Knowledge Nodes: the Building Blocks of a Distributed
Approach to Knowledge Management
(University of Trento, Italy
(Department of Information and Communication Technologies
University of Trento, Italy
(Department of Computer and Management Sciences
University of Trento, Italy
Abstract: In this paper, we criticise the objectivistic approach
that underlies most current systems for Knowledge Management. We show that
such an approach is incompatible with the very nature of what is to be
managed (i.e., knowledge), and we argue that this may partially explain
why most knowledge management systems are deserted by users. We propose
a different approach - called distributed knowledge management - in which
subjective and social (in a word, contextual) aspects of knowledge are
seriously taken into account. Finally, we present a general technological
architecture in which these ideas are implemented by introducing the concept
of knowledge node.
Key Words: Distributed Knowledge Management, Knowledge Nodes,
Knowledge, in its different forms, is increasingly recognised as a crucial
asset in modern organisations. Knowledge Management (KM) is referred to
the process of creating, codifying and disseminating knowledge within complex
organisations, such as large companies, universities, and world wide organisations.
Most KM projects aim at creating large, homogeneous knowledge repositories,
in which corporate knowledge is made explicit, collected, represented and
organised, according to a single supposedly shared conceptual
schema. Such a schema is meant to represent a shared conceptualisation
of corporate knowledge, and thus to enable communication and knowledge
sharing across an entire organisation. The typical outcome of this kind
of project is the creation of an Enterprise Knowledge Portal (EKP), a (webbased)
interface which provides a unique access point to corporate knowledge.
In the paper, we argue that this approach reflects an objectivistic
epistemology, as it presupposes that all contextual, subjective, and social
aspects of knowledge can be eliminated in favour of an objective and general
codification, and that this abstract and general knowledge can be shared
and reused independently from the individuals or the organisational units
(i.e. teams, workgroups, communities) in which it was created.
If, on the one hand, this assumption is coherent with traditional organisational
models and paradigms of control, on the other hand, it seems incompatible
with many theories of knowledge, where subjective and social aspects of
knowledge are viewed as essential features. We argue that this incoherence
between the high level architecture of KM systems and the nature of knowledge
may explain, at least partially, why many KM systems are deserted by users.
We follow a different approach - called Distributed Knowledge Management
(DKM) - in which subjectivity and sociality are viewed as a potential source
of value, rather than as a problem to overcome .
Our proposal is to model an organisation as a "constellation"
of knowledge nodes, namely autonomous and locally managed knowledge
sources. In this approach, a KM system becomes a tool that must support
two qualitatively different processes: the autonomous management of knowledge
which is locally produced within a single knowledge node (principle
of autonomy), and the coordination of the different knowledge nodes
without a centrally defined semantics (principle of coordination).
In the last part of the paper, we describe the high level architecture
of a system which supports this distributed approach from a technological
point of view.
2 Traditional approach to designing KM systems
If we abstract away some inessential differences, most KM projects share
a pattern which involves the following steps (see e.g. ):
- the installation of corporatewide Intranets to ensure physical
accessibility to information;
- the design of a corporate language and of knowledge maps, which are
used to represent corporate knowledge in a standard and common way, and
to create semantically homogeneous and contextindependent knowledge
repositories (the corporate knowledge base, or KB);
- the creation/support of informal communities that represent the place
where "raw" knowledge is produced through spontaneous and emerging
social interaction of company peers (typically, these communities are materialised
as "virtual communities" through the adoption of computer supported
cooperative tools, such as groupware applications);
- the creation of a new role, the knowledge manager, whose goal is to
support and facilitate the interaction within and across organisational
- the design of contribution processes which enable community members
to explicit their tacit knowledge through the codification in the corporate
- the construction of an Enterprise Knowledge Portal (EKP), which
provides a unique, simple interface through which people can contribute
to the KB, socialise, and retrieve information.
Figure 1: The traditional KM approach
The typical outcome is a system like that depicted in Figure 1.
Such an architecture is generally based on technologies like content
management tools (text miners, search engines, and so on), which are
used to produce a shared view (either implicit - e.g., clusters, neural
nets - or explicit - e.g., ontologies, taxonomies) of the entire collection
of corporate documents; common formats (such as HTML, XML, PDF),
used to overcome the syntactic heterogeneity of documents from different
knowledge sources; chats and discussion groups, used to satisfy the need
of social interaction.
3 Problems with traditional KM approach
Despite the claim of business operators and software vendors that this
approach is the right answer to the needs of managing corporate knowledge,
KM systems are often deserted by users, who instead continue to produce
and share knowledge as they did before, namely through structures of relations
and processes that are quite different from those embedded within KM systems
(many case studies are analysed in literature, in particular, the case
of a worldwide consulting firm described in ).
In , it is argued that this situation does not
originate from technological problems, but from an inadequate epistemological
model, which is coherent with a traditional paradigm of managerial control,
but is in contradiction with the deep nature of knowledge. The way most
KM systems are designed embodies an objectivistic view of knowledge, a
view according to which knowledge can be represented in an objective form,
which is independent from all those subjective and contextual elements
that are typical of "raw" knowledge (namely, knowledge in its
original form). However, a large number of researchers, working in different
disciplines, convincingly argued against this objectivistic view.
The basic argument is that knowledge is not a simple
"picture" of the world, as it always presupposes some degree
of interpretation. This means that a fact is not a fact, unless we
have a schema that allows us to give it an interpretation; and that
different schemas produce different interpretations of the
"same" fact. This aspect of knowledge was studied from
different perspectives in different disciplines. Some authors stress
the cognitive nature of interpretation schemas, where a schema is
viewed as an individual's perspective on the world (see, for example,
the notions of context [17, 7, 13], mental space , partitioned representation ); others stress their social nature, where a
schema is thought of as the outcome of a special form of
"agreement" within a community of knowing (see, for example,
the notions of paradigm , frames ), thought worlds ). In
general, interpretation schemas are only partially reducible to each
others. Indeed, to get a complete reduction, one should have a perfect
understanding of other agents' schemas, and many evidences seem to
indicate that in general this is impossible for an agent with limited
resources (see ).
In our opinion, this epistemological view has two important consequences
for designers of KM systems:
- any approach to designing KM systems which requires to organise corporate
knowledge in a supposedly objective picture of the world is in fact trying
to force a privileged schema (e.g., that of the Chief Knowledge Officer)
onto people who may not share (and thus understand) that view;
- any approach which disregards the plurality of interpretation schemas
is bound to trouble, as the outcome will be perceived by users either as
irrelevant (there is no deep understanding of the adopted schema) or as
oppressive (there is no agreement on the unique schema, which is therefore
Therefore, the concept of absolute knowledge, which refers to
an ideal, objective picture of the world, leaves the place to the concept
of local knowledge, which refers to different, partial, approximate,
perspectival interpretations of the world , generated
by individuals and within groups of individuals (e.g. organisational units)
through a process of meaning negotiation, namely a process of "distilling"
a schema which makes sense for that unit. At an organisational level, each
local knowledge appears as the synthesis between a collection of statements
and the schemas that are used to make sense of them. Local knowledge is
then a matter that was (and is continuously) socially negotiated by people
that have an interest in building a common perspective (perspective making
, or single loop learning ),
but also in understanding how the world looks like from a different perspective
(perspective taking  or double loop learning ).
Therefore, rather than being a monolithic picture of the world as it is,
knowledge appears as a heterogeneous and dynamic system of multiple "local
knowledge systems" that live in the interplay between the need of
sharing a perspective within an organisational unit (to incrementally improve
performance) and of meeting different perspectives (to sustain innovation).
Figure 2: DKM architecture 4 Knowledge nodes and DKM
In this section, we extend the approach of DKM (as presented in )
with the concept of knowledge node, which provides a useful abstraction
of organisational units from a designing perspective.
DKM is based on two very general principles:
- Principle of Autonomy: each organisational unit should be granted
a high degree of autonomy to manage its local knowledge. Autonomy can be
allowed at different levels. We are mainly interested in what we call semantic
autonomy, that is the possibility of choosing the most appropriate
conceptualisation of what is locally known (for example, through the creation
of their own knowledge maps, which in  are called
- Principle of Coordination: each unit must be enabled to exchange
knowledge with other units not through the adoption of a single, common
interpretation schema (this would be a violation of the first principle),
but through a mechanism of mapping other units' contexts onto its context
from its own perspective (that is, by projecting what other units know
onto its own interpretation schema).
In this view, a DKM system must support two qualitatively different
processes: the autonomous management of knowledge locally produced within
a single unit, and the coordination of the different units without a centrally
defined semantics. The resulting high level architecture of a system for
DKM is depicted in Figure 21.
If a complex organisation can be thought as a constellation of units,
an important issue is how this "socially distributed architecture"
can be modelled to design an "architecturally distributed" computerbased
system for supporting KM processes. To this end, we introduce the concept
of knowledge node (KN) as the building block of a model for designing DKM
architecture is under development as part of EDAMOK, a joint project of
the Institute for Scientific and Technological Research (IRST, Trento)
and of the University of Trento.
A KN can be viewed as the reification of organisational units - either
formal (e.g. divisions, market sectors) or informal (e.g. interest groups,
communities of practices, communities of knowing) - which exhibit some
degree of semantic autonomy. Semantic autonomy means the ability to
develop local interpretation schemas (perspectives on the world). In other
words, each KN represents a knowledge owner within the organisation,
namely an entity (individual or collective) which has the capability of
managing its own knowledge both from a conceptual and a technological point
of view. Notice that most often knowledge owners within an organisation
are not formally recognised, and thus their semantic autonomy emerges in
the creation of "artifacts" (e.g. databases, web sites, collection
of documents, archives, practices, and so on) which are not part of the
official information system. In what follows, we describe how we applied
the concept of KN to design the prototype of a document management application
within an Italian national bank.
The backend activity of the bank is organised in different offices
(e.g. information technology, marketing, finance), each with different
(but partially related) tasks. Currently, employees within each office
share documents by publishing them on a locally shared directory, called
"public", which is accessible only to people working in that
office. In addition, there is a global public directory, which is used
to share documents across the entire bank. Interestingly enough, these
shared directories do not have a predefined structure, which means that
each employee can add new folders at any depth in the file system in order
to provide a sort of classification to the shared documents. In other words,
local and global public directories are created, organised, and populated
through the active participation of a large number of workers. From our
perspective, each resulting directory structure can be viewed as a sort
of local classification which represents its creator's viewpoint, and provides
a distinctive perspective on the stored documents .
To identify the KNs (i.e. semantically autonomous organisational units),
we investigated (mainly through interviews) the process through which the
directory structures on the publicly accessible directories are created,
maintained, and used. We discovered that many of these structures have
a group of users who - having common problems, using a common language,
and focusing on similar objectives - share an interpretation schema (indeed,
we found some very welldefined directory structures, which were devised
precisely as a more or less stable categorisation of information). More
interestingly, these schemas do not reflect simply the office structure,
but also some interoffice projects (e.g. a multichannel project),
namely projects that involve workers from different offices. The problem
is that, using the current system, the only way to share documents among
people who work for the same project but in different offices is to use
the global public directory, which means that project documents are made
accessible to everybody in the bank (alternatively, people exchange documents
by email, which is a very inefficient solution).
From a designing perspective, this situation led us to represent each
office and project as a distinct KN, each of which needs to share documents
within and across KNs. This is an ideal test bed for a DKM system, as it
is a clear instance of a situation in which the principles of autonomy
and coordination naturally apply. Indeed, for our document sharing prototype,
we designed an architecture (depicted in Figure 2),
which instantiates the architecture proposed for DKM in .
Here's a short description.
Each KN has the following high level architecture:
Local applications. An important assumption of DKM is that different
organisational units tend to (autonomously) develop working tools that
suit their internal needs, and that the choice aand usage of these tools
is a manifestation of their semantic autonomy. This may be for historical
reasons (for example people use old legacy systems that are still effective),
but also because different tasks may require the use of different applications
and formats data (i.e. text documents, audio/movies,) to work out effective
procedures, and to adopt a specific and often technical language. In Figure
2, examples of local applications are software systems, procedures
and artifacts (i.e. relational databases, groupware and content management
tools, shared directories). Even if technologies and data formats are the
same for two or more KN, the appropriation (i.e. the local understanding
of specific uses in a given setting ) of each
KN can be very different, depending - among other things - on the local
In our case study, local applications are extremely simple: basically MS
Office applications plus the local and global public directory structures
on different shared file systems. In this case, the form of appropriation
is reflected in two aspects: the different organisation of the directory
structures (which partially represent a KN's semantic schemas), and the
different processes of contribution to these directories that are implemented
within each KN.
Contexts. In , a context is defined as
an explicit representation of a community's perspective2.
In simple situations, it can be the category system used to classify documents;
in more complex scenarios, it can be an ontology, a collection of guidelines,
or a business process. We can say that a context is the "reification"
of a KN's perspective, and its continuous, autonomous management is a powerful
way of keeping a unit's perspective alive and productive. From a designing
perspective, contexts may be created from scratch, but more often can be
"extracted" from semantic information embedded in the usage of
of context from which we started was formally defined and studied in a
formal setting in  and in .
The basic intuition is that a context is a partial and approximate representation
of the world from a given perspective. As such, a context can be formalized
as a local theory which stands in some relationship (called a compatibility
relation) with other local theories of the world. Such a relationship captures
the fact that contexts, though autonomous, can "overlap", namely
can represent the "same" portion of the world, and therefore
there must be a degree of semantic coordination among them.
These extraction processes can be supported by tools like text miners,
content management tools, and other similar technologies. Notice that,
in a sense, the purpose of these technologies is partially reinterpreted:
from instruments for the creation and management of global interpretation
schemas, to instruments for the creation and management of local schemas.
In the bank, we found that many contexts could be extracted from substructures
of the local and global public directories. As the prototype mainly aims
at document sharing, we decided to use these structures as simple categorisations,
which are used in each KN to provide a perspective on the classified documents.
Technically, we represented contexts in a Context Markup Language (see
 for more details), which allows to represent simple
conceptualisations in an XMLbased format. We also provided a context
manager, namely a simple interface that allows authorized users to
browse and edit the contexts of their KN, and to use local contexts to
compose semantically enriched queries (see below for more details).
The extraction process can be made automatically, but we believe that it
is strategic and necessary that knowledge owners take part of context extraction.
Therefore we create a context editor that helps users to mange (i.e. add
new item, delete, modify) them contexts.
Agents. In the proposed architecture, a software agent is associated
to each KN (denoted as "ia" in Figure 2).
Each agent "knows" (i.e., has direct access to) the context of
its KN. Agents have two main functions: supporting the users of a KN to
compose outgoing queries, and answering incoming queries from other KNs.
The intuition is the following. Since each context represents a KN's perspective
on some domain, it can be used not only to classify local documents, but
also to "explain" to the agents of other KNs what is the semantic
content of a query.
To give an idea of how documents search and sharing works, imagine that
someone in the KN associated to the information technology (IT) office
needs to retrieve documents about a software, say about antivirus
updating. Suppose that, in the context associated to the IT office, classifies
documents about antivirus software under the following semantic structure:
Through a context editor, the user can associate this (semantic) structure
to its query, this way making clear, for example, that she's trying to
find technical documents about antivirus updates, and not about marketing
issues. Now imagine that the agent of the KN associated to the marketing
office gets the query. Its document repository contains documents about
antivirus, but they are classified under the category "/products/software/
antivirus/marketreports/last". Of course, we'd like the agents
to be able to decide that the associated documents are unlikely to match
the category of the IT context. On the contrary, if the agent of the multichannel
project gets the query, and the local context classifies documents under
the structure "/documents/securitysystem/antivirus/antivirusupdate/manuals",
then we'd like the agents to agree that those documents are potentially
This "semantic matching" between contexts is performed through
a protocol that "mimics" the process of "meaning negotiation"
enacted by humans when trying to understand each others (for example, when
we ask to an expert to give us references to relevant papers). Technically,
agents use a matching algorithm between contexts whose preliminary description
is provided in .
In this paper, we extended the framework of DKM (as presented in )
with the concept of KN. AKN is an abstraction from a designing perspective
which provides the building blocks of a technological infrastructure for
DKM. The idea of a KN is that it "reifies" an organisational
unit which exhibits some degree of semantic autonomy, namely the capability
of producing autonomous interpretation schemas. We believe that this capability
is mostly disregarded in traditional KM systems, which tend to embody a
"centralized" approach to knowledge representation and management,
in other words an approach in which local perspectives are abstracted away
and replaced by centrally designed semantic structures. As we argued elsewhere,
we think this is one of the reasons why many KM systems look like cathedrals
in the desert. Indeed, most often the problem is not in the technology,
but in the epistemological and organisational assumptions which are implicitly
made in the way a technology is implemented in a social system.
We suggested that KNs must be explicitly recognised, and granted some
degree of autonomy at different levels: technologically (i.e. in the appropriation
of local applications), syntactically (e.g. different information formats,
and representation systems) and, most important, semantically (different
organisational units must be allowed to generate and use different interpretation
schemas). Indeed, autonomy - and thus heterogeneity - should no longer
be seen as a potential threat for an organisation, but as a potential source
of new insights and innovation. Indeed, most innovation processes are triggered
by the encounter of different perspectives, as this generates a discontinuity
in traditional and incremental organisational learning paths.
Defining the boundaries of semantically autonomous organisational units
(and thus of KNs) can be hard work. Individuals that are part of an organisational
unit are social interconnected with others to solve different objectives,
often are part of two or more units, and use more than one contexts. Indeed
each organisational unit differs from others for characteristics that are
strictly dependent to the organisational strategy, organisational climate,
and organisational competencies. It seems to us that a critical aspect
of the DKM system is to define appropriate criteria that allows observers
to analyse an organisation into KNs. In the paper, we showed how we did
this analysis in a simple case, a national bank, where the objective was
to design and implement a prototype of a document sharing application.
However, we are aware that this analysis may prove to be much harder for
more complex organisations, or for more complex KM applications.
This paper is part of the EDAMOK project, funded by the Provincia Autonoma
di Trento for the years 20012003. We'd like to thank all the members
of the project team, and in particular those who work on the application
scenario, namely Alessandra Molani, Gianluca Mameli, and Elena Andretta.
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