Supporting Knowledge Creation and Sharing in Communities
based on Mapping Implicit Knowledge1
(Fraunhofer Institute for Media Communication, MARS Exploratory Media Lab
Schloss Birlinghoven, Germany
(University of Dortmund, Dept. of Artificial Intelligence
Abstract: This paper discusses some implications of knowledge
creation processes in informal social networks for the development of technologies
to support them. The principal point of departure are social theories of
learning and the theories of organisational knowledge creation. The focus
is on models for the exchange and sharing of implicit knowledge. A model
of personalised learning knowledge maps is presented as one possible way
of addressing the problem of capturing, visualising and sharing implicit
knowledge of a community of users. In particular, we discuss how this model
resolves one critical shortcoming of the existing socialisation and externalisation
approaches: the creation of a semantic representation of a shared understanding
of the community which reflects implicit knowledge and incorporates personal
views of individual users. Finally, we outline the application to a real-world
interdisciplinary Internet platform netzspannung.org.
Keywords: Knowledge Management, Communities of Practice, Knowledge
Discovery, Knowledge Visualisation, Semantic Web
Categories: H.5.1, H.5.3, H.5.2, H.3.3
One of the major models of the generation and the exchange of
knowledge in today's so-called information or network society [Castells, 96] are technologically supported informal
social networks. Such social networks are often referred to as virtual
communities [Rheingold, 93], communities of
practice [Brown, 91], or knowledge
communities. They bring together groups of people based on a shared
set of interests or specific concerns (virtual communities), or based
on work-related sharing of knowledge and experience (communities of
practice). While such social formations have been a major model of
knowledge production and dissemination in scientific research even
before the Internet, in recent years they have been increasingly
acknowledged as major forms of knowledge exchange in professional and
work-related settings, both within organisations and across
organisational boundaries [Davenport, 98], [Cohen, 01].
1A short version
of this article was presented at the IKNOW '03 (Graz, Austria, July
The term "communities of practice" refers to "informal
aggregations of people who share work practices and common experiences"
[Wenger, 98]. In contrast to groups and teams that
are defined institutionally, participation in communities is voluntary
and typically independent of specific projects and formal processes. Rather,
the evolvement of such communities is based on spontaneous participation
and self-motivated choice, common goals such as shared needs and problems
and on a common repertoire (experiences, places and practices) resulting
in common sense-making and a common language. According to this view, knowledge
is created and reproduced through social relationships and interaction
in communities and makes sense only in relation to such communities and
Some authors see such communities of practice as the basic units of
organizations that reflect the "real" functioning of an organization
(as opposed to the formal organizational structures). Organizations are
thus viewed as "communities of communities" with independent
but interrelated "worldviews" whose interaction is seen as the
main source of innovation [Brown, 91].
In this paper we discuss the main implications of the social nature
of processes through which knowledge is generated in such social networks,
for the development of technologies to support them. As the main point
of departure we refer to social theories of learning and construction of
reality (e.g. [Lave, 91], [Berger, 66]) and to the
theory of organisational knowledge creation of [Nonaka,
95]. As a special challenge we consider the problem of sharing implicit
knowledge and its particular relevance in the context of increasingly interdisciplinary
communities of practice. We discuss three main models for addressing this
problem: the "internalisation" model based on individual reflection
on the community discourse, the "socialisation" model based on
direct face-to-face interaction, and the "externalisation" model
based on the explicit construction of a shared conceptualisation. We outline
the existing approaches for supporting these processes in networked communities
of practice, identify the main shortcomings of existing solutions and point
to possible ways of improvement. Finally, we present a model developed
in our own work as a possible way of addressing the problem of capturing,
visualising and sharing implicit knowledge of a community of users.
2 Social construction of knowledge and communities of practice
Social theories of learning (such as constructivism and social constructionism)
help us understand how people construct meaning out of information, and
how this is related to social interaction and communication with other
people. For example, [Berger, 66] describe how people
interacting in a certain historical and social context share information
from which they construct social knowledge as a reality, which in turn
influences their judgment, behaviour and attitude. [Bruner,
90] shows how the construction of meaning can be related to cultural
experiences, in a similar way as [Vygotsky, 86] has
explained how thought and language are connected and framed by a given
socio-cultural context of the learner.
In their theory of organisational knowledge creation [Nonaka, 95] describe the processes of the conversion
between explicit and implicit knowledge, and their importance for
creating collective knowledge. They refer the notion of tacit
knowledge ([Polanyi, 58], [Polanyi, 83]) to highly personal knowledge, which
is derived from experience and embodies beliefs and values. The
studies of [Lave, 91] emphasise the role of
immediate social context for learning a body of implicit and expert
knowledge through a kind of apprenticeship they call "legitimate
peripheral participation". Similarly, [Orr, 96] demonstrates how
knowledge is socially distributed across a network of experts and is
shared through processes such as storytelling.
All these studies demonstrate how the construction of knowledge (learning)
is an inherently social process in which the "learner" actively
constructs meaning, through a process of information exchange and social
interaction with other people. Furthermore, both the personal implicit
knowledge of the learner (his previous knowledge, interests, values and
beliefs), his current context of intention (e.g. a problem or task at hand)
and the social and cultural context in which the learning takes place (e.g.
networked community of practice) fundamentally determine the possible meanings
that the learner can/will construct in this process.
The principal implication of these findings is the notion of a shared
cognitive and social context which has to be established in order for the
members of the community to negotiate shared meanings, and hence construct
collective knowledge. Since the major elements of this shared context include
implicit knowledge, which resides only in community members, the critical
question becomes how to create possibilities for externalising and sharing
this implicit knowledge?
This question becomes especially relevant in the context of communities
of practice that increasingly connect experts from different fields of
expertise. Such communities are found in research fields that span different
areas (e.g. knowledge management, human-computer interaction, EU-IST) as
well as in industry contexts such as consulting agencies and innovation
management. The exchange of knowledge in such networks is commonly reflected
in a collaboratively constructed information pool (mailing lists, project
archives, best-practices etc.), which contains heterogeneous domains of
knowledge expressed in different terminologies. The heterogeneous domain
of knowledge and the decentralised and loosely structured mode of community
interaction make it difficult to express the knowledge contained in the
community information pool by means of a predefined taxonomy. Furthermore,
as knowledge is strongly tied to individual experts, the contents of the
information pool that archive the exchange of the community members will
merely reflect some externalised part of this knowledge. So, even if cross-connected
taxonomies are created by hand through some tedious process of community
negotiation, they will fail to capture this highly personal and implicit
knowledge of individual users [Nonaka, 95]. Hence,
as a central issue for supporting the exchange of knowledge in such communities
we identify the following challenge: How can existing but not explicitly
formulated knowledge structures of a given community or a group of experts,
be discovered, visualised and made usable for collaborative discovery of
knowledge in heterogeneous information pools?
3 Mapping & sharing implicit knowledge
Existing solutions to this problem can be roughly classified into three
main approaches: the "internalisation" model based on individual
reflection on the community discourse (mailing lists, forums), the "socialisation"
model based on direct interaction mediated by CMC & CSCW technologies
and the "externalisation" model based on the explicit construction
of a shared conceptualisation (e.g. Semantic Web, ontologies).
The internalisation model is the only model supported by basic community
technologies such as mailing lists, bulletin boards and discussion forums.
The development of a shared context requires members' extensive and active
participation in the community exchange. There is no mode for the shared
understanding of the community to be expressed, and the repository of the
collective memory is an unstructured space of many interrelated but rather
isolated pieces of information. Context is very difficult to establish.
The socialisation model is addressed by approaches based on the use
of multi-user networked environments. These approaches typically aim at
supporting the sharing of social knowledge through textual chat and through
graphical visualisation of mutual presence and activities of users in a
shared virtual space (e.g. [Erickson, 01]). This
is the so-called awareness and knowledge socialisation approach, which
can be related to two basic premises. The first is that by providing mutual
awareness of spatially distributed, but contextually related users (e.g.
working on same task, or belonging to same community) by means of a shared
virtual space, the cognitive distance between them is bridged. The second
is that once this cognitive distance is bridged, the conditions are established
for the users to enter into conversations through which they exchange otherwise
inaccessible personal knowledge.
Another class of approaches that can be related to the socialisation
model has investigated the possibilities of using textual virtual environments
of MUDs/MOOs as a kind of online learning labs. Here knowledge is exchanged
through shared design practices in building and programming the virtual
world (e.g. [Bruckmann, 93]). Such approaches are
often related to the constructionist theory of learning ([Papert,
80], [Papert, 90]) which emphasises the role of
artefacts. This can also be compared to the approach of "learning
by doing" and to situated learning through "legitimate peripheral
participation" as studied and described by [Lave,
91]. Other investigations on communities in MUDs focused on patterns
of social interaction with respect to issues such as construction of identity
and the self-organising establishment of social norms (e.g. [Turkle,
95]). Yet other approaches have explored the use of MUDs as social
information spaces, in which social interaction is embedded within a concrete
informational context. Related approaches include social navigation such
as collaborative web browsing, populated web pages and collaborative histories.
The explicit externalisation model is addressed by approaches aiming
at supporting the formulation of shared conceptualisations in form of knowledge
taxonomies. The currently most notable approach here is the development
of technologies for metadata frameworks that allow the modelling of the
semantic "meaning" of information in a way both processable by
computers and usable for the communication of meaning between human users.
An example are ontologies, as a model for formal descriptions of concepts
and named relationships between them, that describe how a given individual
or a group of people understands a particular domain of knowledge.
Ontologies have to be created explicitly by hand and require a process
of explicit community negotiation for achieving a consensus about the shared
understanding that is to be expressed. Once created they can be used to
access and navigate the community information pool, as well as to visualise
the semantic structure of the shared community understanding. An example
of existing efforts for building such ontologies in different disciplines
but interrelated to each other is the DublinCore initiative (http://www.dublincore.org),
while the Open Directory Project aims at collaborative definition of a
somewhat simpler taxonomy for manually mapping the content of the whole
The most typical case in practice is the combination of the internalisation
model based on information exchange through mailing lists and bulletin
boards, with the socialisation model supported through textual chat. The
main problem of such approach is that the sharing of knowledge requires
extensive interaction within the community. Recently, approaches have been
developed that try to combine all three models. An example is the SocialWebCockpit
system [Gräther, 01] that combines a shared
workspace for building up a collaborative information repository with socialisation
mechanisms such as awareness and textual communication, and with the possibilities
to explicitly build up and externalise a shared vocabulary without explicit
The main shortcoming of computer-mediated socialisation approaches is
that the sharing of implicit knowledge requires extensive interaction between
individual members, and the resulting exchange still resides only in individual
users. There is no possibility to visualise the resulting structure of
shared understanding. On the other hand, existing approaches to creating
externalised representations of a shared conceptual structure, require
explicit negotiation for achieving consensus between the members. Similarly,
it is not possible to visualise the dynamics of the creation of the shared
knowledge structures as the community evolves, and develops new knowledge.
There is no or little support for expressing the personal points of view
of individual users and putting them in relation to the shared structure.
At the same time, one of the essential mechanisms of knowledge creation
is the ability to change perspective and see the world with "different
eyes". Finally, the challenge remains of how to provide insight into
the underlying values and beliefs shared by a group of users, as fundamental
elements influencing their thinking, judgment and the creation of new knowledge.
We believe that one possible way of approaching this challenge is
to explore the possibilities of the techniques of cognitive and
perceptual mapping such as those commonly used in strategic decision
making and social modelling (e.g. customer segmentation, voting
behaviour analysis). Since these techniques are based on the idea to
capture not only patterns of rational reasoning but rather implicit
elements such as affective and emotional responses, values and
beliefs, they could be used to provide a completely different
perspective on the structure of the community knowledge, than in the
existing approaches. Previous experiments along these lines include
the use of methods inspired by personal construct psychology [Kelly, 55] such as the repertoire grid elicitation,
for extracting conceptual structures of individuals and groups of
users [Shaw, 95]. Also computer-supported social
network analysis based on statistical and linguistic analysis of texts
has been used for visualising the social and semantic networks based
on implicit patterns of community interaction contained in newsgroup
postings [Sack, 00].
On the other hand, the approaches of collaborative filtering and
recommender systems provide a way for putting in relation perspectives
of different users, based on explicit expression of their judgment and
preferences (e.g. ranking) or on implicit statements such as
bookmarks or patterns of interaction with information. Typically, they
allow to identify members with similar interests and can recommend
items of relevance to a given user based on the fact that they have
been highly rated by other users with similar interests. Experimental
applications for supporting communities include collaborative
filtering of postings in Usenet news ([Resnik,
94]), the Firefly system for recommending movies [Shardanand, 95] and purchase recommendations such
as "related books" recommendations of Amazon.com.
Such techniques could be used to create both personalised views on the
community knowledge as well as to construct a shared structure. In our
work we have explored the combination of such methods as a way for capturing,
visualising and sharing implicit knowledge of a community of users ([Novak
and Wurst, 03a], [Novak and Wurst, 03b], [Novak,
02]). The main idea has been to create a form of perceptual maps, which
both capture personalised views on the community knowledge as well as relate
them to a shared conceptual structure, in a way which does not require
explicit negotiation and interaction between the individual members.
4 Personalised Knowledge Maps & Sharing of Knowledge
As a practical context for our work, we take the process of information
seeking and semantic exploration of a document pool. Within the context
of networked communities of practice, this is typically the unstructured
repository of community information exchanges (e.g. mailing list archives,
project descriptions, best-practices etc.). The access to this information
pool can be understood as a process in which the users' interaction with
information both reflects their existing knowledge and produces new knowledge
In order to develop a practically feasible solution for capturing and
visualizing implicit knowledge structures of human users based on their
interaction with information, two basic problems need to be solved:
- A context for user actions has to be created in order to be able to
interpret the meaning of user interaction with information items. The lack
of a sufficiently clear interaction context is the main difficulty of general
"user-tracking" and interaction mining approaches such as in
- A form of visual representation has to be found that communicates to
the user both the semantics of the information space in itself (content,
structure and relationships) and relates this to the meaning of his actions.
To this end we have developed a model of personalised learning knowledge
maps [Novak and Wurst, 03a]. A knowledge map presents a semantic structuring
of an information pool. It consists out of two main elements: the DocumentMap
and the ConceptMap.
The DocumentMap (Fig 1, left) presents the
information space structured into clusters of semantically related
documents. This provides an overview of topics and relationships in
the information space. The ConceptMap (Fig. 1,
right) visualizes a concept-network that provides both a
navigation structure and insight into the criteria that have
determined the structuring in the DocumentMap. In order to construct
such knowledge maps based on a user's personal point of view we
combine methods for statistical text-analysis and clustering based on
Self Organizing Maps ([Lin, 91], [Kohonen, 00], [Honkela, 97])
with methods for supervised learning of user-induced templates based
on the Nearest Neighbour algorithm (e.g. [Aha,
91]). First the user is presented with a system-generated
structure, which she can explore and rearrange in an unobtrusive
manner (moving documents between groups, creating new groups and
adding new cluster labels). In this way the user provides a template,
which reflects her personal point of view and the insights she
discovered and internalised as knowledge. This template is learned by
the system and can be used as a model to automatically classify
information into user defined clusters. In the resulting map, not only
the users interests are reflected but also her way of structuring
information, providing a personalised view on the information
space. Such learned personal maps can dynamically classify arbitrary
new information even as the community pool evolves. Furthermore, based
on the analysis of the user's personal DocumentMaps, the system
extracts a personal ConceptMap, which displays a network of most
relevant terms and connections between them, "seen" from a
user's perspective. To this end, the most relevant terms for the
document clusters from all personal maps of a given user are put in
relation to user-defined labels of the clusters in question. The
cluster labels are selected as main concepts and the calculated terms
of relevance for the given cluster are assigned a weighted
relationship to the corresponding label (Fig. 1, top right).
5 Collaborative Discovery and Sharing of Knowledge
The described model of personal knowledge maps provides an unobtrusive
way of creating dynamic artefacts that reflect the implicit knowledge of
a user. Moreover, the user directly profits from creating personal knowledge
structures. This can be seen as an important motivation factor, which is
crucial in collaborative information systems.
The learned user maps offer two possibilities for the user's
knowledge to be used by others. Firstly, a map can be called up
explicitly by another user and applied to classify an
information pool from the viewpoint of the map author. Secondly, we
can statistical analysis to the maps of all users, in order to
implicitly create an overall knowledge structure that considers
the relationships between viewpoints of all users. We also infer
relations between concepts e.g. stemming from user defined cluster
labels that draw connections between a term and a set of objects. The
resulting ConceptMap provides a way for constructing a semantic
representation of shared understanding of the community: it presents
the main concepts and relationships describing the community knowledge
without the explicit expression and negotiation by the users, and puts
it in relation to individual views. As this collaborative ConceptMap
is created dynamically based on user interaction with information it
will evolve together with the patterns of the community exchange.
For creating this overall structure from personal maps created by
the members of the community, we use a combination of text-based
measure and the co-occurrences of objects in user created
clusters. Initially text-based measures are used to estimate the
similarity between objects and terms. After some user interaction is
available, we can replace these measures by the co-occurrence measure,
which provides a direct user-based indication of relationships between
objects. The switching from text-measure to co-occurrences is
automatic. This combines the advantages of text-based methods
(applicable independently of any user interaction), with the
advantages of collaborative methods (directly related to user views
and independent of the objects content).
Figure 1: The Knowledge Explorer Interface
The inference of relations between concepts from personal maps is based
on the fact that by labelling clusters, the users draw a connection between
a term and a set of objects. Two concepts to which related objects are
assigned by many users can be considered to be related themselves.
Using this relationship, a ConceptMap can be created that
represents the users' shared understanding of the information
space. Using both similarity of objects and similarity of concepts in
combination with text based methods2, allows the shared structure of the
information space to emerge step by step, avoiding the cold-start
problem of collaborative filtering.
The knowledge represented by the created maps can be also used for
dynamic contextualisation of search results. To this end we have
created an intelligent search functionality based on the idea that a
user has a current as well as a long-term information need. By
entering some keywords the user expresses her current information
need. The long-term information need is extracted from the maps the
user has created so far. The search results then contain both the list
of retrieved objects based on keyword match, as well as a list of most
relevant personal maps of different users. The first map from the list
of most relevant maps is automatically visualized and the objects from
the result list are highlighted. In the DocumentMap the user can thus
identify clusters of related documents, which his search query
otherwise would not have retrieved, while in the ConceptMap the
corresponding concepts are marked. Thus the user also discovers new
concepts that might better describe the possible knowledge spaces to
which his query might refer.
In the described way, a semantic representation of shared
understanding of the community is constructed, which presents the main
concepts and relationships describing the community knowledge without
the explicit expression and negotiation by the users. The members of
the community can now share knowledge through exchanging their
personal maps or by navigating the shared concept structure. As the
collaborative ConceptMap is created dynamically based on user
interaction with information it will evolve together with the patterns
of the community exchange. In this way we have realised a possible
solution to some of the main problems of other approaches to sharing
implicit knowledge presented in the previous chapters: in particular
the problem of the creation of a shared structure based on implicit
knowledge of the community that incorporates personal views of
individual users, doesn't require negotiation of consensus and that
evolves with the dynamics of community development and interaction.
2Fig. 2 shows
some preliminary results concerning the combination of text-based
similarity and co-occurrences. The evaluation criterion in this case
is the expected nearest-neighbour classification error as derived
through leave-one-out-testing. While text similarity shows not to be
dependent on the amount of user interaction, similarity based on
co-occurrences strongly improves with an increasing number of
maps. The combination not only chooses the best of both methods at
each point, in some interval it is actually superior to both methods.
Though the result has been derived with a very small document pool and
few users, which makes it not fully representative, it still shows
that our approach is very promising.
Figure 2: Effectiveness of the combined text and
6 Knowledge Maps as Community Artefacts for Reflective Awareness
and the Creation of New Knowledge
While the previous chapter focused on the pragmatic usefulness of
the developed model of personalised knowledge maps for sharing
knowledge in groups of users, another important aspect is what we call
"reflective awareness". The basic idea here is that one of
the critical elements influencing the potential for the construction
of new knowledge is the existing knowledge of individuals and groups
of people. Thus becoming aware of this knowledge is a prerequisite for
processes involving the creation of new knowledge. In other words, one
of the critical aspects of learning is the ability to change
perspective and discover hidden assumptions and mental models
underlying a given point of view.
From this aspect the personalised knowledge maps can also be seen as
a kind of knowledge artefacts that can be interactively manipulated and
discussed by the community members (exploring maps of other users, applying
them to different situations, comparing a personal concept structure to
other individual and shared concept maps) in order to get an understanding
of different mental models and interpretative schemas. The idea is that
rather than just through automatic inference of relationships it is through
one's interaction with the maps that one can develop an awareness of and
insights into implicit structures - such as mental models, values and beliefs
- of one's own or shared by the community. The hypothesis is that by achieving
this kind of reflective awareness the processes of communication and sharing
of knowledge especially in heterogeneous user communities can be qualitatively
improved in order to stimulate the emergence of new knowledge previously
not consciously considered or perceived by the community as a whole.
7 Relationship to Related Work
The basic idea of generating user-specific templates and applying them
for personalized structuring and filtering of information has been previously
realized in several different ways. In one class of approaches the users
have to express their preferences explicitly and as their primary task,
such as by voting, preference profiling or initial selection of items from
a given information pool (see [Herlocker, 00] for
an overview). One critical issue here is the bootstrapping problem: the
available orientation for users' initial identification of relevant items
in an information pool (which they are not familiar with) is based solely
on already available profiles of other users (e.g. [Resnick,
94]). A related problem is that of communicating the intention and
meaning behind user choices that contributed to the creation of a given
profile to other users: the profiles themselves are typically neither "explained",
nor visualised, nor put in relation to the semantic structure of the underlying
information pool. Another class of approaches attempts to analyse the users'
actions in form of click streams and navigation patterns on the web (e.g.
[Joachims, 97], [Chalmers, 01]).
The critical issue here is the lack of a clear context for interpreting
the meaning of users actions.
In our approach both of these problems are addressed by introducing
a system generated map as 1) a clear initial context for user actions,
2) a structure for semantic navigation in an unknown information pool,
3) form of visualising users personal knowledge structures in relation
to the original information space. This approach also allows us to make
the expression of personal points of view unobtrusive and not distracting
from the users main task: that of discovering relevant information and
internalising it into knowledge. Furthermore, the personalized maps in
our approach provide an easy and understandable way for communicating and
sharing knowledge between different users both through explicit selection
of different maps by the users themselves, as well as through implicit
inference mechanisms of the agents that analyse the relationships between
In the context of knowledge management for communities of practice such
an approach can be most closely related to the personalisation strategy,
although it contains some aspects of loosely structured codification (representation
of knowledge maps). But instead of trying to codify knowledge through explicit
extraction into strongly structured forms of description (codification
strategy), our approach supports the creation of knowledge-based networks
that allow knowledge to be communicated between different experts (personalisation
strategy). The particular innovation is a personalisation technique, which
supports the creation of knowledge-networks as side effects of user actions.
The combination of techniques for self-organised clustering and supervised-learning
resolves the bootstrapping problem typical for collaborative filtering,
recommender systems and probability-based topic map extraction. It also
provides a context for interpreting user actions and allows a usable level
of expression and codification of individual knowledge, in a way, which
is unobtrusive for the users and non-distracting from their primary task.
Moreover, the learned knowledge structures are related to the context of
user actions, and visualised and applied in a way, which enables intuitive
understanding of the criteria governing their behaviour - a common shortcoming
of other approaches [Herlocker, 00].
The ability to connect different personalised structures into a shared
concept map based on global patterns of knowledge exchange in the community
also resolves some limitations of methods for ontology extraction which
are applicable only within very specific knowledge-domains and tend to
suffer either from overkill in complexity or underkill in practical relevance.
The tight integration of the visualisation model with the underlying model
for extracting and describing knowledge structures ensures that the resulting
level of semantics is both powerful enough to represent significant relationships
between concepts, context and individual items of information, as well
as simple enough to be intuitively understood and used by the users. Finally,
the ability to interactively manipulate the maps in ways which allow the
user to "take on" dynamic perspectives of different users and
put them into relation both to his own viewpoint as well as to the shared
community structure, supports the user in developing an awareness of implicit
structures, such as mental models, values and beliefs shared by a given
An important issue regarding the integration of the described model
with other applications is the ability to automatically export the discovered
knowledge structures in the RDF and the Topic Map format. This not only
allows for knowledge exchange between different applications, it also provides
an approach to the problem of generating Semantic Web meta information
automatically. This is an essential point, as one of the main reasons for
the relatively small acceptance of the Semantic Web, in comparison to its
predecessor the World Wide Web, can be seen in high effort of creating
Semantic Web information manually.
8 Application to the Internet platform netzspannung.org
The practical test bed and first application context of the
described work is the Internet knowledge platform netzspannung.org [Fleischmann, 01]. netzspannung.org aims at
establishing a knowledge portal that provides insight in the
intersections between digital art, culture and information
technology. Typical netzspannung.org users are artists, researchers,
designers, curators and journalists. The basic requirement of such an
interdisciplinary knowledge portal is: a continually evolving
information pool needs to be structured and made accessible according
to many different categorization schemes based on needs of different
user groups and contexts of use. By using the described system this
heterogeneous user group will be able to interactively compose and
collaboratively structure an information pool, to visualise and
explore it through personalised knowledge maps, and to construct a
shared navigation structure based on the interconnection of personal
points of view.
Following the methodologies of participatory design and user-driven
innovation an early proof-of-concept prototype of the system has been
evaluated in a netzspannung.org workshop with a heterogeneous group of
target users: curators, artists, information technology researchers,
media designers and representatives from museums, cultural
institutions and media industries3.The users had the possibility to
explore system-generated maps and restructure them according to their
own understanding and thus to create personal maps. Since the
learning functionalities haven't been implemented at that point yet,
the resulting maps could only be saved as a kind of personal static
semantic views on information space. Nonetheless, the received
feedback was extremely positive and justified the envisioned overall
model. In particular the users reacted very well to the idea of an
initial system-generated map not only as an overview, but also as an
exploratory interface and a means of inspiration for discovering
unexpected relationships between different thematic fields and
Furthermore the users explicitly highlighted the importance of the provided
ability to express personal views and the planned functionalities of creating
a shared but connected multi perspective structure. This had been repeatedly
pointed out as an essential feature of a model aiming at supporting the
exchanges in such a heterogeneous and loosely connected community as theirs.
Another very much discussed issue has been the users' need to understand
the criteria of the system functioning (clustering) which is incorporated
in the current model by the pairing of a system-generated DocumentMap with
a corresponding system-generated ConceptMap that provides insight into
the clustering criteria and enables its interactive parameterisation by
the users themselves. Finally, the users received enthusiastically the
envisioned possibility of publishing and exchanging their personal maps
with each other.
The current system prototype has been internally deployed as information
access interface to the submissions of the cast01 conference and of the
competition of student projects digital sparks. This simulates the use
scenario in which users can explore possible relations between information
usually isolated in separate archives of different communities in the fields
of media art, research and technology. The results can be tried out in
the guided tour and partially online available interactive demos4. An early visualization prototype for browsing system generated maps
is still in day-to-day use as a public information interface in
9 Critical issues and further work
We are aware of several critical issues of the presented model. One
is the classical problem of collaborative aggregation methods, which tend
to suppress minority views. In consequence, when the collaborative analysis
dominates the similarities from the text-analysis, only mainstream patterns
of relationships might emerge in the shared concept structure. Furthermore,
editing personal knowledge maps, the user can arrange objects only in flat
structures, which is very intuitive and easy to handle, but not always
early proof-of-concept workshop took place in 2001. See http://netzspannung.org/workshops/knowledgemaps.
Therefore, our colleagues are exploring the integration of
a second editor, capable of creating hierarchical structures and other
relations between objects in order to explicitly formulate an ontology
Another critical point is also the question of privacy. Since our concrete
application context is an interdisciplinary professional community of experts
(netzspannung.org), the assumption is that the users will be willing to
share their maps, as a motivation for gaining expert reputation within
the community. But in other cases this might be a non-trivial problem to
Another question is how it would be possible to determine the amount
of the influence that implicit knowledge has had in the created maps, and
how much is still a factor of explicit reasoning? In particular, to which
extent can such a model allow us to incorporate or discover values and
beliefs shared by a group of people? One assumption is that they are supported
implicitly through the exploratory mode of the use of the system, where
the user doesn't have to explicitly formulate a query to communicate the
meaning and intention of his actions. The other is that they can be deduced
by human users themselves by reflecting on the concept maps and the relationships
that appear between the individual concepts in the personal maps and the
shared structure. Currently we are also working on adding a PeopleMap based
on the relationships that can be induced between personal maps of different
users. Further issues include: What kind of social mechanisms develop or
become amplified in this process (e.g. reputation economy)? What role plays
the possibility of reflection on the hidden assumptions and beliefs shared
by the community? Can we use such maps as tools of analysing the knowledge
flows in existing social networks (e.g. of scientific research, EU-IST
programmes) in order to identify which implicitly shared values govern
current trends ?
In this paper we have discussed the main implications of the social
nature of the processes through which knowledge is generated in social
networks such as networked communities of practice, for the
development of the technologies to support them. We have focused on
the problem of sharing implicit knowledge, outlined and inspected the
suitability of existing technological approaches, and identified
possible ways of developing new models. We have discussed how the
social theories of learning and theories of organisational knowledge
creation can inform this investigation. In doing so, we have
demonstrated how such an inquiry requires an interdisciplinary
approach integrating the insights and methods from disciplines as
different as informatics, sociology and organisational science. In
particular, we have presented a model of personalised learning
knowledge maps for capturing, visualising and sharing implicit
knowledge of a group of users. We have shown how this knowledge map
model resolves some of the main problems of the existing socialisation
and externalisation approaches: in particular the creation of a
semantic representation of a shared understanding of the community,
incorporating implicit knowledge and personal views of individual
Furthermore, we have presented possibilities to use knowledge maps as
medium for explicit and implicit exchange of knowledge between different
users. As pointed out, our system differs significantly from so called
"collaborative filtering" systems, as items are not just rated
by the users, but are put into context, in a way which is unobtrusively
embedded into users' primary activity.
Finally, we have discussed the application within the interdisciplinary
Internet portal netzspannung.org, the critical issues and open questions
of this model, and how they will be addressed in the further work.
The work described in this paper has been undertaken within the projects
AWAKE - Networked Awareness for Knowledge Discovery and netzspannung.org
- an Internet Media Lab, both financed by the German Federal Ministry
for Education and Research. We would like to thank Monika Fleischmann and
Wolfgang Strauss, the directors of the MARS Exploratory Media Lab and the
leaders of the netzspannung.org project, as well as Katharina Morik the
head of the Artificial Intelligence Dept. at the University of Dortmund,
for their support of this work. We further want to thank the whole team
that participated in the system development and the realisation of the
described model, and the Knowledge Explorer interface: Martin Schneider,
Kresimir Simunic, Stefan Paal, Reni Banov, Christoph Seibert, Daniel Pfuhl,
Jens Wagner, Roger Sennert, Danijela Djokic and Hartmut Bohnacker.
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