The terms "Knowledge
Management" and "Knowledge Engineering" are always yet
slightly misleading in the subject matter literature. According to
the definition-making rules [Definition-Making
Paradigms] of the TOGA (Top-down Object-based Goal-oriented Approach) meta-theory,
their notions are based on the definitions of : knowledge,
management and engineering.
The definition-making remarks
The definiens
precision determines the precision of the definiendum,
therefore we have to focus on the simple, general and precise,
as possible, definitions, and on their
ontological contexts of
knowledge, management, engineering
concepts.
The first obstacle in such
task, consists the vagueness
of the terms: data, information, knowledge,
preferences, which are widely used in all human activities with numerous and metaphoric
meanings.
An example:
...Albrecht von Mueller differentiates
between data, information and knowledge, and makes
it clear that what we find in the Internet is information, not knowledge.
An healthy knowledge exchange is important for business, otherwise one
is quickly "brain-dead", says von Mueller.
..................
An interesting
but "classical" vision on the state of the art in the field of
Knowledge
Management you can find in the Special Issue of the
International
Journal of Human - Computer Studes (Eds. S.Decker, F.Mauer (1999)
51(3).
.............
A proposal of the most general systemic
definitions on the Web
According to the
TOGA (
Top-down Object-based Goal-oriented Approach) Standard
(since
1989) - initial intuitive syntetic
definitions of the ontological context of the knowledge concept
(see in parallel:
Reasoning Architecture Paradigm, URAP).:
Data:
everything what is/can be processed/transformed in computational and
mental processes
- This concept mainly belongs to the programmers' perspective/language.
Information:
data which represent a specific property of the domain of
human or artificial
agent's activity
(such as: addresses, tel.numbers,
encyclopedic, data, various lists of names
and results of measurements).
Knowledge: every abstract property of human/artificial agent which has
ability to process/transform
(quantitatively / qualitatively) information
into other information
or in another knowledge.
It can be: instructions, emergency procedures, exploitation/user
manuals, scientific
models and
theories.
Document:
passive carrier of different structures of knowledge, information and
preferences
in human
organizations, it can be physical or electronic.
Computer
Program: active carrier of different structures of knowledge with
incorporated information
and preferences, expressed in computer languages.
In order to
make active information and knowledge in a goal-oriented/driven
manner we need also preferences,
as a property of intelligent entity/agent. Preferences
do not depend on concrete states of intelligent entity's domain of
activity and they are neither information nor knowledge.
Therefore the conceptualization
"Data, Information, Knowledge" [see Google
search - 5750 docs! -8/8/2003] is not proper yet but, of course, it
is a subsequent step towards the basic and complete "Information,
Preferences, Knowledge" ( IPK ) conceptual cognitive framework
which was done in frame of the TOGA
meta-theory by A.M.Gadomski, 1989.
For example, lack of distinguishing
between information, knowledge and preferences makes weak the important
UK's report:
Long
Term Technology Review of the Science & Engineering Base.
|
For
more about IPK definitions: TOGA
ontological paradigms
(http://erg4146.casaccia.enea.it/wwwerg26701/gad-dict.htm).
|
Some preliminary ontological and epistemological remarks
on knowledge:
I and K are relative concepts and what is called knowledge can be
considered as information on higher, more abstract meta-activity level.
In the above context the difference between
Information Discovery
and Knowledge Discovery is essential.
Information discovery provides new Information about preselected concrete
Domain of Interest. It uses different domain knowledge.
Knowledge discovery produces new knowledge valid for the preselected
classes of the Domains. Knowledge discovery uses meta-knowledge.
Data mining is
a set of methods useful for knowledge and information
discovery and usually applied to large data bases. Frequently, they
are employed
to the obtaining/retrivial implicite K and I included in
different types of documents.
Information Retrivial Methods and Information Discovery are
considered subdomains
of the Information Management field and should
not be considered a domain of Knowledge Engineering.
It should be stressed that Knowledge Engineering is a domain of
engineering
where the specific domain knowledge is the object of operations
(it
is a domain
of activity of knowledge engineers).
Knowledge, as an abstract domain of activity, is a property of different
systems,
such as: human's, animal's and artificial minds, experts, distributed natural
and
artificial societies, human-machine aggregates, global systems, as well
as different
bases of normative and textual documents.
In general, knowledge have passive and active carriers [carrier
system relation in
the basic paper about TOGA
(pdf file),1994].
Of course, every engineering activity, independently on its domain of intervention
uses certain knowledge.
We have to recall yet that structural numerical data can be
information but
they are
not knowledge.
|
The above defined knowledge
and information conceptualisation is a part of
|
the
complete
IPK
(Information,
Preferences, Knowledge) conceptual pattern, called Universal
Reasoning Architecture
paradigm (URAP)
|
and the IPK architecture
of personoids
in frame of the TOGA
meta-theory.
---------------------
Critical comments
related to the state of the art
* Adam
Maria Gadomski, Paolo Manzelli,
Tower of Babel: Some Remarks
on Vulnerability of
Scientific and Technological Communication in Interdisciplinary Projects
(Challenges for
Science and Humanities), 14
Dec. 2005, ENEA
http://erg4146.casaccia.enea.it/Challenges-Humanities2005-pos7.pdf
*
I suggest to confront the IPK and TOGA
paradigms with the Principia
Cybernetica Web, as well as, with
numerous and continuously evolving structures and definitions of the BDI
model. In my opinion this last approach,
as well as, a recently emerging "Data, Information, Knowledge",
also tends to the IPK basic framework
.[Google search:
"BDI
model", "BDI agent", "BDI architecture", 18 Apr.2002 indicates 117
doc], more detailed
discussion of this problem is included in [Franco Pestili, Thesis.2000,
Rome University "La Sapienza": Un
Approccio alla Modellizzazione Cognitiva: Agente Cognitivo IPK.ppt
presentation]
The similar situation is with the "Data, Information, Knowledge"
conceptualization. Many authors work hardly in
order to re-invent new names for the TOGA's definitions of :
data, information, knowledge, belief, desire,
intention,
wisdom.
See for an illustration only:
Google search: - July 4,05, for information,
knowledge, preferences, data, 4.220.000 docs .
and also: "The Origin of the Data Information Knowledge Wisdom Hierarchy"
http://www-personal.si.umich.edu/~nsharma/dikw_origin.htm..,
unfortunately, for example, they are not orgins from
nuclear
and high-risk systems research prespectives.
--------------------- Last updating March, 2006 |
