Organisational Memory Information Systems An example of
a Group Memory System for the Management of Group Competencies1
José Braga de Vasconcelos
(University Fernando Pessoa, Faculty of Science and Technology, Portugal
(University of York, Department of Computer Science, UK
(University Fernando Pessoa, Faculty of Science and Technology, Portugal
Abstract: As people transform data, information and experiences
into shared corporate knowledge, the management of individual competencies
has become increasingly important to knowledge intensive organisations
(KIO). Knowledge gained during the normal execution of daily tasks is easily
lost in the new and more dynamic business environment. The ability to find
versatile employees and to be able to leverage their knowledge to meet
differing corporate needs, is a matter of pivotal importance for KIOs.
Employees' competencies, in the form of their technical and cognitive capabilities,
are closely related to the ability of a company to exploit existing, and
to create new, knowledge.
The topic of this paper is an example of the design a particular instance
of an organisational memory system: a group memory system for managing
corporate competencies. The system described focuses on internal competencies,
in particular human knowledge sources, their competencies, as well more
straightforward project experiences and related heuristics. We will show
an approach for representing and manipulating corporate competencies, and
highlight the main features of ontology-driven organisational memories.
This research work applies ontologies as a design approach to represent
organisational knowledge and ultimately to create a consensual representation
of corporate competencies.
Keywords: Competence Management, Ontologies, Knowledge Management,
Knowledge-Intensive Organisations, Organisational Memory, Group Memory.
Knowledge Management (KM) is an emergent field in the information systems
(IS) area and is one that is the source of much debate and controversy.
There is a long way to go before a consensus about the nature and scope
of KM is reached within the community. In line with much of the literature
in this area, we view KM as being concerned with the management of organisational
assets in the form of organisational knowledge.
version of this article has been presented at I-Know'03 (Graz, Austria,
July 2-4, 2003)
We view KM as being a cross-disciplinary research field and one where
several theoretical and practical notions coincide. Notwithstanding this,
the focus of this paper is on constructing an IS to support KM.
In order to develop an effective IS to support KM, we need a means of
modelling these assets in a form suitable for the creation of an organisational
IS. This in turn requires a methodology to identify, classify, represent
and these corporate knowledge assets. The approach described in this paper
is to use knowledge modelling techniques based on Ontologies to represent
organisation knowledge assets in the form of individual and group competencies.
The main objective of the ontological discipline is the syntactic and
semantic standardisation of knowledge structures. Here, Ontologies are
used to define a shared consensual structure for corporate competencies
and to act as the basis for a knowledge modelling and engineering technique.
Here we have taken a competency to be a characteristic of an individual
or group that is required to produce an effective organisational performance
[Curtis 1997]. Thus, a competency is related to an
individual or group's underlying knowledge and skills used in performing
a particular a role within the organisation. The application of such an
approach should help to enhance both the development of an enterprise's
information system as well as its overall operational efficiency.
The remainder of this paper is organized in four sections: the first
discusses knowledge intensive organisations by characterising their specific
needs. The next section describes an approach to manage
organisational knowledge based on the ideas of Knowledge Management: Organisational
Memories. This is followed by a section that presents
a discussion of ontology-driven organisational memories. The following
section describes a framework for a group memory system focused on
corporate competencies; finally, we close the paper with a section
that presents our concluding remarks.
2 Knowledge Intensive Organisations
Knowledge intensive organisations (KIO) employ highly skilled staff,
knowledge workers, whose role is essentially one of problem solving. Solving
problems in such organisations involve complex, knowledge-intensive tasks
such as dealing with abstraction and uncertainty or recognising patterns
of organisational behaviour. Decisions often need to be taken in a dynamic
work environment and based on data retrieved from several organisational
Knowledge in KIOs is a product of the expertise, experience and skills
of the individuals and workgroups that make up the organisation; it is
stored in individual's minds, explicitly encoded and documented in corporate
information systems and implicitly embedded in organisational culture,
rituals, policy and procedures.
To be effective workgroups must seek to exploit examples of best practice,
improve their efficiency and contribute to overall organisational learning.
Workgroups in KIOs need to manage their existing skills effectively, create
mechanisms to elicit new ideas and innovations, and identify sources of
In a commercial environment, where downsizing, reengineering, restructuring
and high rates of organisational turnover are common, businesses are beginning
to recognise how easy it is to lose a vital element of their intellectual
property: the organisational knowledge gained during the normal execution
of daily tasks.
People are often unaware of key resources that lie 'hidden' in the heterogeneous
knowledge repositories [Dzbor et al. 2000]. The
challenge for all organisations, but KIOs in particular, is to deal with
their knowledge resources effectively. Not surprisingly, many businesses
are now beginning to recognise the vital importance of managing organisational
knowledge to their operations.
3 Organisational Memories
A core concept in discussions about technological support for KM and
organisational learning is that of the Organisational Memory [Kuhn
and Abecker 1997]. Following this approach, we use the term Organisational
Memory (or Organisational Memory Information System) to mean a comprehensive
computer system that captures a company's accumulated knowledge assets
[Fig. 1] and makes them available to enhance the efficiency and effectiveness
of knowledge-intensive work processes.
Figure 1: Organisational Memory overview
3.1. Rationale to build Organisational Memories
Analysing the literature is one of the classical methods used to detect
the requirements and needs to build organisational memories [Dieng
et al. 1998]. From Macintosh's (1997) work on knowledge asset management,
a set of organisational impediments to more productivity and performance
in knowledge-based companies were identified:
"Highly paid workers spend much of their time looking for needed
"Essential know-how is available only in the heads of few employees".
"Valuable information is buried in piles of documents and data".
"Costly errors are repeated due to disregard of previous experiences".
"Delays and suboptimal product quality result from insufficient
flow of information".
Based in these statements, [Dieng et al. 1998]
elicited possible motivations to build an Organisational Memory:
- to avoid the loss of corporate expertise when a specialist leave the
- to explore and reuse the experience acquired in past projects in order
to avoid the repetition of previous mistakes,
- to improve the information circulation and communication across the
- to integrate the know-how from different sources in the company,
- and ultimately to improve the process of individual and organisational
3.2 Organisational Memory Requirements
The following requirements were analysed from different OM projects
and related research literature [Heijst et al. 1997],
[Kuhn and Abecker 1997], [Abecker
et al. 1998]. OM functional requirements that can minimise the above
organisational limitations and meet some of the motivations are classified
in three categories [Heijst et al. 1997]: Collecting
the information, preserving the information, and retrieving and distributing
the information. The following OM functional requirements are mainly concerned
with the facilities that should be available in the OM to the organisational
employees at the individual and group level. These functions should assist
individual and group work tasks and consequently to improve organisational
3.2.1 Collection and presentation of relevant information
Knowledge needed in work processes is distributed among various information
sources, such as electronic documents, databases, emails, and private notes
of employees. The primary requirement for an OM is to prevent the loss
and enhance the accessibility of all types of organisational knowledge
[Kuhn & Abecker 1997].
An effective strategy for collecting new knowledge assets should be
applied in the organisation. This strategy should be followed by well-defined
criteria for deciding if some information is considered a valuable knowledge
asset for the organisation or not. The information in the form of lessons
learned, best practices, or other domain knowledge assets may be collected
actively or passively [Wiig et al. 1997].
3.2.2 Preserve and integrate different types of information
The preservation and integration of different but related organisational
knowledge is a key requirement of an effective development of organisational
memories. Different knowledge areas within the organisation should be properly
classified and integrated. For example, organisational structure, company
regulations, workflow procedures, employees' competencies, product and
service descriptions should be integrated.
In addition to the integration level of organisational information,
an OM should be engineered in order to be integrated with the existing
organisational environment. An OM system should have an architecture suitable to be integrated with
the existing IS infrastructure, i.e., it has to fit into the flow of information
that is already happening in the organisation [Conklin
1996]. This requirement is crucial for the acceptance of the users
of the OM system.
3.2.3 Retrieve and distribute the information
An OM should provide easy access, navigation and retrieval of the stored
information to organisational employees. An OM should provide intelligent
query mechanisms to assist the user across the information searching processes,
proposing suggestions, alternatives and effective directions. Navigation
and retrieval mechanisms including related documental sources to be able
to access context-based information during problem-solving tasks or other
work activities should be provided.
An OM should also distribute new knowledge assets to employees that
really need that corporate knowledge. For example, provide domain knowledge
subscription mechanisms to be informed of new information needed for a
specific activity, and provide employee-profiling mechanisms to facilitate
the distribution of information to the right organisational employees.
3.2.4 Minimise knowledge acquisition and maintenance activities
Although the benefits of having an OM are recognised, organisations
are reluctant to invest resources into a novel technology where the practical
benefits will be seen only in later stages of development [Kuhn
& Abecker 1997]. The OM development process should be performed
causing a minimal interference with the normal organisational workflow.
A workable OM is only possible to achieve after a long organisational
process. In a KM context, the organisational culture, the medium and long
term objectives, and current IS infrastructure all need to be properly
understood before the effective OM development. The different participants
in its design and development, such as domain experts, knowledge engineers,
IS designers, and prospective users should be aware of the difficulties
and benefits of such organisational system.
4 An Organisational Memory Information System using
The ontological approach applied in this research follows the recent
ontology-driven KM tendency within academic and business organisations
[Staab et al. 2001]. This approach uses ontologies
to represent and manage both organisational knowledge containers and contents.
This technique allows the representation of organisational knowledge in
a way that facilitates knowledge sharing and reuse between organisational
agents. An Organisational Memory seeks to preserve and manage valuable
knowledge assets at the corporate level. OM building is a current endeavour
for many organisations, researchers and industrial practitioners.
For structuring and maintaining large amounts of heterogeneous and distributed
information in the organisation, appropriate meta-level descriptions are
needed to represent the higher-level layer of an organisational memory.
In order to develop an effective methodology to identify, classify, represent,
and reuse the existing corporate knowledge assets, an expressive knowledge
representation and modelling format needs to be chosen.
A consensual definition of an ontology says that it is a high level
formal specification of certain knowledge domain: a formal and explicit
specification of a shared conceptualisation [Gruber
1992]. A domain conceptualisation is a particular and abstract
view about real entities and events and their relationships. Formal
refers to the fact that an ontology is a form of knowledge representation
and has a formal software specification to represent such conceptualisations,
i.e. an ontology has to be machine-readable. Explicit means that
all types of primitives, concepts and constraints used in the ontology
specification are explicitly defined. Finally, shared means that
the knowledge embedded in ontologies is a form of consensual knowledge
[Benjamins et al. 1998], that is, it is not related
to an individual, but accepted by a group.
4.1 Ontology Semantics
Ontologies can be used to represent explicitly the semantics of semi-structured
information, i.e. an ontology provides an explicit conceptualisation that
describes the semantics of the domain data in analysis [Abecker
et al. 1998, Fensel 2001]. Ontologies have a similar
function as a semantic data model, such as a conceptual data schema, but
are a more expressive way of information modelling. In this research, ontologies
as an Artificial Intelligence knowledge representation notation and semantic
data models as an IS notation to define database schemas are complementary,
i.e. the two notations are needed to build effective and expressive organisational
models. The main differences between a conceptual data schema and an ontology
are [Meersman 1999]:
- a language for defining ontologies is syntactically and semantically
richer than common approaches for database schemas;
- the information described by an ontology can be presented at different
levels of formalisation: using a semantic network notation, semi-structured
natural language and formal definitions including logic axioms. Most of
the conceptual data schemas are just tabular information;
- an ontology uses a shared and consensual terminology that makes it
suitable for information sharing and reuse;
- an ontology provides formal definitions to describe the semantics of
the representational constructs, i.e., all the terms used in the ontology
specification are explicitly defined.
Although differences exist within ontologies, general agreement exists
about several issues related to the structure and behaviour of world objects
in order to represent real knowledge domains [Chandrasekaran
et al 1999]:
- There are abstract and physical objects in the world;
- A set of objects denoting similar structure and behaviour is considered
- Objects have properties or attributes that can have values,
i.e. they can be represented as triplets (Object -> Attribute ->
- Objects can exist in various relations with each other;
- Properties and relations can change over time;
- Events occur at different time instants;
- There are processes that occur over time in which objects participate;
- The world and its objects can be in different states.
- Events can cause other events or states as effects;
- Objects can have parts. This means there are atomic objects
and composite objects.
These conceptual objects and primitive constructs are used to create
domain models and they can be represented with ontologies in two different
forms: at the knowledge (or informal) level and through formal software
descriptions. Following accepted ontological development methodologies
[Jones et al. 1998], ontologies should be represented
using informal and formal descriptions.
4.2 An Ontology-driven Organisational Memory
Ontologies and data models have been used in knowledge-based systems
and database management systems, respectively, to specify organisational
model assumptions that will reflect the system's conceptualisation [Gruber
1993]. In this context, semantic data models can be considered as simple
kinds of ontologies [Abecker et al. 1998]. The ontologies
are used to describe the semantics of the different knowledge sources that
can be found within an organisational memory. For example, a competence
ontology can be designed to enrich the knowledge elements concerning the
employees and experts of the organisation. With the same perspective, an
information ontology is designed to provide factual and contextual information
about the different knowledge sources in the form of electronic documents
Effective capture and reuse of less tangible knowledge assets within
the organisation, such as the capture of contextual knowledge may be achieved
using a well-structured common and shared vocabulary. Such common and shared
vocabulary can be represented with ontologies. Such high-level organisational
knowledge description is seen as a set of definitions of context-specific
knowledge representation primitives consisting of domain-dependent classes,
relations, functions and object constants. These primitive constructs can
be applied and represented differently in several organisational domains,
but should have the same meaning for human users and designers of the OM.
The layers of an Organizational Memory are interpreted as follows. The
conceptual layer (layer 1) represents the organisational knowledge in an
informal way that can be interpreted by different OM developers, such as
domain experts, knowledge engineers and software engineers. This layer
aims to create a shared understanding of the organisational knowledge.
The creation of a common vocabulary facilitates communication in design
and maintenance issues across people with different professional backgrounds.
In the previous figure, this layer is made of domain
and general ontologies. For example, the Information and the Organisation
ontologies can be reused in several domains, since the concepts they define
are likely to be used almost universally.
Figure 2: An example of an OM model using Ontologies
The formal layer (layer 2) enables the reuse of domain terms and constructs
from other ontologies in order to facilitate future OM system maintenance
tasks. The ontological descriptions provide a common vocabulary for knowledge
engineers in order to develop further applications in this domain, such
as an inference layer and the related reasoning mechanisms. This layer
is essentially a format layer, where translators to multiple languages
and environments can be hooked.
The application layer (layer 3) uses the encoded domain knowledge. The
knowledge encoded with ontologies can be used in different application
systems within an organization. This layer is the interface with the users,
and can be tailored to different needs; as it is independent of a particular
syntax and application model, changes at the previous layers do not have
much impact at this layer.
4.3 The definition of a meta-model
In a previous OM implementation [Vasconcelos at al.
2002] the interface of the application layer is semi-automatically
generated "on the fly", presenting the user with a standard Web
page. The main goal is the definition of a unified meta-model for building
organisational memories, and to allow for particular instantiations.
Figure 3: Ontology development environment
The integration of ontologies (formal knowledge) with data models (semi-formal
knowledge) can have major benefits for the definition of precise and concise
domain models. This OM development environment [Fig. 3] includes an ontological
editor to specify and manage the OM ontology library. Using such an editor,
the goal is to develop ontologies at the knowledge and formal levels to
assist in effective data model design. This OM architecture should also
include an automatic or semi-automatic tool, based on exhaustive mapping
criteria, to translate ontological constructs and instances into the proper
data model elements whenever possible. The creation of this meta-model
is a key requirement for the effective maintenance of an OM.
In our current development environment, ontologies are translated and
stored in a relational database, as tables. A set of functions was written
as an Application Programming Interface that hides the relational/object
conversion from the programmer. By modifying the meta-model it is possible
to tailor the development environment to different needs, such as, for
example, having two meta-class class types: one where instances are created
locally (local classes), and another which refers to remote instances (remote
classes), which could be located in legacy systems.
4.4 Organisational memory specification and architecture
The preservation and integration of different but related organisational
knowledge is a key requirement for an effective development of organisational
memories. Different knowledge areas within the organisation should be properly
classified and integrated [Fig. 4]. In addition to the integration level
of organisational information, an OM should be engineered in order to be
integrated with the existing organisational environment. An OM system should
have an architecture suitable to be integrated with the existing IS infrastructure,
i.e., it has to fit into the flow of information that is already happening
in the organisation [Conklin 1996]. This requirement
is crucial for the acceptance of the users of the OM system.
Figure 4: OM specification and architecture overview
An OM should provide means to preserve and integrate organisational
information from different organisational sources in an information repository.
The OM design and development should be prepared to handle different types
of information and related levels of information representation [Kuhn
& Abecker 1997]. Therefore, semi-structured information, structured
information and formal information need to be integrated in a coherent
way. Examples of semi-structured information are file documents in the
form of notes, suggestions and hints. Examples of structured information
are file documents in the form of manuals and technical reports. Additionally,
the existing data stored in databases and data warehouses can be viewed
as a particular type of structured information. Examples of formal information
are business rules, design and process guidelines and corporate information
that represent internal (organisation) rules and procedures concerning
business processes and organisational behaviour.
5 A Case Study of Group Competencies
The material in this paper is based on research into the management
of corporate competencies in knowledge intensive organisations. The practical
outcome of this work was the design of a group memory system to manage
heterogeneous and distributed knowledge embedded in business process activities.
The specific emphasis of this work was how organisational processes can
be represented in order to provide an integrated enterprise vision that
will aid the efficient management of corporate competencies.
5.1 Group Memory Systems
A group memory is considered a specific example in a narrower scope
of an organisational memory. To define a group memory we need to consider
the work practices that include formal and informal communication that
exists between people working together or geographically distributed. Group
memory happens in the context of personal and managerial aspects that encourage
people to share their work practices to improve organisational performance.
The Group Memory System described below focuses on corporate competencies,
in particular human knowledge sources, their competencies, such as cognitive
elements, technical expertise as well as project experiences and related
heuristics. At a general level, it includes written and spoken communication,
face-to-face meetings, shared information and co-ordinated work.
Figure 5: An example of a Group Memory
The main role of the group memory is to act as a shared conceptualisation
to facilitate communication between group members and to function as a
common schema for software applications. Thus, the group memory supports
both structurally and dynamically, a shared representation of knowledge
that allows a consensual understanding of shared purposes, roles and competencies.
5.2. Representing Corporate Competencies
People are what make organisations so complex. People have different
and conflicting motivations, perceptions and attitudes, all of which change
over time. As people transform data, information and past experiences into
knowledge, the management of individual competencies will become more important
in knowledge-intensive organisations.
To find versatile employees and leveraging their competencies to meet
different corporate needs is a matter of pivotal importance for knowledge
intensive companies as competencies, in the form of technical and cognitive
capabilities of their employees, directly affect the company's knowledge
creation ability. To build an effective group memory, it is necessary to
address these less tangible aspects of human behaviour within the organisation.
The main goal of corporate competence management is a better usage of
human skills and knowledge. In knowledge-intensive companies, most daily
work tasks require professional expertise and the management of a large
body of knowledge. The construction of descriptions for these competencies
poses both organisational and design challenges.
Firstly, there is the issue of identifying what competencies are relevant
in real-world organisations. According to [Nonaka 1994]
the competencies of an organisation include tacit and explicit knowledge,
and should be conceived of as a mix of skills and technologies. Earlier
we defined a competency as a characteristic of an individual or group that
is required to produce an effective organisational performance. However,
in practice these characteristics often intangible and difficult to define
as they are not only related to the characteristics of the individual but
also to the organisational context they work in, such as the people they
know and group they work with. There have been a number of attempts to
use techniques such as Social Network Analysis to define these groups [e.g.
Tomlinson 2002], however for the purposes of this
work a clearly defined business activity or work step have been used to
define the context for the description of the related employee's competencies.
Secondly, there is the problem of representation. Our aim is that any
representation will eventually form a consensual model for corporate competencies
within the workplace. However, once identified, specific competencies can
differ in fine but significant detail e.g. [Tab. 1]. At the organisational
level, an employee's competency can have different levels of granularity
depending on the business activity or problem-solving task. For example,
a technical competency can be described in terms of different competencies,
such as analysis, modelling and engineering. In addition, each of them
can be further defined by competencies such as test, review, assess and
analysis. Competence granularity means a hierarchy of competencies and
their areas of application that can be defined for a specific workgroup.
Depending on the domain under investigation, different levels of competence
granularity can be defined in a consensual manner. Viewed in this way,
competence elements are to be modelled and retrieved like other knowledge
assets of the group memory.
||Workgroup competency taxonomy and a model for expert annotations
||Represent a consensual workgroup structure of competencies
to assist business process activities
||Portfolio Engineering - a process-oriented company workgroup
- Domain experts
- Interviews results
- Questionnaire focusing in competence management
- Active participation - communication patterns within the workgroup
- Technical documentation
- Corporate managers
- Industrial practitioners
- KM & OM researchers
- Profiling people
- Dynamic creation of competencies within a company workgroup
- Routing information needs to the right experts
- Project teambuilding analysis
- Expert annotation systems
- Enterprise Ontology [Uschold et. al 1997]
- Agent Ontology (Ontolingua Library)
- Ontology of capabilities [Stader and Macintosh 1999]
|Domain questions (some examples)
|What competencies are needed to install a TN-16XE optical
||Competency, Install, Optical Interface
||Has-skill-of (Install, Optical Interface)
Table 1: An example of a knowledge description of the Competence
5.3 A Competence Taxonomy
Personal and group competencies are formalised as special elements of
the group memory, such as competencies, skills, roles, and project experiences.
The competence ontology (using a taxonomic notation) represents the knowledge
and skills needed within the workplace to perform certain business functions
of the organisation. A competency can be stated at a very abstract level.
In this way, competencies can be decomposed to more granular competencies,
such as competencies in designing network solutions, or writing product
technical documentation. A competency can also be decomposed in the skills
required to perform the business processes underlying the business function
for which the competency is maintained.
The competency taxonomy includes two hierarchies (primitive competencies
and application areas) illustrated in [Fig. 6]. The main classes in this
classification are the competency and the entity.
The class competency allows the representation of different levels of
competency granularity through the creation of sub-classes of competencies.
In the same way, the class entity represents the different application
areas in which a specific competency can be applied.
These hierarchies are combined through a set of competence relations
(e.g., has-experience-of) that allows the combination of terms between
these hierarchies. Other competence elements, such as skills, roles, responsibilities,
background knowledge, and project experiences are represented with these
formal relations (competence relations) between the hierarchies.
Figure 6: Competence Taxonomy (adapted from [Stader
& Macintosh 1999])
The hierarchy of primitive competencies is an agreed set of competence
terms relating to a workgroup. This taxonomy of competencies needs to be
dynamic considering organisational changes and technical developments.
New terms in both hierarchies and new relations must be created during
the execution of work-related tasks. Some terms and their instances are
reused from the previous ontologies. The idea is to create and reason with
competence expressions. For example, to be able to represent that a specific
domain expert Has-skill-of (competence relation) in Design (primitive
competence) of Optical interfaces (application area). This expression
example written in a formal notation is Has-skill-of (Design,
5.4 The GMS Prototype
The GMS prototype provides a common framework for describing technical
and personal knowledge from the existing data sources within the organisational
group and adds an interface for cataloguing, indexing and retrieving information.
The GMS uses ontologies as a common and consensual vocabulary that overcomes
the heterogeneity of existing information sources. The GMS acts as a single
front-end to existing sources, adding a conceptual view of the domain and
the vocabulary used in the domain.
The domain specific GMS functions allow the sharing of unstructured
knowledge about technical characteristics and expert information entered
and updated by company (project group) personnel. It provides mechanisms
to perform queries, to navigate across the information sources and to perform
retrieval operations about compliance information. Generically, the GMS
provides a central repository [Fig. 7] for information
about products and people.
The GMS prototype functions are performed in one of three modes.
- Entering and updating the technical descriptions of products, compliance
statements, norms, standards and skills. Trained personnel on technical
compliance activities (domain area) must perform this input data task.
- Browsing product specifications through a dynamic web navigation process.
- Information retrieval about compliance statements concerning a product
or parts of it, retrieving knowledge about compliance statements, expert
annotations, skills involved, and expert identities. This is done by all
people involved in the compliance phase of the current bid response process.
This allows the integration with existing information sources, which
relies on indexing documents and directs experts to the relevant sources.
This GMS view is shown below:
Figure 7: GMS Prototype
The proposed GMS is designed using an ontology-based model concerning
a domain specific business process and related individual and group competence
elements. This ontology-based approach allows the definition of formal
elements of a domain specific ontology. In the context of the previous
competence ontology, the domain specific GMS application prototype is intended
to provide some retrieval mechanisms, such as the following inferences:
- finding knowledgeable organisational employees needed for company problem-solving
- routing information needs to knowledgeable people.
- define new competence templates based on project experiences;
- define new competence evolution schemas based on project experiences.
- competency-based practices, such as the semi-automatic identification
of competence gaps and its classification.
The GMS prototype was developed to assist the project member (user)
through the following steps:
- Identifying and describing a lack of specific expertise;
- Providing a set of guidelines to assist the user in such problem-solving
- When possible, giving the solution for the problem (competence gap)
reusing past project (bid) experiences and related technical information;
- If necessary (and agreed by the project member), a new competency element
and its description can be classified in the existing hierarchy of competencies.
Advances in information and communication technologies, and the emerging
trends in knowledge management and organisational memories, are extending
people's ability to collaborate and co-ordinate activities between business
processes. To remain competitive, organisations with significant intellectual
capital must create an environment that facilitates the better reuse and
deployment of existing corporate knowledge.
The problem of managing knowledge in large companies has grown with
the increasing complexity of organisations and quantity of information
that flows within and between them. An organisation's knowledge is built
upon the experience of their human resources and the lessons they learn.
As we outlined in section 5.2, the effective management
of this knowledge is a considerable challenge. The successful development
of group memories requires a careful analysis of the existing organisational
work practices and the available information technology infrastructure.
In section 3.2, we identified several functional
requirements for the successful construction of an organisational memory
in a KIO. Underlying these requirements are a set of problems that represent
knowledge management deficits at the organisational level. These deficits,
as described in section 3.1, constitute the rationale
to build the group memory system described in this paper.
This system was a particular instance of an organisational memory system:
a group memory system for managing corporate competencies. The system described
focused on internal competencies, in particular human knowledge sources,
their competencies, project experiences and the related heuristics.
Our current work has focussed on a specific domain (the telecom industry)
and was mainly concerned with the development of effective information
systems in ill-structured domains. Here, ontologies provide a technique
for creating a high-level domain clarification and a representation that
can be moved between platforms and systems that is dynamic and capable
of evolving over time.
Our future research aim is to define a domain-independent model to represent
corporate competence elements: an ontology-driven organisational memory
to manage group competencies. The goal is to define a competence model
that can be tailored to a variety of knowledge-based organisations. However,
further research is needed in order to test and validate our approach.
The group memory system as a theoretical concept can only be evaluated
as a practical solution to the problems outlined in the introduction to
this paper through further studies in different organisational settings.
Notwithstanding this, we believe that knowledge-intensive activities
in organisations, such as problem-solving tasks involving people from different
departments, geographical locations and technical backgrounds, would benefit
from access to and use of systems such as the one described in this paper.
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