On a New Powerful Model for Knowledge Management and its
(Graz University of Technology, Graz/Austria
Know Center Graz/Austria
(Know Center Graz/Austria
Abstract: In this paper we present the MaurerTochtermann
Model for Knowledge Management (KM) and present strong evidence that this
model has powerful ramification. First, it shows clearly that KM is not
just "old wine in new bottles" but an important and new area
of research and applications; second, it shows clearly where KM differs
from classical distributed information systems or data bases; third, it
is shown to embrace a number of pragmatic problems that have often been
considered the heart of KM; and fourth, it gives a clear indication of
the areas that will be of increasing importance for KM in the future. We
claim that the model can and should be the basis of future efforts in IT
Keywords: Knowledge Management, Information Systems, Data Bases,
Document Management, Intelligent Agents
Categories: H.1, H.2, H.4
After having been a very scientific discipline for many years, Knowledge
Management (KM) has become focal point of much research, applications and
commercial interest since about 1998. However, there is much discrepancy
on what KM really is: attempted definitions have sometimes stressed organisational
components behind KM with little or no emphasis on information technology,
others see KM mainly as a way to measure the value of the "human component"
in an organisation, i.e. see knowledge assessment as central issue, and
a third school of thought sees information technology as the central aspect
of KM. Even those who emphasize IT end up with a range of attempted definitions.
Thus, there is no uniform agreement on what really constitutes KM, as can
e.g. be seen from the different approaches taken in publications such as
[Woods and Sheina 98], [Studer
et al. 99], [Karagiannis and Telesko 01], [Davenport
and Prusak 98], [Sivan 99], and [Ives
et al. 98]. This of course implies that there is no universally accepted
definition. Worse, most definitions, if at all given, are rather wishywashy.
1 A preliminary
version of this paper was prepared in German for a keynote address at the
Bielefeld Conference, Feb. 56, 2002.
It is the claim of this paper that another approach is more illuminating
than most previous attempts: based on what are often considered the main
issues of KM the MaurerTochtermann Model for KM is presented: it is
shown to clarify many open issues; it demonstrates impressively in which
way KM goes beyond traditional approaches to information management in
information systems or data bases; and it makes it clear what (substantial)
agendas still have to be resolved as KM is getting more and more powerful.
Lest we mislead readers of this paper we want to clarify our position
concerning KM: we think that in actual applications organisational aspects
of KM play a large role: they will thus not be ignored in our model, yet
we do not delve into any details in this paper; we feel that knowledge
assessment (KA) is a topic outside KM: results of KA may influence KM and
conversely. However, we do not treat in our model KA as integral component
of KM. Thus, the MaurerTochtermann Model (MTModel for short)
of KM is, on purpose, IT centric.
One further point is worth mentioning: we believe terms such as "KM"
and "KMSystems" are hard to define in detail, since even
the term "knowledge" is very elusive. The distinction made between
data, information and knowledge is often fairly artificial, fuzzy or both.
After all, when we give you the information that the Knowledge Management
Center (KnowCenter) in Graz was conceived in 1998, you now know
(although "2000" is really a simple dataitem) that
ideas on KM have been around for quite some time: this seems to indicate
that a possible definition of "knowledge" could be "information
in context" or "structured information". Clearly, this is
not terribly satisfactory either: we all know that "knowledge"
is more than this. However, this paragraph and the play with words shows
that since "knowledge" is already hard to define, how can we
expect to really nail down things like "KM" or "KMSystems"?
It also shows that as first approximation equating knowledge with linked
and structured information is at least a good crutch. Hence we will keep
this idea in the background of our minds throughout.
The main part of this paper is structured as follows: in the next Section
2 we present pragmatic situations showing what KM is all about; in
Section 3 we present the MaurerTochtermann Model
(MTModel), followed by core techniques of KM in Section
4: those techniques shed further light on the MTModel. Section
5 gives a brief summary, followed by a number of references in Section
2 Pragmatic Starting Points for KM
The frustrated statement of some managers: " If our employees only
knew what our employees know, we would be a perfect company" illuminates
one of the central aspects of KM: a group of persons always knows more
than any single individual, and even those individuals having similar knowledge
may use and view their knowledge in rather different ways. The challenge
that derives from above statement is clear: how is it possible to get at
least part of the knowledge residing in the brains of people into some
kind of networked computer system, subject to two major constraints: first,
to get the knowledge out of the brains should not create more than at most
a very modest effort for the persons involved; second, the knowledge should
be made available to others "actively" when they need it, without
requiring an explicit request for the knowledge: this latter constraint
is clearly important, since persons will often not know that such knowledge
exists, and hence never search for it.
Suppose for a moment that some organisation is capable of achieving
above kind of KM: the benefits would clearly be enormous. It would:
- avoid duplication of work
- support collaboration between persons
- avoid loss of knowledge if some person becomes unavailable
- ease the training of new employees
- let people learn from other persons' successes or failures
It will be shown later that techniques to achieve at least part of the
above aims do already exist, and are actually built into modern KM systems
such as Hyperwave [Maurer 96], [Hyperwave],
[Hyperwave 99]. We will call this kind of KM "KM
for Organisations" in what follows. Note also that in many cases much
reduced scenarios can already be very useful: for instance, it may not
be necessary or feasible that all employees of a company know everything,
yet an employee working on some topic x should at least be able to find
out whether some other employee has already experience with x. This kind
of KM is clearly much less ambitious, has been realized in a number of
contexts, and solutions are often referred to as defining "Knowledge
Domains" [Helic et al. 01] or "Yellow Pages".
Conversely, it is conceivable that KM goes far beyond the boundaries
of a company or organisation, but applies to much of our society: combined
with ubiquitous computing it is not totally unrealistic that much of mankind's
knowledge is available to everyone at some stage in the future. Putting
it differently, the knowledge outside each individual's brain is of course
much larger than in the brain of any individual: this knowledge outside
may one day become a veritable extension of the human brain using sophisticated
future techniques of KM. Why and how this might work is e.g. discussed
in [Maurer 01].
There is another pragmatic approach to KM that is based also on a certain
amount of frustration, but in a different environment: anyone who has ever
had to deal with large amounts of data coming from many heterogeneous sources
will have sighed more than once: "If we were able to somehow automatically
classify and associate incoming information with existing material we would
finally have solved the problem of information archival." We will
call this problem "KM for Archives".
Let us have a brief look at two examples to explain the situation:
In the Journal of Universal Computer Science, J.UCS, [J.UCS],
[Krottmaier and Maurer 01] papers are not just classified
according to the ACM system and hence allow complex queries, but also provide
for "links into the future". This term refers to the following
fact: if a contribution A was written e.g. in 1955 and a new paper B in,
say, 2002, refers to A, then in any digital library [C.ACM
98] B will clearly have a link to A, i.e. a "link into the past".
In J.UCS, however, a link from A to B is added, thus providing a "link
into the future", specifically from 1955 to 2002. Such new types of
links (that will clearly ease finding the most recent paper on a certain
topic) can be generated in a digital library fairly easily if literature
references follow a well defined format. However, even in the absence of
a standard for references they can be generated, and generated beyond the
boundaries of one digital library using tools such as the Citation Index.
Techniques like "similarity recognition" or "recognition
of connections" as will be discussed later will provide still more
powerful ways to add "links to the future".
Another example is the electronic version of the largest German encyclopaedia,
the Multimedia Brockhaus Premium [Brockhaus 01].
For each contribution a "knowledge net" showing related contributions
(and displayed in a graphical fashion) is generated automatically. This
is done using the already mentioned "recognition of similarities",
and is supported by fine grained metadata [Duval 01]:
each entry is associated with one or more categories thus avoiding cross
links between contributions that do not at all belong together. Indeed
the "knowledge net" will be generated by 2003 based on a complete
ontology [Uschold and Gruninger 96] of the German
language, making crosslinking still more precise.
As will become clear in the next section, "KM for Archives"
can be considered as a subset of "KM for Organisations". For
this reason, we will concentrate only on the latter in what follows.
3 The MaurerTochtermann Model (MTModel)
for KM for Organisation
The MaurerTochtermann Model (MTModel) for KM, as shown in
Figure 1, has been introduced in similar fashion as "communication
model" by the authors before [Tochtermann and Maurer
2000]. Work with it has now matured to a point that it is worthwhile
to present the final version.
Figure 1: The MTModel for KM
Figure 1 shows a group of persons who exchange knowledge with each other.
A large amount of this exchange uses a networked computer system (and hence
enables asynchronous use).
The arrows labelled 1 through 7 have each a special significance, and
indeed represent the core ideas of the model.
We start with a rough discussion of the meaning of the arrows and go
into more detail, afterwards.
Arrow 1 indicates that persons can communicate directly (in coffeebreaks,
at an office party, through a telephone call, ...):
It is this arrow 1 that subsumes all organisational aspects of KM, and
there are indeed many of them. In this paper and this model the fact that
we leave a "slot open" for the rich material on organisational
aspects of KM will have to suffice.
Arrows 2 to 4 symbolize the various ways in which information (knowledge)
can enter the KMSystem. In contrast, arrows 5 and 6 indicate that
there are two very distinct ways how knowledge can pass from the system
to users. To be more specific, arrow 2 stands for the explicit input of
information into the KMSystem, much as data is usually entered into
an information system. Arrow 3, however, symbolizes the implicit input
of information into the KMSystem: information and knowledge is entered
into the system as byproduct of activities users would be doing, anyway:
here, new knowledge is created without burdening the user, a very important
aspect. Arrow 4 indicates that a KMknowledge by observing users. Arrow
5 symbolizes the traditional query as used in classical information systems
or data bases: users formulate such queries in some way (by using a query
language, by filling out a form, by clicking on a succession of links,...)
and obtain chunks of information from the KMSystem: such chunks can
be small pieces of data, or large coherent documents like manuals, books
or even courseware. Arrow 6 is more unusual: it indicates that the system
can generate and offer knowledge without being explicitly asked by the
user. Arrow 7 symbolizes the fact that a good KMSystem is able to
generate new knowledge based on existing one.
Figure 1 shows very clearly the difference between
classical information systems (data bases) and KMSystems: if we omit
arrows 3, 4, 6 and 7 in Figure 1 we have a classical
information system! Thus, KMSystems go beyond traditional systems
if the actions indicated by arrows 3, 4, 6 and 7 can indeed be implemented.
We will prove that this is indeed true in the next section using concrete
Figure 1, a model for KMSystems for organisations
is also valid for "KM for Archives" if we just ignore arrows
1 and 3 in Figure 1. Thus, our earlier claim that it
suffices to study "KM for Organisations" is justified: indeed,
this is not very surprising: after all, KM for any organisation will involve
a substantial body of material that could well be called a (specific) digital
library or digital archive.
4 Some Current Techniques in KM
In this section we indicate how the "critical" arrows 3, 4,
6 and 7 that distinguish a KMSystem form classical information systems
can be realized.
Arrow 3 in Figure 1 symbolizes the implicit input
of information, i.e. the generation of new knowledge as byproduct
of actions that would occur, anyway. The list of such actions is quite
lengthy, and a few examples will have to suffice. There are simple actions
like sending an announcement of some event to a group of persons by email.
Performing such an action in conjunction with a KMSystem will send
the announcement also to the system, into a folder "Upcoming events",
open to the public and sorted by date.
When an event is over (it has its enddate as default expiration
date as "metadata") the announcement is shifted into a folder
such as "Past events of year xxxx", and at year's end is moved
again to a new folder, e.g. "Events of the last ten years", a
list that may well come in handy for a yearly or a tenyear report!
What has been said for the announcement of events holds, of course, for
all information that is put on a Webserver of the organisation, from
telephonedirectories, to the structure of the organisation at issue,
to the tasks of various subgroups, etc. Note that no sizeable organisation
can live without ISO 9000 certification anymore, today. To remain certified,
extensive documentation about each project, persons, resources and tools
involved, the timelines, milestones, documentations, minutes of meetings,
etc. etc. have to be gathered, anyway: all this must become part of the
KMSystems. Such information, properly interlinked (see below) presents
valuable knowledge about the organisation and projects carried out. It
also provides much insight in general, e.g. by allowing to determine why
past projects worked out well or ran into difficulties. The workflow
of an organisation is also available in electronic form today in most organisation
and should be integrated into the KMSystem. The same is true of yearly
reports, of lists of products with description and pricing information
(various types of information accessible only with appropriate authorisation),
manuals and other internal reports. Most important, existing information
systems and data bases have to be integrated, leading to socalled
knowledge portals [Hyperwave 99]. Email should also
be administered in the KMSystem centrally, clearly with suitable authorization
techniques, rather than being handled by each user separately. Why it is
important to bring all this information together will become clear in what
follows: only if we have a substantial body of information does it make
sense that KMSystems create automatic linkages between pieces of information
and classify documents according to potentially a multitude of views. This
generates structured information and hence knowledge according to what
was said in Section 1.
Arrow 4, symbolizing systemic generation of knowledge deduced from observing
behavioural patterns is currently probably the weakest part of all KMSystems.
The basic idea is that inputs, coming from specific sources (data bases
or employees) allow the derivation of general rules and procedures that
can be made available in similar situations. Such rules are often intertwined
with actions symbolized by arrow 5 (explicit queries): a KMSystem
will e.g. realize that certain search paths are used by some persons over
and over again, and hence might provide abbreviations, generate bookmarks
automatically, or note that when information x is retrieved, often y is
also of interest. As a consequence, a user retrieving x is automatically
offered (in the sense of arrow 6) the item y. Observe that many current
software packages, including Winword which the authors are using right
now, help automatically in many situations (sometimes to the chagrin of
authors who wish they could temporarily turn off this function): this approach
corresponds to arrow 6, and to arrow 4, if the system is actually trying
to learn from user behaviour.
To be able to better explain arrows 6 and 7 it is convenient to mention
a few of the techniques that are essential components of a KMSystem.
One of the more important concepts in this connection is the notion
of "active document" introduced by the first author some time
ago. The idea behind this concept is this: whenever a user sees a document
on the screen, the user can ask an arbitrary question (e.g. by typing it,
or maybe even by using a microphone!) and the system provides the answer
Of course it is impossible that any KMSystem can answer any question
formulated. However, there are two approaches that deliver, from a pragmatic
point of view, results that are fairly close to what has been specified.
First, it is sometimes possible to convert a question into a data base
query and have the data base answer it. We will not go deeper into this
interesting area of "natural language data base queries" here
but rather point out the second less wellknown technique: if a question
is asked more than once for a particular document it can be answered the
first time by an expert, possibly asynchronously. The question and answer
are stored in the KMSystem. If a semantically equivalent question
is asked later, the KMSystem has "only" to determine this
equivalence to present the answer. This approach has proven particularly
useful if a document remains "stable" over a reasonably long
period and is used by many persons (this is e.g. true of some manuals,
general information and often courseware!).
The problem with this approach is clearly how to recognize that two
pieces of text x and y are "semantically equivalent", i.e. in
our case represent the same question. There are many approaches to this
One is just to compare the frequency of words in the two documents at
hand: this technique can be improved by excluding trivial words (like "the"
or "and"), by using stemming to reduce nouns to first case singular
and verbs to their infinitive, by using synonymdictionaries, or by
even using semantic nets [Meersman et al. 99] and
ontologies. If it looks as if the question x now asked means the same as
a former question y, the user is presented with question y and a text such
as "Is this what you mean?" If the answer of the user is affirmative,
the system presents the answer previously given to y. If the answer is
negative, and another previously asked question z also looks similar, the
process is repeated with z, and so forth, until alternatives run out. At
that point the user gets feedback of the type: "This is a very good
question. You will receive the answer by email as soon as possible".
In this case, an expert will answer the question (potentially some time
later), and of course this new question x and the answer are now also entered
into the data base for future use. Putting it differently, this approach
employs the intelligence of the user to make the final decision whether
two questions are indeed identical in meaning or not. The method can be
further refined by using approaches from artificial intelligence such as
case based reasoning, but none of those have had a wider impact, so far.
Of course it would be more elegant if one were able to prove
that two pieces of text x and y are semantically equivalent: however, with
current day and foreseeable techniques this is not possible in general.
It is feasible only if the area of discourse and the syntax of how the
text is written are severely restricted [Heinrich and
However, there is one other very simple approach for providing for of
active documents. It could be called "local FAQ's". More specifically,
if a user has a question concerning a document currently on the screen
of the computer the user marks the sentence, formula, graph or whatever
at issue and types a question. This question is sent by email to an expert
(including the document at issue to make it easy for the expert to answer),
and the expert sends the answer back: during "office hours" immediately,
else at some later stage. The main point is, however, that the area highlighted
by the person asking a question will now be preceded by a special icon
that indicates to later readers "someone has asked questions here
and expert answers are available." Thus, a user coming later and having
a query concerning the highlighted material will first click at the icon
mentioned to see the questionanswer dialogues that have taken place.
Only if the question of interest is not among the ones posed before,
does this new user also type a question, thereby increasing the number
of questions and answers attached to this particular area of the document.
Note that such "local FAQ lists" will never get long for two
reasons: first, there won't be that many questions concerning a small fragment
of a document; second, if many are asked, the system hopefully reports
this to the author since such a large number of questions indicates that
something is at miss with the explanations given. Putting it differently,
this technique is an easy solution for active document and also provides
excellent feedback for authors. Note that this method will not work if
documents are very much time dependent (hence each questionanswer
dialogue may have an expiration date attached to it.). Observe further
that this approach is particularly useful if very large numbers of persons
are going to read the same document. In one of the first applications of
this technique a company with 150.000 employees provided extensive information
and learning resources for everyone. The local FAQ's generated "stabilized"
very rapidly: after the first 600 persons had read the material only 0.03%
of the other employees posed new questions. As a result, experts were made
available initially around the clock to answer queries with little delay
until the first 600 employees had worked through the material. Of the remaining
other 149.400 employees (more than 99.6% of all employees) only 45 asked
new questions, justifying "time delayed" answers in those few
Above discussion should make it clear that no good KMSystem can
do without active documents. Hyperwave [Hyperwave] was
the first system providing this feature, and has continued to improve it.
In connection with active documents we have already encountered the
problem of determining whether two pieces of text or documents x and y
are similar. A number of techniques to test documents for similarity are
available, most based on an extension of the idea of checking for important
identical words in both documents as mentioned above. Such methods allow
KMSystems to automatically classify documents (in the sense of arrow
7 in Figure 1), but also to actively notify users (in
the sense of arrow 6 in Figure 1) about suspected similarities.
This gives rise to many practical applications, as the four concrete examples
that follow will show.
Example 1: Let us consider a large distributed company working on a
myriad of development projects. If a new project is started in location
A a document outlining the project as required by ISO 9000 standard will
be prepared. This is automatically translated by the KMSystem from
the native language into (passable) English. This English version is compared
with all other project descriptions in the company. If a similarity is
discovered with a project carried out at location B, both A and B (and
often also a supervisory agency) are notified by the system that some duplication
of work might be about to happen. Of course the KMSystem may be wrong
in the sense that the similarities are irrelevant. Still, if only a small
number of project duplications are avoided the gain is significant.
Example 2: In a company with sizeable research departments an employee
A enters a new paper into the company's digital library. Almost instantaneously
A obtains the information that there are already two similar documents
in the library, authored by B and C. At the same time B and C obtain information
that a contribution similar to what they have written before has been entered
into the digital library by A.
Like in Example 1, this kind of approach minimizes the danger of duplication
and fosters collaboration. Ideally, the search for similar documents is
not done just in the company's digital library but also in other digital
libraries accessible via the Internet, such as e.g. J.UCS [J.UCS],
[Krottmaier and Maurer 01].
Example 3: The KMSystem checks all "nonprivate"
emails (or all emails accessible to some group of persons) to discover
similarities and notifies persons accordingly. This very important application
requires fairly sophisticated deliberations concerning privacy and authorization!
Example 4: In a discussion forum some topic is started. The KMSystem
realizes that this topic has been discussed extensively before. It avoids
an entirely new and repetitive discussion by pointing out the contributions
in the forum made earlier.
Summarizing, the use of similarities of documents is one of the most
powerful tools available in today's KMSystems. Similarities and connections
(see below) can often be shown graphically in a very intuitive way. Such
a representation is often called a "knowledgenet" and is
used extensively at various levels of granularity in e.g. the electronic
encyclopaedia Brockhaus. Figure 2 shows such a knowledgenet at the
level of course granularity as is generated automatically for each entry
as mentioned earlier. In the example the knowledge net is shown for "Raumsonde"
(i.e. "spaceprobe"). It could be refined (more entries generated)
by clicking at the button "ERWEITERN".
Figure 2: A small knowledgenet
A more complex variant of similarity recognition is what is sometimes
called "connection recognition". Techniques for this are not
generic enough at this time to be useable in general, hence they have to
be customized for specific situations. Let us explain the basic idea by
again using an example.
Suppose we have a very large collection of documents, e.g. all publicly
accessible documents on WWW servers and data bases, including electronic
newspapers, reports by news agencies, etc. The problem to be solved is
to find out persons who are likely to have been in contact with some other
If the KMSystem finds somewhere "X stayed in Nassau October
15, 2000", this fact is entered into the "recognition data base"
as e.g. (X, 15/10/2000, Nassau). When analysing the entry "Maurer
made vacation on North Eluthera October 10 20, 2000" the KMSystem
does the following: (a) it recognizes "Maurer" as name of a person
putting (Maurer, 1020/10/2000, North Eluthera) into the recognition
database; (b) it compares all entries in that data base with the currently
Since October 1020, 2000 overlaps with the date October 15, 2000
and since "Nassau" is recognized as capital of the Bahamas and
North Eluthera as one of the islands of the Bahamas, there is a chance
that X and Maurer have met. As a consequence something like (X, Maurer,
1) is entered into the "Xconnection database" indicating
that so far there is one indicator that X and Maurer have some connection.
If the KMSystem finds "X has met the person Z" and "Maurer
and Z went to school together" in potentially completely different
documents at some later stage, the triple (X, Maurer, 1) in the Xconnection
database is replaced by (X, Maurer, 2). If the third component of this
triple, the "counter of indicators", reaches some threshold,
i.e. 100, i.e. the triple turns into (X, Maurer, 100) the system sends
out an alarm: it is now very likely, that X and Maurer have some connection:
this alarm is a typical action corresponding to arrow 6 in Figure
1. The computation of the triples mentioned corresponds to arrow 7.
It should be clear that the establishment of connections even in the
example described is fairly complex. However, such techniques have proved
invaluable in the past in the case of e.g. tracking down criminal activities.
There are a number of much simpler applications, e.g. in connection
with eCommerce. A typical example is to use the shopping habits of
two persons A and B to find out that they have similar habits concerning
books. If A buys a book from a new author, and shortly thereafter another
book from the same author, the system would guess that A likes this author:
this causes the system to point out the author at issue also to B.
Much research in this area of connection recognition is still necessary
and going on. However, it is clear by now that connection recognition will
be one of the major tools that must be supported or supportable by any
The MaurerTochtermann Model (MTModel) that has been presented
in this paper shows very clearly where KMSystems differ from traditional
systems and shows where progress has been made, and further progress is
essential. The functions described that a KMSystem has to satisfy,
like active documents, similarity recognition, knowledgenets, etc.
are not science fiction features, but available in uptodate KMSystems
such as Hyperwave [Maurer 96], [Hyperwave].
[Brockhaus 01] Der Brockhaus Multimedial 2002
Premium; DVD, Brockhaus Verlag, Mannheim (2001).
[C.ACM 98] Communication of the ACM: Special Issues
on Digital Libraries: 38, 4 (1995); 41, 4 (1998); 44, 5 (2001).
[Davenport and Prusak 98] Davenport, T., Prusak,
L.: Working Knowledge: How Organizations Manage What They Know; Harvard
Business School Press, Boston (1998)
[Duval 01] Duval, E.: Metadata Standards: What,
Who & Why; (eds. Tochtermann, K., Maurer, H.) Proceedings of Int. Conference
on Knowledge Management IKNOW 01, Springer Pub. (2001), 137147.
See also http://www.jucs.org/jucs_7_7/metadata_standards_what_who.
[Heinrich and Maurer 2000] Heinrich, E., Maurer,
H.: Active Documents: Concept, Implementation and Applications; Journal
of Universal Computer Science 6, 12 (2000), 11971202. See also: http://www.jucs.org/jucs_6_12/active_documents_concept_implementation.
[Helic et al. 01] Helic, D., Maurer, H., Scerbakov,
N. : Knowledge Domains: A Global Structuring Mechanism for Learning Resources
in WBT Systems; Proceedings of WEBNET 2001, AACE, Charlottesville, USA
[Hyperwave 99] Hyperwave: Hyperwave Information PortalWhite
[Ives et al. 98] Ives, W., Torrey, B., Gordon,
C.: Knowledge Management: An emerging discipline with a long history. Journal
of Knowledge Management 1, 4 (1998), 269274.
[Karagiannis and Telesko 01] Karagiannis, D., Telesko,
R.:Wissensmanagement Konzepte der Künstlichen Intelligenz und
des Softcomputing; Lehrbücher Wirtschaftsinformatik, Oldenbourg Verlag
[Krottmaier and Maurer 01] Krottmaier, H., Maurer,
H.: Transclusions in the 21st Century; Journal of Universal Computer Science
7, 12 (2001), 11251136. See also: http://www.jucs.org/jucs_7_12/transclusions_in_the_21st.
[Maurer 96] Maurer, H. (Ed.), HyperWave: The Next
Generation Web Solution; AddisonWesley Longman, London (1996).
[Maurer 01] Maurer, H.: Die (Informatik) Welt in
100 Jahren; Informatik Spektrum, Springer Verlag, (April 2001), 6570.
[Meersman et al. 99] Meersman, R., Tari, Z., Stevens,
S. (Eds.): Database Semantics; Kluwer Academic Publishers, USA (1999).
[Sivan 99] Sivan, Y.: The PIE of Knowledge Infrastructure:
To Manage Knowledge We Need Key Building Blocks; WebNet Journal 1,1 (1999),
[Studer et al. 99] Studer, R., Abecker, A., Decker,
S.: InformatikMethoden für das Wissensmanagement; Angewandte
Informatik und Formale Beschreibungsverfahren, TeubnerTexte zur Informatik,
Vol. 29 (1999).
[Tochtermann and Maurer 2000] Tochtermann, K. &
H. Maurer: Knowledge Management and Environmental Informatics. Journal
of Computer Science 6, 5 (2000), 517536. See also: http://www.jucs.org/jucs_6_5/knowledge_management_and_environmental.
[Uschold and Gruninger 96] Uschold, M., Gruninger,
M.: Ontologies: principles, methods and applications; The Knowledge Engineering
Review 11,2 (1996), 93136.
[Woods and Sheina 98] Woods, E., Sheina, M.: Knowledge
Management Applications, Markets and Technologies; Ovum Report (1998).