Meta-Knowledge Engineering and Management Server, ENEA

Proposal of Meta-Ontological Assumptions
as  Paradigms for  Knowledge and 
Socio-Cognitive Engineering
[Adam Maria Gadomski,, the page since 1999 ]

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. 

    Documentpassive 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

     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.

A TOGA vision: One of the basic functions of knowledge in the information/knowledge  society is to produce servicies.
In this perspective we assume that many different socio-cognitive business preferences systems activate
social, organizational and individuals 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

   *   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", unfortunately, for example, they are not orgins from
     nuclear  and high-risk systems research prespectives.

--------------------- Last updating March, 2006

  [Meta-knowledge Engineering & Management Server]      [Adam Maria Gadomski]    [ HID Research Group]   [ENEA]