MANAGING CHARACTERISTIC ORGANIZATION
KNOWLEDGE IN COLLABORATIVE NETWORKS
Ekaterina Ermilova and Hamideh Afsarmanesh
Collaborative Networks Group, University of Amsterdam, Kruislaan 419, 1098 VA Amsterdam, The Netherlands
Keywords: Ontology Engineering, Knowledge Modeling, Knowledge Management, Virtual organization Breeding
Environment, Organization’s Profile, Organization’s Competency.
Abstract: Modeling and management of the knowledge/information related to the organizations is fundamental to
efficient operation of the Collaborative Networked Organizations (CNOs). Continuous changes in different
aspects of involved organizations, is an unavoidable reflection to the dynamic changes in the demands of
stakeholders and customers in the market / society. Organizations’ profiles, and especially their competency
characteristics tailored to the requirements of opportunities emerging in the market, need to be modeled and
managed. Furthermore, mechanisms are required for handling the flexible representation and processing of
the organizations’ knowledge. Both research and practice have shown that the formation of collaborative
networked organizations requires pre-establishment of a cluster, also called a breeding environment. In this
paper we address the Virtual organizations Breeding Environments (VBEs), which provide the necessary
conditions and support for configuration and creation of Virtual Organizations (VOs). Using the ontology
engineering approaches, we present an approach for modeling of organizations’ knowledge inside VBEs,
and specify the ontology for their profiles and competencies. Furthermore, we present the required
mechanisms for management of the organizations’ knowledge, and specify the functionality required to
manipulate organizations’ information through the life cycle of VBEs. The paper also addresses the logical
design of a database for storage of organizations’ information and for the visualization of organizations’
profile and competency knowledge.
1 INTRODUCTION
Modeling and management of knowledge that is
gathered and generated by organizations is a
fundamental driver for organizations’ successful
operation in the market and society (Balakrishnan et
al, 1999) (Caie, 2007) (Afsarmanesh and
Camarinha-Matos, 2005). Existing networks/clusters
of are now eager to model and manage their
knowledge, accumulated across their environment,
in order to process and analyze it for discovery of
possibilities, and deciding on strategies for future
network evolution. In this paper we focus on
modeling and management of different
organizations’ knowledge within the Collaborative
Networked Organizations (CNOs). The CNO has
provided a new paradigm applied to many emerging
domains (e.g. domain of tourism, health care,
manufacturing, among others) in the market and
society. (Camarinha-Matos and Afsarmanesh, 2006-
a) gives the following definition for the CNO: CNO
is an alliance constituting a variety of entities (e.g.
organizations and people) that are largely
autonomous, geographically distributed, and
heterogeneous in terms of their: operating
environment, culture, social capital, and goals, that
cooperate/collaborate to better achieve common or
compatible goals, and whose interactions are
supported by the computer network. Unlike other
networks, collaboration in a CNO is an intentional
property that derives from the shared belief that
together the network members can achieve goals
that would not have been possible or would have
had a higher cost if attempted individually.
Supporting the main features of the CNOs, such
as scalability, dynamism and geographic
distribution, require the need for the ICT-based
management of CNOs. In our research we address
management of the CNO member organizations’
knowledge that constitutes the base for the entire
CNO management. We focus on a specific type
(Afsarmanesh and Camarinha-Matos, 2007) of
CNOs, namely the Virtual organization Breeding
Environment. We adopt the following definitions of
the Virtual organization Breeding Environment and
the Virtual Organization: Virtual organization
313
Ermilova E. and Afsarmanesh H. (2008).
MANAGING CHARACTERISTIC ORGANIZATION KNOWLEDGE IN COLLABORATIVE NETWORKS.
In Proceedings of the Tenth International Conference on Enterprise Information Systems - AIDSS, pages 313-320
DOI: 10.5220/0001709603130320
Copyright
c
SciTePress
Breeding Environment (VBE) (Afsarmanesh and
Camarinha-Matos, 2005) is a network of
organizations and some related supporting
institutions that adhere to a base long term
cooperation agreement, and adopt common
operating principles and infrastructures, with the
main goal of increasing their preparedness in
advance, towards potential collaboration within
Virtual Organizations. Virtual Organization (VO)
(Camarinha-Matos and Afsarmanesh, 2006)
represents a network of legally independent
organizations that come together and share
resources and skills to achieve common goals, such
as preparing a proposal or a bid, or jointly
performing the tasks needed to satisfy an
opportunity, while using the computer networks and
software systems as the base
communication/interaction infrastructure.
In VBEs, the member organizations’ knowledge is
mainly represented by their “profiles” (Afsarmanesh
and Camarinha-Matos, 2005) (Ermilova and
Afsarmanesh, 2007]. Considering the main aim of
collecting knowledge about member organizations’,
which is to enhance their involvement within VOs,
the main element of the profile is the organizations’
competency” (Afsarmanesh and Camarinha-Matos,
2005). In this paper, we provide “extensive”
definitions for “organization’s profile” and
organization’s competency” in VBEs:
Organization’s profile consists of the set of
determining characteristics (e.g. name, address,
capabilities, etc.) about each organization, collected
in order to: (a) distinguish and compare each
organization with others, (b) analyze the suitability
of each organization for involvement in some
specific line of activities / operations.
Organization’s competency is the main element
of the VBE profile that provides up-to-date
information about capabilities and capacities of
each organization, as well as conspicuous
information about their validity, qualifying it for
participation in some specific activities / operations
within the VBE, and mostly oriented towards the VO
creation.
This paper addresses our approach and
mechanism for modeling, specification and
management of VBE members’ profiles and
competencies. Our approach is based on the formal
specification of pre-defined ontologies for the
organizations’ knowledge in VBEs. The research
results, presented in this paper, further contribute to
the design and development of a Profile and
Competency Management System (called PCMS) for
VBEs, which is outside the scope of this paper, but
the topic for forthcoming papers.
Below in this Section, after discussion of the main
motivations for the ICT-based modeling and
management of member organizations’ profiles and
competencies, we address the requirements and the
identified research challenges. In Section 2, the
paper addresses the models for VBE member
organizations’ profiles and competencies, introduced
earlier in (Ermilova and Afsarmanesh, 2007).
Section 3 presents the ontologies developed to
support modeling and management of organizations’
knowledge in VBEs. In Section 4, the paper
specifies the ontology-based functionalities designed
for management of VBE member organizations’
profiles and competencies. Section 5 presents the
designs of both: the database and visual interface,
specified for the VBE member’s knowledge. Finally,
Section 6 concludes this paper.
As such, the main motivations for management
of detailed profiles and competencies of VBE
member organizations are listed by m-1 to m-8: for
profiles – (m-1) creation of awareness inside the
VBE, (m-2) selection of partners for new VOs, (m-
3) evaluation of members by the VBE
administration, (m-4) introduction / advertising in
the marker / society, and (m-5) identifying
competency gaps; specifically for competencies
(m-6) matching VBE members’ competency details
against the risen opportunity, (m-7) determination
of VO’s competency, and (m-8) processing the VBE
members’ competencies.
Furthermore, the design and development of ICT-
based representation and management of the VBE
member organizations’ profiles and competencies
are motivated by the following: (i) In medium and
large VBEs (i.e. with more than 20 members) as
well as in geographically distributed VBEs, the only
way for all VBE members to get the up-to-date
information about each other is through computer-
based representation and distribution of their
profiles. (ii) Due to daily changes of customer
demands in the market and society, the profiles and
competencies of VBE members are dynamically
changing. In medium and large VBEs, the VBE
administration is not able to obtain and analyze up-
to-date knowledge about all members with such a
high level of dynamism. Thus, there is a need for
ICT-based submission and processing of the
members’ profiles and competencies facilitating the
VBE’s dynamism.
From our analysis of the VBEs, a number of
requirements are identified for modeling and
management of VBE member organizations’
knowledge are listed below by r-1 to r-5: for
modeling – (r-1) in order to be easily adopted by
VBEs operating in different domains (e.g. in
domains of tourism, health care, manufacturing,
etc.), development of unified/generic models is
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314
needed for organizations’ profiles and competencies;
for management - (r-2) support for continuous (e.g.
daily) updating of profile and competency data, (r-3)
handling the confidentiality of profile and
competency data, (r-4) support for adaptability of
the management approaches to the wide varieties of
VBE applications, and (r-5) support for
sustainability of the management approaches in
dynamic and expanding environments.
Among the technical challenges involved in
modeling and management of VBE member
organizations’ knowledge, the following challenges
of c-1 to c-7 are addresses by our approach: for
modeling - (c-1) support of different abstraction
levels from general knowledge classes to the
domain-dependent knowledge classes, and (c-2)
unification / generalization of different competency
representations / models as exists in research and
practice; for management - (c-3) common
understanding of profile and competency models by
all VBE members, (c-4) categorization in catalogues
of competencies, (c-5) unified naming of the
competencies and their standardization, (c-6) formal
representation of profile and competency data in
order to support their further semi-automatic
processing, and (c-7) semantic integration of the
different description of VBE members’ expertise
submitted to VBEs.
2 MODELING KNOWLEDGE OF
ORGANIZATIONS
The model, designed for organizations’ profile and
competency knowledge, represents the set of
knowledge classes, as well as the relationships
among these classes, that need to be collected and
managed in VBEs. These knowledge classes are
empirically identified through: (a) interviewing and
questioning the representatives of running VBEs,
(b) study of related work on organizations’ profiles
and competencies, and (c) study of existing
enterprise models and ontologies (Ermilova and
Afsarmanesh, 2007). A subset of classes and their
relationships representing the profile and
competency knowledge are addressed in Table 1 in
the form of a hierarchy, where elements labeled by 1
to 8 represent classes, and each “x.y” represents
either an attribute of the “x” element (e.g. “3.2”) or
another class that is related to “x”, e.g. “3.3”. Please
note, that Table 1 provides only the names of the
main knowledge classes identified for the VBE
member organizations’ knowledge model. The
detailed specification of this model is the subject for
a forthcoming paper.
A main objective for our research on knowledge
modeling and management is to distinguish among
the domain-independent knowledge classes (e.g.
common for all VBEs) and the domain-dependent
knowledge classes (e.g. related only to a specific
domain of activities, e.g. manufacturing). In this
paper we introduce the concepts of a “core-class”
and a “domain-class”. As such the knowledge
classes represented in Table 1 are the core-classes,
because they are generic enough to be presented in
all VBEs, independent of their domains / business
areas. Additionally, some of the core classes (e.g.
competency, resource, products/services, etc.) have
further domain-dependent sub-classes, i.e. domain-
classes. For example, within the manufacturing
domain the competencies can be classified by the
specific manufacturing activities, e.g. “metalworking
competency”, “welding competency”, “turning
competency”, etc. As such, the domain-classes can
be integrated into the generic profile / competency
model as the elements labeled by 3.1, 3.2, 3.3.1,
3.3.4.1, 3.3.5.1, 5.1, 6.1, 7.1, and 8.1 (i.e. italicized
elements in Table 2). The domain-classes shall be
arranged in the form of generalization hierarchies,
e.g. the “milling competency” and “welding
competency” are subclasses of “metalworking
competency” in the generalization hierarchy of
competencies for the metalworking domain. Such
hierarchies shall also support the categorization and
cataloguing of VBE member organizations by their
domain related characteristics.
Table 1: Hierarchy of the main knowledge classes in the
VBE member organizations’ knowledge model.
Label Elemen
t
Label Elemen
t
1 General data 4
F
inancial data
2 Contact data
4.1
Annual balance
3Com
p
etencies
4.2
Annual revenue
3.1
Com
p
etenc
y
class
5
R
esources
3.2
Com
p
etenc
y
name
5.1
R
esource class
3.3
Ca
p
abilities
5.2
Resource name
3.3.1
Ca
abi
it
class
5.3
Ca
p
acities
(
ref. 3.4
)
3.3.2
Ca
p
abilit
y
name
5.4
Cons
p
icuities
(
ref. 7
)
3.3.3
Resources
(
ref. 5
)
6
P
roducts
3.3.4
Raw materials
6.1
Product class
3.3.4. Raw material class 6.2
Product name
3.3.4. Raw material name 6.3
Product markets
3.3.5
Standards
7 Cons
p
icuities
3.3.5. Standard class 7.1
Cons
p
icuit
y
class
3.3.5. Standard name 7.2
Cons
p
icuit
y
name
3.3.6
Products
(
ref. 6
)
7.3
Issue date
3.3.7
Cons
p
icuities
(
ref.
7.4
Issue
r
3.4
Ca
p
acities
7.5
Ex
p
iration date
3.4.1
Total rate
8
A
ssociated
p
artners
3.4.2
Available rate
8.1
A
ssociated
p
artner
3.4.3
Available time
8.2
Associated
p
artner
3.5
Cons
p
icuities
(
ref. 7
)
8.3
General data
(
ref. 1
)
8.4
Contact data
(
ref. 2
)
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315
3 SPECIFICATION OF
ONTOLOGY
We have introduced the concept of “VBE ontology”
in (Afsarmanesh and Ermilova, 2007). As such, the
VBE ontology represents a form of unified and
formal conceptual specification of the heterogeneous
knowledge in VBE environments to be easily
accessed by and communicated between human and
application systems, for analysis and evolution
purposes. The main objective of the VBE ontology
is the provision of support for knowledge modeling
and management in VBEs, particularly for: (1) the
establishment of a common semantic subspace for
VBEs, (2) the development of VBE knowledge
repositories, (3) the processing VBE knowledge by
software tools, (4) the enabling inter-organizational
learning & operation, and (5) the integratability of
VBE knowledge.
The structure of the VBE ontology consists of
four levels of abstraction and ten partitions
constituting sub-ontologies of the VBE ontology, as
also illustrated in Figure 1. The four levels of
abstraction, including the “meta” level, the “core”
level, the “domain” level, and the “application”
level, reflect the reusability of the VBE ontology at
different VBE domains and application
environments. For example all VBE applications
from the same domain share the ontology defined
for all the above levels and differ only at the
application level. However the ten ontology
partitions, such as the “VBE profile and competency
sub-ontology”, the “VBE history sub-ontology”, the
“VBE Bag of Assets sub-ontology”, etc., mainly
refer to the knowledge about the fundamental
entities and concepts in VBEs as being addressed by
different VMS sub-systems (Afsarmanesh et al,
2007). Please also note that in Figure 3, the
number/symbol represented inside the parenthesis
next to each ontology level represents the cardinality
of instances for this VBE ontology level, e.g. there is
only one VBE meta level ontology and one VBE
core level ontology common to all VBEs, while N
and M both represent “many”, e.g. addressing the
fact that there are many different domains / business
area ontologies for different VBEs and each VBE
domain / business area may in turn also have many
VBE application ontologies. Furthermore, the
decomposition of this ontology structure into levels
and partitions supports the incremental development
of the VBE ontology, and the developed parts of the
ontology can be reused by VBE management
subsystems for different VBEs.
VBE ontology aims at providing the
specifications for all VBE knowledge classes
through presenting the following properties for each
classes: (i) a definition of the concept, represented
by this class, (ii) a set of synonyms for the concept,
(iii) an abbreviation for the class’s name, (iv) a set of
subclasses of this class, and (v) a set of attributes /
relationships of this class.
Figure 1: Structure of the VBE ontology.
While the meta and core VBE ontology levels
are specified and constructed manually, the domain
and application VBE ontology levels must be
constructed on demand for each specific VBE
domain and application. The VBE core level
represents ten separate OWL (OWL, 2007) files that
were constructed using Hozo (Sunagawa et al, 2004)
and Protégé (Protégé, 2007) editors.
To support the modelling and management of
VBE member organizations’ knowledge, we use the
core and domain levels of the “VBE profile and
competency sub-ontology” as addressed below:
VBE core profile and competency sub-ontology
(Figure 2) represents the core/generic profile and
competency model, as addressed in Table 1. The
main purposes and usage of the VBE profile and
competency core sub-ontology for the modelling and
management of organization’s knowledge in VBEs
includes the following: (1) Support of the R&D in
the VBE field through providing means for the
evolution of the organizations’ profile and
competency model by being an extensive, uniform
and sharable representation of these models. (2)
Support for the common understanding of the
structure of the profiles and competencies through
providing the extensive definitions of the related
concepts. (3) Support for semi-automated design and
development of a database for organizations’
knowledge (Guevara-Masis et al, 2004). (4) Support
for automatic structuring of the profile and
competency knowledge for its representation in a
GUI.
VBE domain profile and competency sub-
ontology is a form of representation of the domain
classification for the organization’s profile and
competency model. The VBE domain profile /
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316
competency sub-ontology can be further partitioned
into several specific “sub-sub-ontologies” depending
on: a specific core concept (e.g. only for domain
resources), or a specific domain / business area (e.g.
only for welding). Figure 3 illustrates a sub-ontology
developed for the classification of
standards/practices and capabilities/processes in the
metalworking domain. The usage of the VBE profile
domain sub-ontology includes the following:
(1) Support of the representatives of the VBE
member organizations with the definition of their
domain-specific profile and competency related data
(e.g. identification of classes of the domain-specific
business processes performed within an
organization). (2) Support for representation of the
“standard names” and the “standard relationships”
for the domain-dependent profile and competency
knowledge that can be further facilitate the software-
based matching/processing of the knowledge. (3)
Support for automatic structuring of the domain-
dependent profile and competency knowledge in a
GUI.
Figure 2: Example partial screen-shot from the profile core
sub-ontology constructed in Hozo.
Figure 3: Example screen-shot from the ontology for
metalworking capabilities constructed in Protégé.
4 MANAGEMENT
FUNCTIONALITIES
Management of VBE member organizations
profiles and competencies takes place through the
entire VBE life cycle (i.e. from the VBE creation
stage to the VBE dissolution stage). In this paper we
introduce eleven main functionalities identified for
the organizations’ profiles and competencies
management, as illustrated in Figure 4. All
functionalities are activated at different VBE life
cycle stages. In Figure 4, these activation times are
indicated next to each functionality’s name as
follows: [C] for the VBE creating, [O] for the VBE
operation, [E] for the VBE evolution, [M] for the
VBE metamorphosis, and [D] for the VBE
dissolution. Detailed description of the
functionalities, as well as of their support by the
VBE profile and competency sub-ontology, is
addressed below:
1. Uploading of Core Profile and Competency
Model. This functionality supports the uploading
and installation of the core profile and competency
model in the form of the VBE core profile and
competency sub-ontology and adaptation of the
database to this model (see Section 5 for more
details).
2. Customization of the Core Profile
Competency Model. This functionality supports
modification of the organizations’ profile and
competency models.
Figure 4: PCMS’s life cycle functionalities.
3. Uploading of Domain Classifications. This
functionality supports uploading and installation of
generalization hierarchies of specific domain classes
of profile and competency knowledge in the form of
VBE domain profile and competency sub-sub-
ontologies.
4. Customization of Domain Classifications. This
functionality supports updating of the generalization
MANAGING CHARACTERISTIC ORGANIZATION KNOWLEDGE IN COLLABORATIVE NETWORKS
317
hierarchies of domain classes, for example through
adding new domain-dependent profile knowledge
classes needed in a specific VBE application.
5. Registration of Organizations. This
functionality supports registration of VBE member
organizations in the system and thus creation of their
profiles.
6. Submission of Profile and Competency
Knowledge. This functionality supports uploading
of profile and competency knowledge from each
member organization. For each “piece” of
knowledge, the class of this knowledge needs to be
indicated in the VBE profile and competency sub-
ontology.
7. Viewing of Profile and Competency
knowledge. This functionality supports viewing the
profile and competency knowledge accumulated in
the VBE. The viewing scope shall address both:
single profile knowledge and the collective profile
knowledge of the entire VBE. The structuring of the
knowledge in the GUI (see Section 5 for more
details) shall mimic the VBE profile and
competency sub-ontology.
8. Matching/searching of Profile and
Competency Knowledge. This functionality
supports both: the search for specific profile
elements and the matching of the profile and
competency descriptions of a group of VBE member
organizations against the detailed descriptions of the
new collaborative opportunities arisen in the VBE.
In case there is no “direct” search results for some
specific knowledge classes, “alternative” results for
“other” knowledge classes shall be suggested, based
on the closeness of knowledge classes in the VBE
profile and competency sub-ontology.
9. Transmission of Profile and Competency
Knowledge. This functionality supports the
transition of VBE member organizations’ profile and
competency knowledge to a special format in order
to support its transmission to other VBEs, external
institutions, or further VBE-related activities in the
market and society.
10. Transmission of Core Profile and
Competency Model. This functionality supports
transmission of the customized core profile and
competency model of one VBE to the format of the
VBE core profile and competency sub-ontology in
order to support its usage by the R&D organizations
working on the evolution of the generic/core VBE
profile and competency model.
11. Transmission of Domain Classifications. This
functionality supports transmission of customized
generalization hierarchies of domain classes of one
VBE to the format of the VBE domain profile and
competency sub-sub-ontologies in order to support
its inheritance by other VBEs from the same domain
or by the R&D organizations working on the
evolution of these generalization hierarchies.
5 DATABASE SCHEMA AND
VISUAL INTERFACE
The design of the database for the VBE member
organizations’ knowledge aims to support both
structuring of members’ profiles/competencies based
on the VBE profile and competency sub-ontology,
and dynamic customization of the profile and
competency model, i.e. through dynamic
creation/deletion of knowledge classes and
instances. As the DBMS, the PostgreSQL
(PostgreSQL, 2007) has been initially chosen
because it represents a strong free-ware system, for
which the needed drivers and documentation are
available. A number of logical/physical database
designs have been considered and evaluated.
However, the approach, where one physical table in
the relational database corresponds to one class of
the profile knowledge has been rejected. The main
reason for the rejection is the need to support the
dynamic creation/deletion of database tables,
resulted from the requirement of dynamism in
profile knowledge classes. Such dynamic operations
with tables are however problematic in PostgreSQL.
Instead, we chose for a more object-oriented design
of the database with only one generic table for all
knowledge classes. Thus, all operations with classes
simply represent the operation with the records in
this table. The final database schema consists of
seven tables/relations (as illustrated in Figure 5).
The main four tables include: (a) the “class” table
for representing the profile knowledge classes; (b)
the “instance” table for representing the profile
knowledge instances (i.e. real data from VBE
entities); (c) the “class_relation” table for
representing different relationships among the
classes; and (d) the “instance_relation” table for
representing the relationships among the instances.
The other three tables are meta-data defined above
these four tables.
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318
Figure 5: Database schema.
Suitable user-friendly graphical user interface
of the profile and competency knowledge is required
to support their navigation, understanding and
“digestion” by human users. The more complex the
knowledge is the more difficult it is to specify
proper form for its visualization. Below we
introduce some “catalogue” forms for the profile
knowledge visualisation. Each catalogue mimics the
visualization of an acyclic graph including the
knowledge “classes” (representing both: core classes
and domain classes) and their “instances” as its
“nodes”, and three types of relationships as its
“edges”. These relationships include: (1) the
generalization relationship (called “has subclass”)
defining classes and their subclasses, (2) the
aggregation relationship (called “has attribute”)
defining classes and their attributes, and (3) the
instantiation relationship defining classes and their
instances. Two types of catalogues, namely the
PKCC and PKIC that are described below, are
designed for representing the VBE member
organizations’ profile and competency knowledge.
In relation to illustrations of PKCC and PKIC,
addressed in Figures 6 and 7, please notice that in
both catalogues described and illustrated below, the
bold entries represent knowledge classes. The
image
, located next to a class, indicates that this
class is a sub-class of its upper-level class. In the
PKCC, absence of this image next to a class
indicates that this class is an attribute of its upper-
level class. In the PKIC, the italicized entries
represent dome instance’s data for attributes, also
called “records”. The image
in the PKIC
indicates a record. In both catalogues, the smaller-
font entries next to the class entries, e.g. “14
attributes”, “2 subclasses”, “2 records”, represent the
summary for the class definitions. The clickable
images
and support expanding and collapsing
the catalogue items. A radio-button
next to each
entry supports the selection of an entry to perform
some operations on it (e.g. creation of a new
subclass, a new attribute, or a new instance).
The Profile Knowledge Class Catalogue (PKCC)
represents an acyclic graph including the profile
knowledge classes as the nodes, and the
relationships among these classes as the edges. The
root of PKCC is represented by an abstract class
called “Profile knowledge”. The relationships
among the classes represent two types of the “has
attribute” relationships (e.g. a competency definition
has a capability definition as an attribute) and the
“has subclass” relationships (e.g. the manufacturing
capability has the welding capability as a subclass).
An example illustration of the PKCC is addressed in
Figure 6. This Figure illustrates the list of the top-
level classes of profile knowledge such as “General
data”, “Contact data”, “Resource”, etc. The
representation of two classes, namely the “General
data” class and the “Resource” class are also
expanded, so that the fourteen attributes of the
“General data” class, as well as one attribute and
four sub-classes of the “Resource” class, can be
viewed.
Figure 6: An example illustration of PKCC.
Figure 7: An example illustration of PKIC.
The Profile Knowledge Instance Catalogue
(PKIC) represents an acyclic graph that includes the
profile knowledge classes and the profile knowledge
instances as the nodes, and the relationships among
these classes and instances as the edges. Please note
MANAGING CHARACTERISTIC ORGANIZATION KNOWLEDGE IN COLLABORATIVE NETWORKS
319
that the relationships among classes in PKIC
represent only the “has subclass” type, the instances
are not connected among each other directly, while
the main types of the relationships in this models
represent the relationships between classes and their
instances. An example illustration of the PKIC is
addressed in Figure 7. This Figure illustrates a list of
classes for the profile knowledge and some existing
records for these classes from a member
organization of Swiss Microtech (SMT) - a VBE
from Switzerland. In this Figure, the record for the
“General data” is expanded, so that the records for
its attributes can be viewed, For example, the record
for the “Creation date” is “1956”. It also illustrates
that the “Resource” class does not have direct
instances/records, rather it has records only through
one of its sub-classes, e.g. through the “Human
resource” class.
6 CONCLUSIONS
This paper addresses an approach for ontology-
based modeling and management of characteristic
knowledge collected from organizations/companies
collaborating in CNOs and specially in VBEs. Each
organization is presented in VBEs by its “profile”
and specifically by its “competency” - a fundamental
element of the profile. This paper starts with the
definitions of organizations’ profiles and
competencies. It addresses the motivations,
requirements, and technical challenges for ICT-
based modeling and management of organizations
profiles and competencies in VBEs. It introduces the
“VBE profile and competency sub-ontology” to
support modeling and management of organizations’
knowledge in VBEs. Furthermore, the functionalities
for profile and competency management are
presented that are based on the ontological
representation of the organization’s knowledge
model. As steps required for specification and
development of the Profile and Competency
Management System (PCMS) for VBEs, the designs
of both: the database and the GUI for profile and
competency knowledge, are addressed. More details
about specification and modeling of organizations’
competencies as well as about the PCMS’s
development fall outside the scope of this paper, and
are the topics for forthcoming papers.
ACKNOWLEDGEMENTS
The work on this paper is supported in part by the
FP6 IP project ECOLEAD, funded by the European
Commission.
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