06-11-2012, 03:50 PM
Knowledge Management and Information Systems
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INTRODUCTION
Information systems (IS) and management of knowledge are often discussed either as separate entities
or alternatively as analogies. But what is the gap between information processed with IS and human
information or knowledge? Is the gap insurmountable, or can the subspecies be analysed and selected,
so that section of these two sets will be found or so that the union of information and knowledge
complete each other?
IS and users share information, which is why it is in this context more important than data, as the
basis for systems, but human knowledge is the final aim. As a background there is a philosophical
classification of knowledge. Positivism, post-positivism and critical theory are briefly presented. This
presentation is assuming constructivism as the most appropriate viewpoint to knowledge.
There are various species of information, which are analysed more deeply. ICT consists of
information processing and communication technologies. From philosophy, there can be the same
main streams found. Information theory gives us quantitative classes based on probability. Semantics
leads us to qualitative information categories.
Information and communication theory live along with systems theory. Systems analysis is an
engineering discipline based on this theory of the nature of systems. This analysis framework for
studying and modifying the world is used for the examples about engineering, that are mainly from a
project WISE - Web-enabled Information Services for Engineering. WISE is concerned with
knowledge management (KM) in participatory design processes of complex products, putting the
engineer in the centre of the overall picture. Main objective of the project is not on developing new
specific KM tools and methods but rather to integrate and exploit existing state-of-the art approaches
oriented towards the needs of industrial users. This research project will prototype a meta-system for
different kinds of information sources. In this context, WISE can be an example to show what
findings are observed from engineering knowledge.
INFORMATION AND KNOWLEDGE
Most definitions of KM share the perspective of collection and dissemination of knowledge to benefit
organisation and its individuals. Typically knowledge is defined like ‘information that is relevant,
actionable, and based at least partially on experience’. We must take a look at paradigms of
philosophy and species of information to find out what is meaningful for KM.
Paradigms from Philosophy
The paradigms from philosophy can be distinguished by ontology, which (in philosophy) concerns
beliefs about the form and nature of reality, and epistemology, which concerns the nature of
knowledge and the relationship between those who know and knowing. Four main paradigms are 1)
positivism, 2) post-positivism, 3) critical theory with a) postmodernism and b) post-structuralism, and
4) constructivism.
For positivistic ontology the reality can be apprehended, and there are observer independent data as
facts. The positivistic epistemology is based on objectivity, a possibility to find universal truths.
Information Species based on Probability
As the data, information and knowledge are separated, the middle layer remains crucial. The
probability interpretation of information is giving us three categories of physical, syntactic and
semantic information.
Physical information is the orientation degree of systems, opposite to entropy. It is the common
denominator that can bring matter, energy and time into a single, unified framework of analysis. All
matter-energy transformations are change of state information. Animate and inanimate objects -
information condensations of matter-energy, e.g. DNA, atom, galaxy - are including the more
information the more complicated they are. Actually, it’s impossible to say confidently of anything that it could not be information. Physical information can further be classified as natural and manmade
artefacts.
Syntactic information is attached to communication in any channel where messages are sent and
received using some notation system. The amount of information is depending from the rarity of each
notation string. The theory of syntax is very close to the statistical-mathematical information theory.
However, when someone is creating or utilising syntactic information, there is always interpretation –
even with completely automatic IS.
DISCUSSION
ISs and KM are mainly based on other than constructivist philosophy. Tightly rationalistic KM is
epistemologically impoverished, seemingly oblivious to the thousands years of vigorous and not
concluded debate about the nature of human knowledge. Awareness of this is especially important
when discussing about ISs for KM.
The syntactic, pragmatic and data-derived information species are useful for conceptual clarification
of ICT. Information species based on probability are considered wrongly more crucial, although
human interpretation is always needed for knowledge. ISs are representing something, and
probabilities will be misleading without proper interpretation.
A categorisation of information is a presupposition for working IS. Usually KM systems are suffering
from the information overload lacking the utility value. Best practices and lessons learned are working
only with knowing the interpretation based on information species. It is often experienced error to
deliver irrelevant data as a repetition.