The terms included in this category represent different viewpoints, i.e. the human sciences perspective and the technical perspective. The different contexts and connotations of the terms should be taken into account when using the terminology.
Actor is someone
involved in individual or collective action, activity, with common or
conflicting interest related to this action.
The involvement is cognitive, motivational and emotional. Actors adopt strategies
to deal with the inequality of position within the social word. Knowledge is a key issue, as 1) an actor’s
position is related with access to knowledge and level of education 2)
mastering knowledge is a way to master uncertainty and provides power. Actors
are members of different communities,
and as such acquire and develop explicit
and tacit knowledge related to reasoning
as well as to behaviour, interaction habits and values. Any formal, abstract,
elicited knowledge is rooted in member-knowledge, also called “the natural
attitude”, allowing that the world of common sense is taken for granted, due to
unquestioned acceptance of the stock of knowledge at hand. NOTICE: This definition of
the actor is different from
the actor related to UML.
Articulation work: informal
but structured work practices that are not part of anybody's job description
but which are crucial for the collective functioning of the workplace, such as
regular but open-ended meetings without a specific agenda, informal
conversations, gossip, humour, storytelling, tacit
rules of coordination, bodily language etc. Cooperative work is necessarily
distributed, not only in time and space, but also because actors
are semi-autonomous in term of the evolving situations of work they face, as
well as in term of their strategies, heuristics, perspectives, goals, motives.
Articulation work deals with scheduling, aligning, meshing, integrating
individual and yet interdependent activities. Understanding the nature and
structure of invisible work is crucial to designing and managing organizations.
When organizations are restructured or when tools are implemented and work is
reorganized, invisible but valuable work is often eliminated. No one recognizes
that it is being done, or that it is of value, so the time and personnel it
requires are not allotted in new plans. [Schmidt 1991 a & 1991 b]
Data are a set or string of symbols that can be associated with structures
and behaviours. Symbols are not yet interpreted and they will become meaningful
only when related to a given context. They can be stored and, if storage is to
be meaningful or coherent, then within that context storage will occur
according to a set of criteria that are worldview derived. Data are a
symbolical reflection of numbers, quantities, magnitudes or other facts. They
are able to reflect variety differentiation in complexity. Stored data are also
retrievable according to the pattern of meaning created for them within the
context, e.g. by defining a set of entities that meaningfully relate to each
other. However, these facts or statements are raw material and have no tangible
meaning until used. Whilst knowledge management
experts tend to imply a distinction between information
and knowledge, they use the words
interchangeably -- even in the system
context.
Experience,
intuition and judgement belong to knowledge,
which is hence a product of information, experience, skills
and attitude. All knowledge is worldview local, and belief related. It can be
defined as patterns of meaning that can promote a theoretical or practical
understanding that enables the recognition of variety in complexity. These
patterns are often developed through a coalescing of information. If
information is seen as a set of coded events, then consistency occurs with the
definition that explicit knowledge is
codified. Experience is intuitive, tacit,
highly assimilated and complex understanding of the world. Experience is
related to direct action and rooted in inductive reasoning and comparison.
Experience can be associated with abstract and formal understanding. Going back
from integrated and embodied schemes to formal, explicit
knowledge is a long and difficult process that needs specific methods
and conditions involving other actors, reflexive
discussions and motivation.
Actors and knowledge evolve through experience. Experience related to an other object B modifies our previous knowledge of an object A. Experience dynamic do not achieve in the closure of a specific knowledge, but in opening new possibilities of experience. [Gadamer 1976] Organisational memory can select the kind of experience to be supported in order to build the knowledge needed for its adaptive functioning. It can also reduce the part of active experience to domains where predictive knowledge is not stabilized or not efficient enough. Experience always occurs under historical conditions. This historical context must be specified for both building and reuse of experience.
Explicit
Knowledge refers to knowledge that
is easy to document and to convert into procedures. Explicit knowledge can be
articulated easily and captured and shared via information technology. [EKMF]
Explicit knowledge can be expressed in language, and it can be processed,
transmitted and stored quite easily. [Nonaka & Takeuchi 1995]
Organizational, social explicit knowledge is more difficult to formalize, even
if such knowledge plays an important part in technical decision. This is mainly
due:
-
to the fact that most conflicts of interest are solved
through negotiation where each actor tries to
preserve or increase his influence and power on the situation,
- to the fact that conflicting views are kept tacit, even if most people know about them. For instance, there is sometimes contradiction between what designers need to know in order to understand previous technical choices, and what lawyers need to know when looking for attribution of responsibilities. Both technical and organizational explicit knowledge elicitation can be confronted with differences between standards and practices that are common knowledge but are kept tacit, even if they are helpful.
Information is interpreted data.
These (set of) signs or signals predispose an actor
- that is, an individual, or group of persons acting as a unit or organisation.
This is consistent with a definition that considers information to be a set of
coded events. Information generates from the comparison of data, which are
situationally structured in order to arrive at a message that is significant in
the given context. It can also be defined as that which enables a viewer to
perceive greater variety differentiation in a complex situation. Information is
relevant data for a specific purpose, and its meaning can be recognized. The
meaning is given in a specific situation for a specific human or organizational
entity through interpretation and selection of raw data. Knowledge
can be seen as an opportunity to give a meaning to data and to convert it into
information.
Knowledge. Information is one raw material of knowledge. Knowledge is often defined in relation to action, e.g. information transformed into capability for effective action. However, together with ability to do, knowledge is also just what we know. The use of information or an ability to perform a certain task depends also from other personal factors. Consequences of knowledge, like a capacity to perform a particular task, or a productive resource or factor in providing competitive advantage are totally short sighted and narrow substitutes for business purposes only. Experience, intuition and judgement belong to knowledge, which is hence a product of information, experience, skills and attitude. All knowledge is worldview local, and belief related. It can be defined as patterns of meaning that can promote a theoretical or practical understanding that enables the recognition of variety in complexity. These patterns are often developed through a coalescing of information. If information is seen as a set of coded events, then consistency occurs with the definition that explicit knowledge is codified.
Another view on the relationship between data, information and knowledge makes it a loop. Data and information are only two opposite ends on a continuum. Concentrating our attention to certain aspects of knowledge makes it focal. The focal knowledge can, sometimes and partially, be articulated and furnished with words and be referred to as information. If the information becomes too de-contextualised, i.e. too distant from the knowledge required to interpret it, it can be addressed as data. Since a piece of text itself is not sufficient to exhaustively describe the knowledge to which it refers, the reader's tacit knowledge must be compatible with that of the writer in order to interpret and fully comprehend the implications of the information. Hence, what one conceives as information another sees as data. [Stenmark 2002]. Adopting a social perspective on knowledge leads to different criteria, related to the level of stabilization. Knowledge is widely stabilized among many people through institutional procedures (school, university, skills institution etc.). Data is significant to a small amount of specialists. Information implies a continuous process of evaluation, interpretation more or less stabilized, built in order to cross boundaries.
Knowledge creation refers to a continuous, self-transcending process where the boundaries of the old self are crossed and one enters into a new self by acquiring a new context. Knowledge is created in the interactions between individuals and their environments, and there is a transition between the self and other. The process of knowledge creation consists of three elements: 1. The process in which knowledge is conversed through socialisation, externalisation, combination and internalisation from tacit to explicit and back to tacit (SECI process), 2. Ba, a shared context for knowledge creation. 3. Knowledge assets, i.e. the inputs, outputs and moderators of the knowledge creating process. All these elements have to be combined in order to create knowledge. [WISE D1.1; Nonaka & Takeuchi]
Knowledge representation refers to multidisciplinary subject that
applies the theories and techniques of logic, ontology
and computation. Knowledge representation is an
application of logic and ontology to the task of constructing computable models
for some domain. In knowledge representation symbols are used to surrogate for
objects in order to model the existing world, and it includes a set of
ontological commitments. Knowledge representation is a medium for efficient
computation as well as for human expression. The levels of knowledge
representation are implementational, logical, epistemological, conceptual and
linguistic. [Sowa] Knowledge representation can also be defined as a set of semantic and syntactic rules to describe objects
and facts. [WISE D1.1] Knowledge representation refers also to the need of
stabilizing a shared understanding of the relation established between an
object of the word and a sign that will stand for it, because this relation
always is arbitrary, contextual and situated. When it comes to represent
constituted knowledge, many codes, specialised vocabulary and graphic are
available, as well as documents template related to the internal logic of a
community. Technical objects represent knowledge as well [Poitou 1997]. From
the users point of view,
sticking to stabilized representations makes knowledge more easily manipulated.
But the need for digitalised data to be processed by computer leads to the
building of abstract models, which are sometimes claimed to be helpful to push actors to elicit tacit
knowledge. Knowledge representation should take both aspects into
account.
Knowledge sharing refers to the
beginning of the knowledge creation
process, where tacit knowledge is shared
among the individuals through socialisation. Typically this happens in a team
with members from various backgrounds. Tacit knowledge can only be shared
through shared experience. Apprenticeship is a
common way of sharing (tacit) knowledge. [Nonaka & Takeuchi] Knowledge is
“naturally” shared through any social activity, within numerous communities of
practice. When it comes to share knowledge between different
communities, a certain degree of formalisation and decontextualisation occurs.
This both allows generalization and opens misunderstanding and inconsistency.
Sharing knowledge thus means 1) being able to relate a specific knowledge to a
specific context to be described, 2) provide the rationale for the categories
used when doing so 3) making sure that the same categories are available for
people willing to share, and collecting their own categories if necessary 4)
discussing the problematic points and making this public. Knowledge sharing is
strongly constraint by the dynamic of the inscription supports (books are
easier to exchange than obelisks, people stick to paper they can annotate when
digital up-dated documents are “closed” to readers’ individual/shared comments
etc).
Learning refers to an intentional activity of
acquiring knowledge. As a result, there is a
change in understanding, decision and action. The knowledge may be gained
through studying, by experience or be being
taught. [EKMF] See eLearning
and Community of
Practice.
Memory refers to the capacity for retaining, perpetuating, or reviving the thought of things past, skills, and knowledge. [OED]
Organizational Memory Walsh and Ungson,
applying an organizational memory information processing view, refer to OM
"in its most basic sense" as the stored information
(from an organization's history) that can be brought to bear on present
decisions. They define that this information is stored as a consequence of
implementing decisions to which they refer, by individual recollections, and
through shared interpretations... information can be considered as decisional
stimuli and responses that are preserved in particular storage bins and that
have behavioural consequences retrieved. [Walsh and Ungson 1997, p. 181.]
Besides the contents of organizational memory, the definition of Walsh and
Ungson also refers to organizational memory retention media (bins, in their
terms) and processes that handle memory contents, mostly for decisional reasons.
Memory media (for retention, processing, and transfer) and organizational
processes together are the memory means in an organization. [Wijnhoven 1998, p.
31.] The other most referenced authors, Stein and Zwass [1995, p. 89], define
organizational memory as "the means by which knowledge
from the past is brought to bear on present activities, thus resulting in
higher or lower levels of organizational effectiveness". However, there
are some similarities in those definitions.
Both [Walsh & Ungson and Stein & Zwass] definitions incorporate three "building block" concepts [Mandviwalla et al. 1998, p. 2]:
·
sequentiality - the idea that information has a
time-line - an age - so that it can be distinguished from current and new information
·
storage and retrieval - including the capture,
recording, representation, and
retrieval of information
·
action - the use of the information to perform a
relevant action such as making a decision.
In addition, the above (and other) definitions
also seek to qualitatively differentiate information - often as data versus
knowledge. Organisational memory refers to a largely form of collective
competence, the know-how of an organisations people and systems taken together. The
information an organisation needs to keep for re-use. [EKMF] Project memory
refers to the capitalisation of lessons learned
and experiences from a given project. Also
project definition, activities, history and results. [EKMF]
Metadata is descriptive and classifying information about an object. It describes
certain important characteristics of its target in a compact form. Metadata
plays a central role in improving searching and categorising objects within a
defined context of use.
Metadata can also be defined as data about data. In
this sense metadata describes a data set and the format of this data. In
addition, metadata can be described by a set of meta-metadata. Meta-metadata is
descriptive information on the metadata record itself.
Meta-knowledge may be loosely defined as
"knowledge about knowledge". It is
therefore depending from the definition of knowledge. In AI and other system context, meta-knowledge
includes information about the knowledge the
system possesses, about the efficiency of certain methods used by the system,
the categorisation schemes used [Simone & Sarini 2001; Schmidt & Wagner
2001], the probabilities of the success of past plans, etc. The meta-knowledge
is generally used to guide future planning or execution phases of a system.
Meta-knowledge research has also been closely associated with, for example, the
ability of expert systems to explain lines of
reasoning, acquire knowledge automatically and to reason strategically.
More generally, meta-knowledge is concerned with the extent and origin of
our knowledge of particular subject sets and about the reliability and relative
importance of certain information and facts about the domain or world in which
we are involved. Meta-knowledge is also concerned with performance: our
strengths, weaknesses, vulnerabilities, levels of know-how in different
domains, and perceptions of our own abilities to innovate, replicate and
progress. So meta-knowledge is not only an abstraction of information but also
includes factors such as: meta-level reasoning, reasoning by analogy, reasoning
procedurally, formal reasoning, generalization and, of course, abstraction.
[Knowledge Management Think Tank]
Ontology is an explicit conceptualisation of a
model, comprised of objects, their definitions and relationships among these
objects. The ontology is formed by an agreement on a conceptualisation shared
by a community. [EKMF] Ontology is a study of the categories of things that
exist or may exist in some domain. [Sowa]
In philosophy, ontology is a theory about the nature of existence, of what
types of things exist; ontology is a discipline studies such theories.
In computer science and engineering, an ontology means a set of formally
specified metadata structures consisting of
commonly agreed concepts that bear a limited sense of meaning with them. An
ontology is an explicit specification
of a domain conceptualisation. An ontology can also contain inference rules and
logic. It enables both people and computers to understand things, because all
terms have been explicitly defined and assumptions clearly written down.
Semantics refer to signification or meaning.
Semantics is the branch of semiotics (the philosophy of signs) that deals with
meaning. The other two branches of semiotics are syntax and pragmatics.
Semantics is basically the study of the relationship between what an object
represents and the object itself.
Skill enables a person to act by adding value to information. Skill is related to embedded and
embodied practices, and relies on the acquisition, by imitation and systematic
experiment, of complex schemes. Skill also refers to responsible and validated
know-how. [EKMF] Skill can also be defined as a capability of accomplishing
something with precision and certainty; practical knowledge
in combination with ability; cleverness, expertise. Also, an
ability to perform a function; acquired or learnt
with practice. [OED]
Tacit refers to something not openly expressed or
stated, but implied; understood, inferred. Also unspoken, unvoiced; silent,
emitting no sound; noiseless, wordless. [OED] Latin tacitus, silent, past
participle of tacre, to be silent. Done or made in silence; implied, but not
expressed; silent; as, tacit consent is consent by silence, or by not
interposing an objection. Not spoken. Implied by or inferred from actions or
statements. Indicated by necessary connotation though not expressed directly;
"gave silent consent"; "a tacit agreement"; "the
understood provisos of a custody agreement". Synonyms: implied, silent,
understood. [Webster; AHD; WordNet]
Tacit Knowledge refers to knowledge
deeply embedded into a person’s consciousness. The term was coined by Michael
Polanyi [1958/1974] to express the idea that certain cognitive processes and/or
behaviours are under girded by operations inaccessible to consciousness. As he
put it "we can know more than we can tell" [1966/1997, p. 136]. Tacit knowledge evolves from expert resources and it
enables us to perform actions without being able to fully explain. [EKMF] Tacit
knowledge is personal and context-specific by nature, and it is therefore
difficult to communicate and formalise. Tacit knowledge is tightly rooted in
action, tools and procedures. Also subjective insights and beliefs are
categorised as tacit knowledge. [Nonaka & Takeuchi 1995] Tsoukas [1996],
building on Polanyi, argues that tacit knowledge is inseparable from explicit knowledge since "[t]acit
knowledge is the necessary component of all knowledge"
[ibid., p. 14]. All articulated
knowledge is based on an unarticulated and tacitly accepted background of
social practices.
Taxonomy refers to a systematic classification
of anything. [OED] Taxonomy is
also the study of general principles of classification and relationships in a system, and represents these as
generalizations of that system. Taxonomy is a classification of organisms in an
ordered system that indicates natural relationships. [AHD]