Noospheric Way of Organizing Knowledge in the Knowledge Bases of

Innovate Importance

Yurii Pikalov

Department of International Economic and Business, Cherkassy State Technological University, Ukraine

Keywords: Knowledge, Signs, Economic Activity, Infosphere, Phylogenesis and Ontogenesis of Knowledge, Innovative

Activity, Noospheric Knowledge Map.

Abstract: A major part of the flow of new knowledge is knowledge about facts. Its value is inversely proportional to

the increasing amount. It can be enhanced by the way of conceptualization of knowledge, developing and

applying the innovative ways of its organization. It is suggested to use the way of organizing knowledge based

on the model of cognition of objects as spheres of phenomena; convergence of the sphere of natural and

conscious phenomena, models of phylogenesis (Paradigm Innovative Development) and ontogenesis

(Vertical Integration and Parabola of Knowledge) of knowledge as well as based on the paradigm of the

ontology of sign constructions. The above-listed tools have been obtained as a result of studies of M.V.

Polyakov’s scientific school, and they have been adapted by the author of the paper to develop architecture

of the knowledge bases functioning as a part of innovative systems of venture enterprises.

1 INTRODUCTION

Any conscious activity bears information nature. The

economy is not an exception. Information is an

ambiguous term. One of its meanings is measure of

the impact of message on its recipient. Besides,

information is synonym of message. Messages

consist of signs; therefore, we will speak of

fundamentally sign nature of conscious activity.

Signs are the form of existence of knowledge.

Knowledge can be old or new. It also can be genuine

or false. It depends on criteria of genuineness and is

determined in practice. Therefore, almost any

conscious activity is cognitive, even if the subject

does not strive to it. The subject either reinforces old

stereotypes, or gains new experience.

Cognition is inseparable from economy, whether

the subject wishes it or not. Although, in terms of

innovations, it can take place with zero result.

Depending on the object and phase of development,

cognition merges with economy or stands apart of it.

Similar points were raised by Friedrich August

von Hayek in his paper “Individualism and Economic

Order” (1958).

https://orcid.org/0000-0002-4968-965X

1.1 Some Related Work and

Relationship with Knowledge

Management Area

Many books and articles on knowledge management

begin with definitions of their essence (Nonaka and

Takeuchi, 1995), (Lundvall, 2007). The diversity of

various definitions of knowledge suggests that they

have failed so far. Most definitions concern not so

much with the essence, but with the origin, purpose

and application of knowledge. Such works as

“Ontology and knowledge economy” (Polyakov,

2015) may be an exception, because it concerns with

the ontology of knowledge that is reduced to the

ontology of signs. Such “knowledge about

knowledge” is necessary, first of all, to optimize the

structure of the denoting part of knowledge, called the

knowledge base. For innovation activity, it is much

more important to be able to recognize the knowledge

necessary and sufficient to solve the innovation task.

Traditionally, there are researches devoted to this

problem which are related to librarianship. The

results are well-known library classification systems

(UDC, educational standards, nomenclature of

subjects for which academic degrees are awarded,

etc.). However, they describe a well-established

250

Pikalov, Y.

Noospheric Way of Organizing Knowledge in the Knowledge Bases of Innovate Importance.

DOI: 10.5220/0008347302500257

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 250-257

ISBN: 978-989-758-382-7

picture (more precisely, a map) of knowledge, and

today it is a stream of changes, especially in the

infosphere. Especially when it comes to computer

knowledge base, with its ability to update the

structure and content. This disadvantage is partly

compensated by the efforts of educational

organizations, virtual research networks, organizers

of scientific communications, publishing houses of

specialized literature, etc., institutes, such as

INSTICC, Common Ground, Global Science and

Technology Forum (GSTF) and others. They react to

the facts of changes in science and technology

promptly, but the reactions do not always coincide

and do not always fit into the traditional framework.

It takes time to comprehend and systematize them.

It is worth mentioning researches of

S.Ranganathan (1957) or Karin Karlics (2013).

However, in terms of the connection with ontology

and coverage of the variety of objects and aspects of

knowledge in their relations in this research, we

focused on the works of M.V. Polyakov and his co-

authors (Polyakov, 2017; 2018).

The originality of our research lies primarily in its

conceptual character. It consists in a completely non-

obvious interpretation of the results of the

development of the noospheric approach to cognition

and its transfer to the problem of knowledge, its

essence and properties, with application to the

development of Knowledge Bases that are part of

innovative systems.

1.2 Structure of the Paper

Section 2 discusses the basic concepts and includes:

General requirements to Knowledge Bases; Object

structure of cognition process and knowledge;

Genesis of cognition and economy in the spheres of

phenomena, Vertical integrated units of knowledge

and their genesis.

Section 3 (Outcomes) discusses the concepts of

Knowledge maps and Noospheric knowledge map, as

well as multimaps, which underlie noospheric

knowledge bases. Section 4 is Conlusions.

2 BASIC CONCEPTS

Knowledge, which is the result of activity of such

fields as semantic technologies (Berners-Lee, 2001),

organizational semiotics (Stamper, 2000), ERP-

systems, theory and practice of business processes

(Scheer, 1999), differs drastically from natural-

science and humanitarian knowledge by content as

well as by methods of obtaining and application.

This knowledge is related to infosphere, but relies

on computers, representing the implemented

knowledge of physical phenomena. As a rule, it is

obtained by young people, often by students within

small enterprises or informal groups. These structures

have a chance (not always high one) to become

successful startups. The above-mentioned people do

not have theoretical background in the field of

infosphere, as well-recognized theories just do not

exist there, and it is not even sure whether they could

exist. At the current stage, for the whole period of its

development, infosphere demonstrates maximum

degree of integration of cognition and economy. Here

it is, in fact, two sides of the same coin. The same high

degree of unawareness of what is going on should be

noted, which is quite natural for pre-paradigm phase

of development. This is particularly evidenced by the

words of Grady Booch, who has acknowledged that

“many years will pass and OOAD will become as

usual as motherhood or apple pie, but no one will be

able to explain what the Object Oriented Analysis and

Design is” (Booch, 2004).

Having formulated the productivity paradox of IT

in economy and business, Robert Solow actually

pointed the signs of technological and financial

bubble in the sphere of IT application in the global

economy (Solow, 1987). A discussion took place in

the scientific world, which, in our opinion, was not

able to resolve the paradox, having maintained the

status quo.

Kris Freeman and Carlota Perez have found the

emergence of these bubbles at the stage of adaptation

of innovate technologies to social and economic

environment to be regularly recurring pattern

(Freeman, 1982, Perez, 2011).

All innovations, which have taken place in the

infosphere until today – are based on pure heuristics,

experience (analogy, association) or modelling, when

the abstraction of the deeper level is applied to

simplify the existing practices. This series continues

in future in the form of such trends as Big Data and

Artificial Intelligence.

It is interesting that Peter Druker spoke of the

failure of IT to become a tool for management of

economy and business, like of something very

obvious: “…all of us nonconformists agreed on one

thing: The computer would, in short order,

revolutionize the work of top management. We could

not have been more wrong. The revolutionary

impacts so far have been where none of us then

anticipated them: on operations.” (Drucker, 2001).

One more thing: “But they did not, as a rule,

realize that what was needed was not more data, more

technology, more speed. What was needed was to

Noospheric Way of Organizing Knowledge in the Knowledge Bases of Innovate Importance

251

define information; what was needed was new

concepts.” (Drucker, 2001).

In our opinion, quasi-physical approach to

conscious phenomena, and products created on this

basis, in particular, knowledge bases, are of obvious

interest in this respect.

2.1 General Requirements to

Knowledge Bases

Knowledge, indeed, is diversified, and its

development requires different innovative systems

and methods for innovative management.

In this study, the knowledge, accumulated in the

knowledge base, serves as the major resource for

creation of innovative products, including description

and the process of creation of the products

themselves. However, there is also an inverse

relationship. The experience in empirically heuristic

cognition, being a result of economy, as well as the

experience in application of scientific findings in

economy, is a resource for development of paradigm

and post-paradigm (scientific) cognition.

It is obvious that a skilled carpenter or joiner is

obliged to excel in woodwork, metalworker – in

metalwork, etc. Accordingly, an individual or

collective Subject of Innovative Activity (SIA) at the

enterprise has to know the ropes of knowledge.

Unlike the specialist or teacher, who works in a

narrow sphere, he needs to be able to deal with

an ample sphere of diverse knowledge. In order to be

well-versed in a rapid stream of knowledge, it is

necessary, first of all, to have an idea of architecture

(in other words, structure) of the whole wide range of

knowledge, accumulated by mankind and being

relevant at the current state of cognition. It is critical

to understand what is the state and trends in the

development of knowledge system.

We can implement rational investment policy,

build ideal innovate system, but what will be the

benefit if, due to unawareness of what is going on, the

goals and trends in the development turn to be risky,

or even false and needless? In order to diminish these

risks, Noospheric knowledge base should be not just

a topogram, but also a sort of “roadmap” of the

innovative development of infosphere. Dynamic

properties of the models of PIDev and parabola of

knowledge provide this opportunity.

2.2 Object Structure of Knowledge and

Cognition Process

According to Noospheric thinking of

V.I. Vernadsky, the world as an object of cognition

(the signified part of sign) is divided into the spheres

of phenomena, as follows: a) physical –

physiosphere; b) biological– biosphere; c) conscious

– noosphere.

Figure 2.1 shows the relationship between

physiosphere, biosphere and noosphere within the

universal sphere of phenomena.

Figure 2.1: Object structure of knowledge.

Concentric circles symbolize the levels of

abstraction, from zero (practical knowledge which

can be implemented in bodies or be imitating the

activity) to philosophy and methodology (depth 5).

A complete circle corresponds to noosphere,

segment 240’ – to biosphere, and 120’ – to

physiosphere. Each sphere of phenomena has a

corresponding vertical and parabola of knowledge.

The object and subject structure of physiosphere

and biosphere has been fundamentally developed

within several centuries after scientific revolution of

the 18

century. Scientific cognition of noosphere is

at the beginning of its path. In the framework of the

noosphere, we are, primarily, interested in the

infosphere and economic sphere. Figure 2.2 shows

their relationship.

Truncated cone symbolizes the infosphere. As far

as information is an ambiguous term, denoting

messages as well as the extent of their impact on the

recipient, it is more preferable to use the term “sphere

of sign phenomena”.

The discovery of physiosphere (starting from

geosphere) as well as biosphere and noosphere,

scientific revolution in the information sphere,

approaching owing to a wide use of data processing

technologies being physical by their nature, have

caused cessation of the traditional structure of

scientific knowledge to correspond to the goals and

objectives of further innovative development.

Thus, according to interpretation of Vernadsky’s

teaching, provided by M.V. Polyakov jointly with

coauthors (Polyakov et al., 2018), the object of

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integral cognitive and economic activity is the sphere

of phenomena. The cognition of phenomena is

concluded by implementation of its results in bodies

and processes, needed by people (artefacts – artificial

phenomena). As Niels Bohr said, “the objective of

science is to make something incomprehensible to

become trivial.”

Figure 2.2: Object structure of knowledge. Noosphere.

In order to handle the arrays of phenomena, they

have to be simplified. Vernadsky, in particular,

suggested breaking down the array of world

phenomena into the series of above-stated spheres,

each with a certain entity (physical bodies, living

organisms, sign bodies) behind phenomena. The

phenomena, related to different sphered, are closely

interrelated between each other, and in each of the

artefacts, as a rule, knowledge about more than one

kind of phenomena is closely intertwined between

each other.

Therefore, if it is an innovative enterprise and its

mission is “To cognize and manage!”, the object of

innovative activity and the respective knowledge base

shall correspond to the structure, described above.

2.3 Genesis of Cognition and Economic

in the Spheres of Phenomena

A human cognizes the essence of phenomena and

implements it in the artefacts. The outcomes are

represented by knowledge in sign and embodied form

(artefacts). At the same time, the economic is

inseparable from cognition. Theoretically, cognition

can be suspended, but then the economic will cease to

develop and start degrading. Actually, the economic

is also the cognition. Their living connection can be

broken due to specialization and alienation. This

leads to breakaway of cognition from production, and

the latter – from creativity.

Similar assumptions were made by many of

outstanding thinkers, among which are Ferdinand de

Saussure (2017), Friedrich August von Hayek (1958).

Wide application of information technology in the

sphere of conscious phenomena enabled Bertin

Martens (2004) to compare the economy with an

information machine for production of knowledge,

including their embodied form, and Peter Brödner

(2005) compared the organization with a computer

program. The process of cognition is spread over

time, and in diverse time segments, it behaves

differently. Accordingly, it requires a diachronic, in

other words, historical approach.

Polyakov’s work shows an example of the

application of diachronic model of Paradigm

Innovative Development (PIDev) of cognition and

economy (Polyakov et al., 2017).

Being a single whole, every sphere, driven by

innovations, undergoes a number of phases in its

development, as follows: 1) empirically heuristic

(before paradigm); 2) paradigm; 3) scientific (post-

paradigm).

The objects of changes are represented by the

innovative products, i.e. knowledge in symbolical or

embodied form.

Computers emerged as physical technology of

data processing. At that time, the infosphere was and

it is now in the empirical and heuristic phase of

development. It means that innovations take place

there at the level of practices (zero depth), do not

affecting the level of constructions and technologies,

not to mention the deeper levels of abstraction.

Computers have accelerated the development of

infosphere, thereby exposing the problems, and

approached the transfer to paradigm phase.

As far as the spheres of phenomena have

multilayer structure, there can be relict layers, along

with the advanced ones. For instance, physics of

a solid body, liquids and gases, optics, atomic and

quantum physics and other branches of physics

developed asynchronously. Nevertheless, multiple

principal tenets combined them into the physiosphere.

By analogy with Darwin's theory of evolution, we

can say that PIDev model, which determines

historical development of the whole sphere of

phenomena, is phylogenesis. In this case, innovations

act as analogues of specific bodies, and innovative

cycles within the framework of parabolas can be

considered ontogenesis.

Macrogenetic or phylogenetic characteristic of

knowledge stems from PIDev model.

2.4 Structural Units of Knowledge and

Their Genesis

It is difficult for an individual human to acquire

knowledge of all phenomena at once, even in the

amount of a single sphere, and even more so for the

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253

universal scale. That is why we have to fragment

knowledge.

Although, it is only holistic and completed

knowledge that can be quite understandable and,

therefore, productive and constructive. Figure 2.3

shows decomposition, and, respectively, integration

of knowledge about a particular sphere of phenomena

(or its parts) “vertically” (by levels of abstraction).

Figure 2.3: Ontogenesis of knowledge (Polyakov et al.,

2017).

The right branch of the parabola describes rising

from the abstract and simple (paradigm) to the

concrete and complex (practice). The abstract

corresponds to the set, and the concrete – to the

individual. In case of rising, every level of abstraction

(depth of innovation) also denotes a step. When the

steps are skipped during immersion and rising, it

means that the method of modelling is applied. For

example, we can see a certain and poorly

understandable correspondence between practical

results and mathematical apparatus.

Further, lacking full understanding, we can risk

applying this apparatus for improvement and

regularization of practical results. At the same time,

the obtained model will require subjective

interpretation, results of which, with a certain

probability, can be useful, useless or even harmful.

The application of abstractions of the high level to

practical results, bypassing the steps of the vertical of

knowledge, is called the modelling of conscious

phenomena (Fig. 2.4).

The term “modelling”, similarly to many other

terms in the sphere of conscious phenomena, got the

meaning, different from the traditional one. In the

sphere of natural phenomena, it means simplification

of real-life understandable object, while in the sphere

of conscious phenomena it is a hypothetical

understanding of still not understandable phenomena

and their effects.

Figure 2.4: Modelling of conscious phenomena.

The parabola of knowledge determines the

structure of knowledge units as well as their place and

role in the innovative processes. These properties

should find reflection in the structure of the

knowledge base being developed.

Therefore, this paper formulates a methodological

and theoretical background for development of the

architecture of knowledge bases, focused on

identifying, setting and addressing the crucial

innovative challenges in the sphere of conscious

phenomena, first of all related to information and

economy. It also serves as a methodological basis for

analysis of the existing solutions in the field of

knowledge organization in the form of knowledge

base as well as for evaluation of an impact of the

created knowledge base on the economic efficiency

of enterprise’s performance.

3 OUTCOMES

3.1 Knowledge Maps

It is convenient to use the term “Knowledge Map”

(KM) to denote graphical or text representation of the

architecture of knowledge. Its synonyms can be

represented by “topography (topogramma) of

knowledge” (ToK).

The genesis of knowledge is represented by

PIDev and VIK motels. PIDev reflects the

development of the sphere of phenomena, being not

differentiated by innovative products or levels of

abstraction, and, by analogy with biology, it can be

called as phylogenesis of knowledge. Knowledge

maps, PIDev and VIK models are not only mandatory

tools for innovations. It is also the means for

development of the innovators’ holistic world view,

being one of the most critical factors which have

impact on productivity of innovative activity.

The knowledge map should expose the real

significance and trends in development of cognition

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and economy, confirming or denying the

appropriateness of the existing trends, or facilitating

the emergence of the new ones.

3.2 Noospheric Knowledge Map

Knowledge base, the concept of which is a practical

outcome of this study, is called noospheric.

Noospheric Knowledge Map (NKM) has a

leading role among the knowledge maps, being a part

of the multimap. Every Structural Unit of Knowledge

– SUK (text, audio, video, and hypertext) should be

characterized by a number of properties, determined

by the acknowledgement of the fact of noosphere as

an objective reality. Table 1 shows the specification

of noospheric properties of NKM and the values

taken by them.

Imposition of the vertical of knowledge on the

infosphere demonstrates the vacancy of the key box,

corresponding to fundamental (ontological) theory of

information (to be more precise, sign) phenomena.

This gap does not allow developing the applied

theories and technologies. It enforces resorting to

modelling, which, in terms of the parabola of

knowledge, looks like the attempts to jump from the

deep philosophical (ontology, object, universal sign)

and mathematical abstractions over the levels of

fundamental and applied theories as well as

technologies to practices. As practice shows, in such

cases, a risk of falling considerably exceeds a

probability of success.

NKM does not assume that we should not deal

with the empirical and heuristic innovations or use

modelling for this purpose. It emphasizes the

necessity in these cases to carefully consider the risks

and use emerging opportunities for paradigm

innovations, which, addressing business challenges,

simultaneously build a ladder for scientifically

grounded innovations, characterized by law

probabilities of continuously recurrent success.

3.3 Application Area and Multimaps of

Knowledge

The noospheric knowledge base is the most important

component of the innovation systems of enterprises,

primarily those that create IT, wherein the noospheric

map should ensure the divergence of knowledge in

relation to the kind of phenomena and the phases of

phylogenesis and ontogenesis of knowledge, as well

as methods of immersion and ascent between

concrete and abstract, using the VIK. The noosphere

map ensures the convergence of knowledge relating

to different areas of phenomena and levels of

abstraction. Distinguishing the phases of the

phylogenesis of knowledge used as an innovation

resource, it allows you to select the mode of the

innovation system corresponding to the pre-

paradigm, post-paradigm or paradigm state of the

VIK used in solving a specific innovation task.

However, initial knowledge can enter the

noospheric base as element of different structures

(“maps”) of knowledge that do not fit with the

noospheric structure. Their names, annotations and

content may differ from the noospheric knowledge

map. Therefore, the noospheric knowledge base

provides the possibility of indexing knowledge

fragments according to several “knowledge maps”,

whatever they are called.

Table 1: Noospheric properties in NKM.

Name of SUK property Property value Remarks

1. Sphere of phenomena Physiosphere, biosphere, noosphere

«Spheric» approach,

V. I. Vernadskyi

2. Layer of phenomena

To be determined upon necessity. Unlimited number of layers and

levels. For example, infosphere (semiosphere, sign sphere),

econosphere, etc.

Layer is a part of

sphere of phenomena

and the higher laye

3. Innovation (product, process,

service)

Sequence number of product

Product is a part of

sphere or layer of

phenomena

4. Phase of development (cognition)

of the sphere of phenomena

Pre-paradigm, paradigm, post-paradigm (scientific) See PIDev model

5. Level of abstraction

Practice, technologies and constructions, applied theories, fundamental

theory, mathematics, methodology, philosophy

See VIK model

6. Method of cognition (knowledge

gaining)

Heuristics (assumptions). Empirics (experience, analogies). Immersion

from the concrete to the abstract, rising from the abstract to the

concrete. Modelling

See parabola of

knowledge

7. Depth of innovation

Levels: practices, technologies, applied theories, fundamental theory,

mathematics, methodology, philosophy

See VIK model

8. Modality

Problem (left branch of parabola of knowledge), solution (right branch

of parabola of knowledge)

See parabola of

knowledge

Noospheric Way of Organizing Knowledge in the Knowledge Bases of Innovate Importance

255

The users-participants of innovation processes

determine these “knowledge maps”, wherein the

noospheric map performs not only the described

special functions of divergence-convergence of

knowledge, but also serves as a “common

denominator” for many other maps.

4 CONCLUSIONS

This paper suggests the idea of conceptualizing the

knowledge, accumulated in computerized knowledge

base and used in the process of production.

At the present time, historically developed system

of knowledge with its division into natural and

humanitarian knowledge is intensively diffused by

the flows of heterogeneous knowledge about

individual artefacts.

Every flow has a corresponding division of

knowledge into subjects – the knowledge map. Still it

is not possible to tell what of the maps is more

adequate to the reality. Information and software

apparatus of the knowledge base should provide a

user with an opportunity to work with a multitude of

knowledge maps, giving preference to one or another,

i.e., to work with the multimap.

At the same time, we need a special knowledge

map, guiding the innovator in space of knowledge,

warning him of risky challenges and prioritizing him

in the direction where the vertical of knowledge is

filled with content or near to be filled, and, therefore,

the paradigm innovations are highly probable.

In order to manage such knowledge base, we

should use a flexible metadata base, built on the

grounds of noospheric concept (PIDev model, VIK

and parabola of knowledge). Moreover, to develop a

flexible infrastructure of data, we should use the

knowledge about sign constructions, gained as a

result of paradigm innovation.

This article defines the general architecture and

semantics of the noospheric innovation knowledge

base, which defines the semantic structure

(architecture) of its designating part (syntax). It

describes the architecture of the object (array of

knowledge). In the process of creating a knowledge

base, it is necessary to develop its pragmatics and,

accordingly, a pragmatic syntax which describes the

processes of processing (selection, input, structuring,

remembering, searching, assembling, displaying,

etc.) of data. In addition, it is necessary to determine

the structure of the syntactic syntax that reflects the

state of the object, to develop and implement the

organizational and software of the noospheric

knowledge base as an important component of the

enterprise innovation system.

ACKNOWLEDGEMENTS

The authors are thankful to Yuliya Davydova for her

significant assistance with the translation of the paper

into English.

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