships among technologies,
research
topics,researchers,
andapplications/products?

Network Visualization. Here nodes in a network
include elements of the ontology, researchers,
publications, patents, and projects. Network
visualization is helpful for understanding therelations among nodes. Fig. 2 shows an example ofa network among researchers tied by sub-domains,in which Ti (i = 1, ..., 10) denotes the 10 subdomains, Rsj (j = 1,…, 94) denotes researchers.
Calculating distance/similarity. The network canprovide rough distances between each two nodes
which can be roughly calculated as the smallest
number of connections between them. Someexisting approaches and algorithms can provide
more detailed distances based on different
understanding
anddefinitionsofdistances/similarity. Considering our data setscontains both numerical data and categorical data.
In our system, we selected a method put forward
by SiQuang Le and TuBao Ho (see Le S et al.
2004) to calculate distances between nodes.

A Case Study: Vehicle-Related Fuel Cell
Technologies

Roadmapping between nodes. This function is tohelp researchers to identify connections between
two specific nodes in networks. Thus if a
researcher is doing work related to one technology,
and now he/she is considering to do some work
related to another technology, the system will tell
the researcher what technologies might be the links
between them, or we can say the system suggest
something which maybe the researcher shouldhave a look at.

Here we introduce an application of this approachto a domain defined by two keywords, fuel cell andvehicle. With this domain, the academia data set
was obtained from the database of publications ofachievements, National Institute of Advanced
Industrial Science and Technology, Japan(http://www.aist.go.jp/RRPDB/system/Koukai.Top), the industry data set was obtained from thepatentcirculation
database,Japan(http://www.ryutu.ncipi.go.jp/PDDB/Service/PDD

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References:

Concluding Remarks
The computer-based approach introduced in thisarticle can be looked as a starting point of aroadmapping process in academia. It can be
integrated with other computer-based approaches,
and also most likely with expert-based approaches
and workshop-based approaches, for generating
research roadmaps. During application, theapproach should be customized according todifferent objectives and other context. For example,
in the case study introduced in this article, data
were from Japanese databases since in the project
what the researchers cared about were the triple
helix of academia-industry-government in Japan.
When applying the approach in a different country
or in a different field, the data sources will bedifferent. The
ontology is not limited to fourlevels if a two or three-level ontology is foundmore appropriate.

Etzkowitz H (2002). The Triple Helix of
University - Industry – Government Implications
for Policy and Evaluation, working paper, Science
Policy Institute, Stockholm.
Le S and Ho T (2004). Measuring the Similarityfor Heterogenous Data: An Ordered ProbabilityBased Approach. International conference on
discovery
science,
Padova,ITALIE
20041973, vol. 3245, pp. 129-141
Ma T., Liu S., Nakamori Y (2006). Roadmapping
as a way of knowledge management for supporting
scientific research in academia, Systems Researchand Behavioral Science, Wiley, Vol. 23, No. 6, pp,743-755.
Wolfe JM, Butcher SJ, Lee C, Hyle M. (2003)
Changing Your Mind: On the Contributions of
Top-Down and Bottom-Up Guidance in Visual
Search for Feature Singletons, Journal of
Experimental Psychology: Human Perception and
Performance, Vol. 29, No. 2, 483–502.

Constructing ontology and the computer-based
approach introduced in this paper itself does not
generate research roadmaps. The approach help
researchers to identify “where I am” and “where I
want to go”, but does not give answers to “howcan I get there”, instead it suggests somethingwhich might be important for getting there.

Fig.2 An example of a network

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Researcher Column 3:

both approaches in order to construct a usefulontology.

Constructing Ontology for the 21th
Century COE Program Technology
Creation Based on Knowledge Science
1.

Spring, 2007

2. Bottom-Up Classification and Specification:
Keyword Analysis
To build an outline of the ontology of COE program,we started with the paper presenting an introduction
to this program authored by the program leader(Nakamori 2004). After analyzing the purpose andsub-projects of the program, we selected the key
terms and concepts mentioned in the paper andorganized an ontology outline with three levels of
branches. The first level included five main topics:
knowledge
science,systemsscienceandmethodology, education in knowledge science,
knowledge creation, and management of technology.
In addition, we also referred to the program reports
presented by the program leader in later periods tocheck and revise the outline.

1.2.

Jing Tian , Andrzej P. Wierzbicki , Hongtao
Ren1., Yoshiteru Nakamori1.
1.
Center for Strategic Development of Science andTechnology, JAIST;
2.
National Institute of Telecommunications, Poland
jtian@jaist.ac.jp

The work presented here attempted to create an
ontology characterizing the 21st Century COE
Program Technology Creation Based on Knowledge
Science at Japan Advanced Institute of Science andTechnology (JAIST), based on a combination of abottom-up and top-down approaches to ontology
creation. An example of application of this ontology,
related to an adaptive hermeneutic agent (AHA), is
given.

Furthermore, we collected the papers authored by
COE project members - as many as were available.
Since we had to limit this search to electronic files,
we finally considered only 43 papers, which were
either included in Proceedings of International
Symposium on Knowledge and Systems Sciences(JAIST, 2004), or Proceedings of the First World
Congress of the International Federation for Systems
Research (Kobe, 2005), or in the International
Journal of Knowledge and Systems Sciences (Issues
1 to 6). We extracted the keywords from all papers
and counted the frequency of their occurrences in
the full body of papers by using a computer program
designed by a member of our group. We havechosen the keywords with high frequency tosupplement the outline of COE ontology as the
fourth level.

1. The Goals and method of Constructing
Ontology of the COE Program
We tried to construct the ontology of 21st Century
COE Program Technology Creation Based on
Knowledge Science at JAIST as a case study, withthe following goals:
I.
To clarify the use of the concept ofKnowledge Science in this Program and makeexplicit (at least, as much as possible) assumptions
about this concept that are often tacitly made;II. To represent a vocabulary of terms used inthis COE Program, together with a systematizationof terms used;
III. To help in the development of a softwaresystem designed to support hermeneutic search ofliterature, and possibly in other projects related to
the COE Program.
Known ways of constructing ontology can betreated not as absolute recipes, but hints how toproceed. There is a distinction of a top-down
approach - actually, starting with an intuitive
perception of the basic concepts in hermeneuticalhorizon (Król 2007) and specifying them in detailsubsequently – and a bottom-up approach - starting,say, with the concepts actually used in a given fieldof knowledge and trying to interpret them and theirstructural relations. The top-down approach starts
with issues related to meta-model functionality; thebottom-up approach starts with issues related tosystematization and standardization (Dieng and
Corby 2000). Obviously, we need a combination ofAnother attempt was a clustering of keywords basedon their joint occurrence. We selected a simple QT(quality threshold) clustering algorithm, see, e.g.
Heyer et al. 1999): if the frequency of occurrence ofa pair of keywords equals or exceeds an assumed
threshold t, the pair might be counted to belong to a
candidate cluster; the largest of such candidate
clusters is counted as an actual cluster, it issubtracted from the entire set of keywords, and theprocedure is repeated on the remaining keywords. It
turned out that the joint occurrence of keywords is
not common, most frequencies of such cooccurrence are zero, thus the clustering was done at
the threshold level t = 1. The result of a clustering of
keywords showed us the correlations among the
keywords and provided their comparison with the
top-down outline of ontology.

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technologists, who should be well understood bymanagers. Thus, we believe that the two views listed
above should be combined. Moreover, they
incompletely describe what knowledge management
is; there is a third, essential view, seeing knowledge
management as the management of human resourcesin knowledge civilization era, concentrating on
knowledge workers, their education and qualities,
assuming a proper understanding of their diverse
character, including a proper understanding oftechnologists and technology.

3. Top-Down Approach: a Reflection on theConcept of Knowledge Science
Knowledge science (KS) is often confused with ortacitly assumed to be subordinated to knowledge
management (KM), thus we first reflect on theorigins and meaning of the second term. Knowledge
management has much popularity in management
science, but its technological origins are often
forgotten. It was first introduced by computer
technology firms in early 1980-ies – first in IBM,then Digital Equipment Corporation who probably
was the first to use the term knowledge management– as a computer software technology in order torecord the current work on software projects. This
started the tradition of treating knowledge
management as a system of computer technologies.
Later this term was adopted by management science,and made a big career. This has led to two opposite
views how to interpret this term, see, e.g. (Wiig
1997, Davenport and Prusak 1998):
 As management of information relevant forknowledge-intensive activities, with stress on
information
technology:
databases,
data
warehouses, data mining, groupware, information
systems, etc.
 As management of knowledge related processes,with stress on organizational theory, learning,types of knowledge and knowledge creation
processes.

This is particularly visible concerning the conceptsof technology management versus knowledge
management. Management science specialists inknowledge management often tend to assume thattechnology management is just a branch ofknowledge management; technologists specializingin technology management stress two aspects.
However, an essential meaning of the word
technology is the art of designing and constructing
tools or technological artefacts (thus, technology
does not mean technological artefacts, althoughsuch a meaning is often implied by a disdainful useof the word technology by social sciences, e.g., in
the quoted above phrase dumping technology). In
this sense, the term is used in the phrase technology
management. Secondly, technology management
might be counted as a kind of special knowledge
management, but it is an older discipline, using welldeveloped concepts and processes, such astechnology assessment, technology foresight (see,e.g., Salo and Cuhls 2003) and technology
roadmapping (see, e.g, Willyard et al. 1987, Phaal etal. 2004). Only recently, some of these processes
have been also adapted to knowledge management,see (Ma et al. 2005).

The first view is naturally represented byinformation technologists and hard scientists; thesecond by social scientists, philosophers,
psychologists and is clearly dominating inmanagement science. Representatives of the second
view often accuse the first view of perceiving
knowledge to be an object while it should be seen as
knowledge related to processes; they stress thatknowledge management should be management ofpeople.

All the above discussion implies that we areobserving now an emergence process of a newunderstanding of knowledge sciences – aninterdisciplinary field that goes beyond the classicalepistemology, includes also some aspects ofknowledge engineering from information technology,
some aspects of knowledge management frommanagement and social science, some aspects ofinterdisciplinary synthesis and other techniques(such as decision analysis and support, multiple
criteria analysis, etc.) from systems science. Thisemergence process is motivated primarily by theneeds of an adequate education of knowledge
workers and knowledge managers and coordinators;
however, also the research on knowledge andtechnology management and creation needs suchinterdisciplinary support.

However, while it is correct that knowledge
management cannot be reduced to management of
information, such a correct assessment is a pitfall(an unfortunate impact of binary logic on our
thinking): being sure that they are right, therepresentatives of the second view overlook both thecomplexity and the essence of the controversy. The
complexity is that, historically, knowledge
management has started with technology and cannot
continue
withouttechnology;
thus,
both
interpretations should be combined in adequate
proportions. The essence of the controversy is thatmanagement of people should be also understood asmanagement of knowledge workers; and knowledge
workers are today often mostly information

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Strategic Development of Science and Technology

Spring, 2007
particular sub-topics (Third Lever). All keywords
was summarized as and categorized into the subtopics (Fourth lever). In addition, the clustering of
the keywords gave us the hints to find the relations
between the subtopics and the further relations
between topics as well as sub-domains. The
structure of the ontology is not only a simple tree,
also a network. Because of the limitation of pages, I
can not list the proposed ontology here. Ourclassification is naturally not absolute nor theultimately final; it might be further enhanced andcorrected as new data will become available.

The classical understanding of the words knowledgescience might imply that it is epistemology
enhanced by elements of knowledge engineering,knowledge management and systems science.
However, the strong disciplinary and historical focus
of epistemology suggests an opposite interpretation:knowledge science must be interdisciplinary, thus itshould not start with epistemology, although it must
be enhanced by elements of epistemology. The fieldclosest to knowledge science seems to be systems
science – at least, if it adheres to its interdisciplinary
origins and does not suffer too much from theunfortunate (but unavoidable today) disciplinary
division into soft and hard systems science. The
noticeable tension between soft and hard systems
science is just an older version of the tension
between understanding knowledge management
either from the perspective of knowledge
engineering, or from the perspective of social andmanagement science, mentioned above.

5. An Application: Adaptive Hermeneutic Agent(AHA)
On the basis of requirements of researchers (Tian etal. 2006a, 2006b) and the phenomenon of
Hermeneutics (Wierzbicki and Nakamori, 2006), a
software tool for information and knowledge
retrieval was designed, see (Ren and Wierzbicki2007), (Ren et al. 2007), in order to help researchers
in gathering and interpreting relevant knowledge or
research materials; this software tool is called
Adaptive Hermeneutic Agent (AHA). The AHA isequipped with a simple and intuitive search interface
and uses familiar search syntax, such as used bypopular search engines (like Google, Yahoo). The
search support can be extended to the definition ofqueries that will be automatically executed by thesystem with a fixed period of time. The definition ofa query by the user is helped by ontologicalinformation; actually, the ontology described above
is used in AHA as a basis of defining queries thatcan be selected from this ontology, supplemented or
modified, for example, by adding new keywords thatare relevant to the searched topic. After the query is
executed, the AHA can also filter the obtained
results by using a reinforcement learning approachthat relies on a profile of the user’s interests. The
AHA could also use a visual interface for theclustering and graphical presentation of search
results.

To summarize, we should thus require thatknowledge sciences gives home to several
disciplines (quoted here in an alphabetic order): Epistemology and philosophy of science, Knowledge engineering, Management
scienceandknowledge
management, Sociological and soft systems science, Technological and hard systems science,On equal footing, with a requirement of mutual
information and understanding, this basic
classification should be also reflected in the
proposed ontology of the COE Program.
4. Final Proposal of the Ontology
Based both on the bottom-up classification and on
the above reflection as a basis of top-down approach,
the ontology of the COE Program can be proposed.
It is organized as an inverted tree, with fourth-level
branches corresponding to keywords found in thepapers of COE Program members. The generalcategory of the domain of Knowledge Science
includes the following eight sub-domains as the firstlever of ontology of the COE Program: Knowledge Creation and Transformation
 Knowledge Representation, Systematization,
Acquisition Knowledge Management
 Systems Science Education and Knowledge Science Management of Technology
 Technology Creation
 Diverse Related Themes
Each sub-domain is consisted of several topics(Second lever); the different topics include

Therefore, the COE ontology as described earlier is
an important element, first step in developing thesoftware tool of AHA. The second step is thecreation of user profile. The user, for example, a
COE member, could extract the knowledge from
COE ontology to formulate the outline of user
profile, for example, select the domains (keywords)
he are most interested in and give the weights fordifferent keywords. Then, the user could gather
relevant knowledge and information based on his
profile by using search engines connected to AHA.
The AHA will do adaptive selection automaticallyas following steps: text extraction (from MS-word

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JAIST-COE News
Strategic Development of Science and Technology
file to text or from PDF file to text); keyword
extraction and frequents calculation (extracting
keywords from the search results by statistics
method); measurement of the similarity of each fileand user profile; giving a ranking list including top
N results. The final step is user evaluation.

Spring, 2007
Theory and Practice’, Nov. 10-12, JAIST, Japan, pp.
1-4
Salo A., Cuhls K. (2003) Technology Foresight – Past
and Future. Journal of Forecasting 22(2-3):79-82
Phaal R., Farrukh C., Probert D. (2004) Technology
roadmappingg - a planning framework for evolution
and revolution. Technological Forecasting andSocial Change 71:5-26
Ren H., Wierzbicki A.W. (2007) Implementing Creative
Environments for Scientifc Research. Journal of
Systems Science and Systems Engineering, to be
published.
Ren H., Tian J., Nakamori Y., Wierzbicki A.P. (2007)
Electronic Support for Knowledge Creation in a
Research Institute. Journal of Systems Science andSystems Engineering, to be published.
Tian, J., Nakamori, Y., Xiang, J. and Futatsugi, K. (2006a)“Knowledge Management in Academia: Survey,
Analysis and Perspective”, International Journal ofManagement and Decision Making, Vol.7, No. 2/3,
pp. 275-294.
Tian J., Wierzbicki A.P., Ren H. and Nakamori Y. (2006b)“A Study of Knowledge Creation Support in a
Japanese Research Institute”, International Journalof Knowledge and System Science, Vol. 3, No. 1,
pp.7-17, March 2006
Wierzbicki, A.P. and Nakamori Y. (2006) Creative Space:
Models of Creative Processes for the Knowledge
Civilization Age. Springer Verlag, Berlin-Heidelberg
2006
Wierzbicki A.P., Nakamori Y. (2007) Knowledge sciences– some new developments. Zeitschrift für
Betriebswirtschaft, in print
Wiig K.M. (1997) Knowledge management: an
introduction and perspective. Journal of Knowledge
Management 1(1): 145-156
Willyard C.H., McClees C.W. (1987) Motorola’s
technology roadmap process. Research Management30(5):13-19

Other possible applications of the work on ontology
formation described here include, for instance, thedevelopment of an ontology of the School
Knowledge Science in JAIST, an ongoing projectthat will include the lessons from the work described
here; or a construction of a Knowledge Map or a
research network for professionals interested inrelated domain, etc.
References
Davenport T. and Prusak L. (1998) Working Knowledge:
How Organizations Manage What They Know.
Harvard Business School Press, Boston Ma.
Dieng R., Corby O. (2000) Knowledge Engineering andKnowledge Management: Methods, Modesl andTools. Springer Verlag, Berlin-Heidelberg 2000
Heyer L.J., Kruglyak S, Yooseph S. (1999) Exploring
expression data: identification and analysis ofcoexpressed genes. Genome Res., 9, 1106–1115.
Król Z. (2007) The Emergence of Complex Concepts inScience. In Wierzbicki A.P. and Nakamori Y., eds.:
Creative Environments: Issues of Creativity Support
for the Knowledge Civilization Age. Springer Verlag,
Berlin-Heidelberg 2007, in print
Ma T., Liu S., Nakamori Y. (2004) Roadmapping for
Supporting Scientific Research. Proceedings of
MCDM 2004 (17th International Conference on
Multiple Criteria Decision Making, Whistler,
Canada), Simon Fraser University
Nakamori Y. (2004) Introduction to a COE Program at
JAIST. Proceedings of International Forum‘Technology Creation Based on Knowledge Science:

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Strategic Development of Science and Technology

Spring, 2007

COE Center News
♦ Prof. Andrzej P. Wierzbicki left the Center and came back Poland as of April 2007. He will visit to
JAIST on October 2007.
♦ Dr. Zhu stayed JAIST from April.17 to March 17 2007. He gave us a COE seminar. And he gave us alecture about The JAIST Scool: Continuity and Future.
♦ Dr. Adam Wierzbicki visited to JAIST to give us the lecture about Modern Open Distributed Systems:
Research Challenges and Applications.
♦ Dr. Jing Tian left the Center and was in Tokyo as of March 2007.
♦ Dr. Homei Miyashita left the Center and was in Tokyo as of April 2007.
♦ Mr. Hiroyuki Asano left the Center and was in Kanagawa in Japan as of April 2007.
♦ Dr. Tomoko Kikuchi joined the Center as postdoctoral researcher as of April 2007.

Call for Papers:

IJCKS 2007 (KSS2007/KICSS2007)
International Joint Conference on Knowledge Science
The 8th International Conference on Knowledge and Systems Science (KSS2007)
jointly with the 2nd International Conference on Knowledge, Information and Creativity Support Systems (KICSS2007)
November 5-7, 2007
Ishikawa High-Tech Exchange Center, Ishikawa, Japan
http://css.jaist.ac.jp/ijcks2007/(Details on page 18)

JAIST-COE News is published by the Center for Strategic Development of Science and Technology, Japan
Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan.
Questions, comments, article proposals and for more information about the JAIST-COE News, contact
Kikuchi Tomoko at tkikuchi@jaist.ac.jp, tel. +81-761-51-1839 or go to the link:www.jaist.ac.jp/coe/index.htm
Articles in this newsletter are based on a variety of sources. Signed articles reflect the views of the author.
Unsigned articles are prepared by the editorial staff, which is solely responsible for their content.

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