=Paper=
{{Paper
|id=Vol-125/paper-13
|storemode=property
|title=Towards Ontology-Driven Institutional IS Engineering
|pdfUrl=https://ceur-ws.org/Vol-125/paper10.pdf
|volume=Vol-125
}}
==Towards Ontology-Driven Institutional IS Engineering==
https://ceur-ws.org/Vol-125/paper10.pdf
Towards Ontology-Driven Institutional IS Engineering
Slim Turki 1,2, Christine Aïdonidis2, Abdelaziz Khadraoui 1,2, Michel Léonard1
1 MATIS Geneva Team – University of Geneva
Uni-Dufour, rue du Général Dufour 24, CH-1211 Genève 4, Switzerland
{slim.turki, abdelaziz.khadraoui, michel.leonard}@cui.unige.ch
2 Centre des technologies de l'Information - Etat de Genève
Architecture des Systèmes d'Information
Grand-Pré 70, C.P. 149, CH-1211 Genève 8, Switzerland
{slim.turki, christine.aidonidis, abdelaziz.khadraoui}@etat.ge.ch
Abstract. An information system (IS) is a support for business activities and it
is necessary to take into account the knowledge context of these activities. Use
of ontologies is one of the ways to consider this kind of knowledge. They
allows to define and to position concepts that describe the domain, in order to
design and to drive the IS. Moreover, ontologies help to overcome the limits of
IS interoperability, evolution and openness. In this paper, we consider
ontology-driven IS engineering and we present our modeling frame to use
ontologies. This frame is used in the case of institutions, where activities are
governed by laws.
Keywords. IS engineering, institutional IS, ontology, legal ontology.
1. Introduction
Information system (IS) engineering is quite different from software engineering:
an IS has to support efficiently human activities, tasks, and responsibilities. These
activities are interwoven with the IS to be efficient. Persons, who assume these
activities, are not simple IS users: their responsibilities, their way of thinking about
their activities, their own efficiency depend on the IS (Léonard, 2003). As we
consider IS as a support for business activities, it is necessary to take into account the
knowledge context of these activities. However, the knowledge of a domain is often
scattered. It can be formal, specified for example in work documents or embedded in
implemented applications; or informal where a big part of the knowledge is kept in
human minds.
Ontologies are often proposed as a way to take into account business knowledge
under many points of views: the linguistic one, the artificial intelligence one, the
encyclopedic one, and-so-on. Even if ontologies are proposed to normalize the
meaning of things, the term “ontology” itself is not clearly defined. No agreement
exists on the exact meaning of the term (Gruber, 1995). Of course, there is a relative
consensus around the origin of the term. The word “ontology” has a very long history
This work is supported by:
1. the State of Geneva, Switzerland,
2. the INTEROP, NoE (Partner #52), and
3. the University of Geneva, Switzerland.
2 Slim Turki1,2, Christine Aïdonidis2, Abdelaziz Khadraoui1,2, Michel Léonard1
in philosophy starting with the Aristotle’s works. Defined as “the science of being”, it
comes from the Greek “ontos” which means being and “logos” for both language and
reason (Roche, 2003).
In the context of IS engineering, we consider ontologies as a way to make an
inventory, to define and to position the concepts that describe the knowledge of a
specific domain, in order to design and to drive the IS supporting the activities of this
domain. Ontologies in this case have to remain independent from technologies and
business practices. Moreover, ontologies help to overcome the limits of IS
interoperability, evolution and openness due to the growing number of disparate
modeling methods, paradigms, languages and software platforms. In this paper we
consider ontology-driven IS engineering.
Figure 1 represents our vision of ontology-based IS engineering: we are interested
only by concepts that can be projected on the world of activities and on the world of
implementation through the world of information.
Concepts
Activities Information Implementation
Fig. 1. Positioning concepts in IS engineering
In the case of institutions, it is possible to benefit from a particular source of
knowledge such as laws. Activities in institutions are governed by a legal frame
represented by a set of laws that regulates their execution. Laws also are indisputable
and “nobody should ignore law”. Laws constitute a real source of knowledge
describing in a precise way concepts, rules and constraints governing the treated
domain. Consequently, an IS intended to support the activities of institutional
professions has to conform to this legal frame.
In this paper, we present an innovative approach to design institutional IS based on
a legal frame. In section 2, we present the context of this work. In section 3, we
present our modeling frame to consider ontologies in IS engineering. This frame
includes a conceptual map model to represent ontologies and a set of mapping
guidelines from conceptual maps into other object specification formalisms. In section
4, we present how we deal with texts of law to (re-)build the cognitive space of an
institutional domain. Section 5 describes how we use ontologies to drive institutional
IS (re-)engineering projects. Finally, section 6 draws some conclusions and
discussions about our future work.
Towards Ontology-Driven Institutional IS Engineering 3
2. Context of the work
The work presented in this paper has been led in the context of collaboration
between the State of Geneva and the University of Geneva. This collaboration
concerns the re-engineering of an ERP used in the social domain. More exactly, it
consists in re-building the informational level of an existing ERP and proposing an
alternative solution, i.e. a new IS, while ensuring the migration of the data from the
ERP databases to the new IS in less than nine months.
The new IS has first to interoperate with the legacy ERP to be initiated with the
existing data. Next, in the perspective of the sustainable development, the new IS has
to interoperate with the dozen of others IS used in the social domain at the State of
Geneva.
Interoperability is the ability of two or more sys tems or components to exchange
information and to use the information that has been exchanged (IEEE, 1990). In the
public administration such as the State of Geneva, which has six hundred applications
and more than thousand databases, interoperability cannot be only a technical
challenge (it would be too easy!!!). To establish an information exchange between
these different systems, it is necessary (i) to have the semantic view and the
description of the available or exchanged information and (ii) to ensure the conceptual
mapping between different pieces of such information.
The modeling frame that we present in section 3 have been developed in this
context. The aim of this frame is to allow a reliable and no-ambiguous representation
of the concepts of the studied domain. An object oriented informational model can be
faithfully used for this purpose.
Due to the sensitive nature of the social domain at the political level, we did not
have any direct access to the business activities of the State of Geneva. To resolve this
problem, we have used laws to re-build the ontological level of the social domain.
3. Modeling frame
For centuries, graphic notations have been used to represent knowledge, as much in
the domains of the logic, the linguistics as in the psycho logy or the philosophy (Sowa,
1984). By 1960, at the beginnings of artificial intelligence, “network notations” were
one of the first representations of knowledge. By 1970, the semantic networks were
more and more developed. By 1980, due to the increase o f the complexity and the size
of applications, there was a need to organize and to publish the contents of knowledge
bases. Semantic networks have been used then as a mechanism to represent
knowledge. Some of these networks have formal logic bases whereas others are much
more informal. In spite of their differences, they characterize a family of knowledge
representation systems.
We are particularly interested by the conceptual graphs. They represent a logical
system based on the semantic networks (Sowa, 1991). A conceptual graph is a
formalism allowing to model the knowledge of a domain through concepts and
4 Slim Turki1,2, Christine Aïdonidis2, Abdelaziz Khadraoui1,2, Michel Léonard1
abstract relations. A concept is a node representing knowledge or an object. A
conceptual relation is an arc linking various concepts.
In our work, we use a conceptual graph namely conceptual map to represent the
knowledge of a specific domain. In the next sub-sections, we detail our modeling
frame based on the use of ontologies in IS engineering. This frame has two levels: (i)
the meta-model to represent ontologies, and (ii) the classical specification level
representing the informational space on which the IS will be built.
3.1. Conceptual map
Our approach aims to (re)-construct the cognitive level of the domain under
consideration. It consists in making an inventory, defining and positioning the
concepts that describe the knowledge of the domain, independently of technologies
and business practices. A domain may be an activity, a problem or a discipline in
which a language called a “particular language” (as opposed to the general language)
exists and is used by the experts of the domain (Bonjour, 1994).
We model the knowledge of a domain, called a cognitive space, by using a
conceptual map. A conceptual map takes the canonical shape of an oriented graph
where nodes are concepts and arcs are either (i) instanciations or (ii) existential
dependencies or (iii) generalization-specialization links.
Before to present the meta-model of our conceptual map (section 3.1.2), we
introduce the application example that we use to illustrate this paper (section 3.1.1).
3.1.1. Example of conceptual map
Ressource
Allowance
Allowance Citizen
Demand
Residence
Allowance
Allowance
nominal value Beneficiary
Validation
Allowance
Value= 13812 CHF granted value
A B The concept A depends existentially of the concept B
A B The concept A is a specialization of the concept B
A B The concept A is an instance of the concept B
Fig. 2. Allowance attribution conceptual map
Towards Ontology-Driven Institutional IS Engineering 5
Let us consider the social assistance attribution example (figure 2). An allowance
has a nominal value. A citizen has to submit an allowance demand. The citizen has to
communicate his residence and resource. This demand is examined. If the demand is
validated, the citizen becomes a beneficiary and receives an allowance with a granted
value.
3.1.2. Conceptual map meta-model
Figure 3 represents our meta-model for conceptual maps. As shown in this figure,
the main element of this meta-model is called a concept.
A concept is a general and abstract representation of an object or a set of objects. A
concept is defined according to its understanding and to its extension. It represents
proprieties of the reality or a unity of thinking based on a set of proprieties allocated
to an object or class of objects. A concept is described by a term, a definition and one
or several attributes. A term is a way to designate the concept in the language of the
expert of the corresponding domain. It can be accompanied with several synonyms.
The definition of the concept is given as a text in natural language. An attribute is a
property of the concept having a name and a type. In our example illustrated in figure
2, the concept citizen, identified by the term “citizen”, is defined as “an inhabitant of
the State of Geneva” and has three attributes (not represented in figure 2): name, date
of birth and family status.
In our model, a concept can be an instance of another concept. The instance, itself
considered as a concept, takes then values for each of the attributes of the instantiated
concept; the relation of instanciation is a sharing of values. For example (figure 2),
the concept allowance nominal value is instantiated by the concept value.
ConceptMap
idMap ConceptInMap
mapDescription
dependentConcept existentialDependency
specializationOf Concept idDependency
Specialization idConcept
idSpecialization conceptDescriptiont
conceptTerm referencedConcept
generalizationOf
ClassB is a ClassB
attributeOf specialization depends
Attribute of ClassA existentially of
Instance ClassA
instanceOf attributeName
idInstance ClassA
attributeType ClassA
valueTakenByInstance attributeValue valuetakenForAttribute
attValue ClassB ClassB
Fig. 3. Conceptual map meta-model
A concept can be the generalization or the specialization of another concept. The
generalization-specialization relationship is useful to classify concepts according to
their common properties or their specificities. The generalization-specialization
6 Slim Turki1,2, Christine Aïdonidis2, Abdelaziz Khadraoui1,2, Michel Léonard1
relationship is a sharing of representations. In our example (figure 2), the concept
beneficiary is the specialization of the concept citizen.
A concept C1 depends existentially on the concept C2 if the existence of C1 is
related to the existence of C2: if the concept C2 disappears then the concept C1
disappears too. In our example (figure 2), the concept allowance demand depends
existentially of the concepts allowance and citizen. If one of these concepts is
removed, allowance demand is also removed.
The generalization-specialization relationship is a particular case of existential
dependencies.
The particularities of our conceptual maps are:
− It does not distinguish the static aspects from dynamic aspects (static-
dynamic integration). These choices concern the implementation level and
not the ontological one.
− It uses only existential links, generalization-specialization links and
instantiation links. These links are easy to understand, they do not introduce
ambiguity and can be directly implemented.
− Instances of a concept are considered as concepts. This allows building
multi-level conceptual maps. The conceptual maps are different from the
class diagrams where only two levels of abstraction are used: class and
instance (object).
The conceptual map meta-model shown in figure 3 is being implemented as an
extension of the CAISE tool M7 opensource (Estier, 1995) and the CASE tool Mega
(Mega).
3.2. Informational model
Informational model is an intermediate and unavoidable stage to go from the
cognitive space towards the effective implementation. It allows to transform a concept
into information before translating it into computer object(s). It is not a question of
modeling business activities or computerized treatments, but designing the IS kernel,
that is the information space the IS will be built on. Therefore, our approach consists
in transforming a cognitive model of the domain under consideration, represented by
a conceptual map, into a relevant and no-ambiguous object informational model,
before envisaging the implementation.
The object-oriented methods introduce the concept of object gathering structure of
data, controls and processing and propose a total specification of an IS via
complementary static and dynamic specifications. Object informational models are
generally built around diagrams of classes on which are grafted object life cycles and
sets of integrity rules.
A class diagram describes classes endowed with attributes, with keys and with
methods, as well as the associations between the classes.
The diagrams representing object life cycles specify the rules of evolution of the
class objects. There are several ways to express the dynamic specifications of an IS
Towards Ontology-Driven Institutional IS Engineering 7
such as the state charts diagrams in UML (Booch, 2000) or the Petri nets (Petri, 1962)
and-so-on.
Integrity rules (or constraints) are conditions defined on one or more classes of the
IS, validated algorithmically. Their role is to preserve the coherence, correctness and
consistency of an IS during its exploitation.
3.3. From a conceptual map to an informational model
The derivation of an informational model from a conceptual map has to associate
to each concept or link between concepts its representation in terms of object-oriented
schema elements, describing the form taken by an instance of the concept within the
underlying implementation of the IS. Figure 4 illustrates the derivation process.
Ontological level
Informational level
Implementation level
Fig. 4. Global derivation process
The derivation process asks the IS designers to consider the IS with an operational
optic. They have to make choices and to decide for each concept and each link
between concepts if it is mapped into a class, or a transaction or an attribute, etc.
Citizen
Allowance name
AllowanceDemand birth_date
nominal_value
family_status
effect_date
residence
ressource
ValidatedAllowance
Beneficary
granted_value
Fig. 5. A possible partial class diagram in UML derived from the concepts map of figure 2
Figure 5 represents a possible specification (in UML) that can be derived from the
conceptual map of figure 2.
8 Slim Turki1,2, Christine Aïdonidis2, Abdelaziz Khadraoui1,2, Michel Léonard1
The following list enumerates a set of elementary derivation guidelines that we
have identified. The mentioned examples reference the case illustrated in figure 2 and
figure 5.
Concept → class
In the simplest case, a concept is mapped to a class; an object represents an
instance of the concept. For example, the concepts citizen, allowance demand and
beneficiary are converted into three respective classes.
If a concept C1 depends existentially of a concept C2 and if C1 and C2 are
transformed respectively into two classes cl1 and cl2, then cl1 depends existentially of
cl2. For example, the class demand allowance depends existentially of the classes
citizen and allowance .
If a concept C1 is a specialization of a concept C2 and if C1 and C2 are converted
respectively into two classes cl1 and cl2, then cl1 is a specialization of cl2. For
example, the class beneficiary is a specialization of the class citizen.
Concept → attribute
If a concept C1 depends existentially of C2, C1 may be converted into an attribute
att1 of a class cl2 derived from a concept C2. In our example, Resource and residence
become attributes of the class citizen.
Each instance of a concept is then transformed into a possible attribute value.
Concept → instance
An instance of a concept can be converted into a set of objects.
If a concept C1 is an instance of a concept C2 and if C2 is converted into a class cl2
then C1 is converted into an object o1 instance of cl2.
If a concept C1 is an instance of a concept C2 and if C2 is converted into an
attribute att 2 then C1 is converted into a possible value of att2. For example, there is an
object of the class allowance having “13812 CHF” for the attribute value.
4. Development of a cognitive space from legal sources
The institutional domain has a particularity: institutional activities are governed by
a legal frame expressed in term of laws. Consequently, the IS intended to support
these activities has to conform to this legal frame. We use laws as a source of
knowledge to analyze and construct the cognitive space of an institutional domain.
For a given domain, we propose at first, to identify the set of the legal texts such as
laws, application regulation, civil code, etc. which formalize the domain. The study of
each of these texts should be made only in the perspective of IS engineering. Laws are
not made to design IS. The texts of laws contain information and knowledge of purely
legal nature, which cannot be considered in the IS. Only the characteristic concepts of
the domain are identified and retained. The textual character of documents allows us
to collect information such as the semantic definition of concepts in the language of
the corresponding domain, synonyms, homonyms and values of attributes. The
Towards Ontology-Driven Institutional IS Engineering 9
respect of the concepts semantics permits to preserve the granularity of these concepts
and to ensure conceptual allegiance with the reality of the domain.
The finalization of the conceptual map requires establishing relations between
concepts. As mentioned in section 2.1.2, to our meta-model defines only three types
of links: (i) instanciation, (ii) generalization-specialization and (iii) existential
dependencies:
− Remarkable values such as tables, limits, scales, etc. discovered during the
analysis of texts, are instanciations of some original concepts.
− According to the common points between concepts and the specific
characters of each of them, the classification of concepts allows to put in
evidence the links of generalization-specialization.
− If the existence of a concept is directly subordinated to the existence of
another concept, then an existential dependency is established between these
two concepts.
In such a manner, a conceptual map is established from a unique source of
information, legal texts, and so a cognitive space of the domain is (re-)established.
The example proposed of figure 2 is a real case elaborated from the articles 10 and
12 of the Genevan law “RMCAS”.
Laws evolve but this does not mean that everything in law modeling is at variance.
The major conceptions of what law is about remains relatively stable over decades of
legal practice and jurisprudence, and can be expressed in the form of an ontology
(Valente, 1995). Ontologies allow to drive the evolution of the underlying IS to adapt
it to the evolution of the legal frame.
In the case of our project, where it was impossible for us to access the directly the
activities of the domain, the laws have been a relevant and reliable source of
knowledge that allowed us to re-build the ontological level of the domain.
5. Ontologies for IS interoperability
Interoperability is the ability of two or more systems or components to exchange
information and to use the information that has been exchanged (IEEE, 1990). In IS
engineering, heterogeneity of modeling methods, paradigms, languages and software
platforms are not the only brake to interoperability. The lack of semantic view and
description of the available or exchanged information is also an obstacle to IS
interoperability.
Ontologies, which represent a common, sharable view of the systems domain, are
used to give meaning to the information structures that have to be exchanged between
systems (Missikoff, 2002). Ontologies offer a common language to describe the
semantic of the underlying systems.
10 Slim Turki1,2, Christine Aïdonidis2, Abdelaziz Khadraoui1,2, Michel Léonard1
5.1. Ontologies in IS engineering
If an explicit ontology plays a central role in the IS development, we call it
ontology-driven IS engineering. In this case, the ontology drives all aspects and
components of the system (Fonseca, 1999). In ontology-driven IS, the ontology is
called application ontology and it is a specialization of domain ontology and task
ontology (Guariano, 1998).
Building IS using ontologies allows not only to obtain IS better adapted and
adequate to the activities world that it supports, but also to have available a
conceptual map defining and positioning the concepts of the corresponding domain.
The conceptual map can be used to drive on one hand the evolution of the IS, and on
another hand its interoperability, integration and its openness.
5.2. Ontologies in IS re-engineering
We describe here, in short, how we used ontologies to drive an institutional IS re-
engineering project. Indeed, the presented approach was applied in the case of a social
IS re-engineering project carried out at the State of Geneva.
Law Text
Law Text
Title I
LawTitle IText
Art.1 : "..............................."
Title I
Art.1
Art.2 : "..............................."
: "................................"
Art.1
Art.2 : "..............................."
: "................................"
Art.2 : Title II
"................................"
Title II
Art.3 : "..............................."
Title II
Art.3
Art.4 : "..............................."
: "................................"
Art.3
Art.4 : "..............................."
: "................................"
Art.4 : "................................"
MAPPING
Ontological level
Ontological level
Informationnal level
Informationnal level
Implementation level
Implementation level
Legacy IS New IS
Fig. 6. Ontology driven IS re-engineering
Figure 6 illustrates our ontology-driven IS re-engineering process. As shown in this
figure, we established first the conceptual map of the legacy IS. This conceptual map
describes the set of concepts that led to the implementation of the legacy IS.
Next, the corresponding laws were modeled to establish conceptual map of the new
IS. This conceptual map was used to specify and to implement the new IS, and
especially to build the database schema which support it.
For each IS to rebuild, one of the main challenges is to recover data from the
legacy IS. To achieve this migration, we needed to carry out the mapping between the
Towards Ontology-Driven Institutional IS Engineering 11
two conceptual maps, in other words, between the two ontologies: the ontology
related to the legacy IS and the ontology related to the new IS.
6. Conclusion
Often, interoperability was reduced to its technological aspects while neglecting
the conceptual and semantic aspects of the implemented elements. Nowadays,
ontologies are unavoidable to drive the IS interoperability, evolution and openness.
In this paper, we presented our modeling frame considering ontologies in IS
engineering. We also presented how we deal with the legal frame of institutional
domains to (re-)build their cognitive spaces or to drive the (re-)engineering of
institutional IS.
This paper presents an ongoing work that we continue to develop. The modelling
frame is still under construction and need to be positioned in comparison to other
existing models. The proposed set of derivation guidelines has to be completed and
improved with qualitative criteria.
In the case of institutional IS, laws constitute a precious source to establish the
ontologies of such IS, and open up new perspectives for their driving.
Our future preoccupation is to continue the improvement of the conceptual map
meta-model and the development of specific tools to support the use of ontologies in
institutional IS engineering.
Due to the huge number of activities of an Enterprise, which have to be supported
by an IS, it is more and more difficult to obtain a pertinent global view of an IS, to
distinguish its different parts and to identify the overlaps between these parts
(Léonard, 2003). It is henceforth indispensable to reason in term of components and in
term of overlaps between these components. It is a question of method to work with
models of cognitively human size. We are working on a component based IS
engineering method, that to be effective, has to address in a global approach the
different levels of the IS.
Acknowledgement
We would like to address our special thanks to J.-M. Leclerc, Director of the CTI –
State of Geneva for his support and encouragement.
We would like to thank C. Visentin, T. Ben Hamadi and M. Singarella for their
collaboration during the prospecting phases of the project “Model-based IS Re-
Engineering”, M. Lai and J. Ralyté for their help during the preparation of this paper.
We are also grateful to the anonymous reviewers of our paper. Their comments
were very constructive and helpful to improve the quality of our work.
12 Slim Turki1,2, Christine Aïdonidis2, Abdelaziz Khadraoui1,2, Michel Léonard1
Bibliography
(Abrial, 1974) Abrial J.R., Data Semantics, in Database Management, J.W. Klimbie & L.L.
Koffman (eds), North-Holland, 1974.
(Bar, 1998) Bar M., Enjeux de la modélisation des systèmes d'information légistiques. XVIème
Congrès INFORSID’98, Montpellier, 1998.
(Bonjour, 1994) Bonjour M., Falquet G., Concept Bases: A Support to Information Systems
Integration. in Proc. of CaiSE 1994, Utrecht.
(Booch, 2000) Booch G., Rumbaugh J., Jacobson I., The Unified Modeling Language User
Guide, Addison-Wesley 2000.
(Chein, 1992) Chein M., Mugnier, M.L., Conceptual Graghs: Fundamental notions. Revue
d’intelligence artificielle, Volume 6 – n4/1992, pp365-406, 1992.
(Colomb, 1998) Colomb R. M., Completeness and Quality of an Ontology for an Information
System. In Proc. FOIS'98, Trento, Italy, 6-8 June, 1998. IOS-Press.
(Estier, 1995) Estier, Th., Intégration des spécifications dans la conception des systèmes
d'information. PhD Thesis, Université de Genève, 1995.
(Fonseca, 1999) Fonseca F.-T., and Egenhofer M.-J., Ontology-Driven Geographic Information
Systems. 7th ACM SAGIS, November 1999.
(Gruber, 1995) Gruber T., Towards principles for the design of ontologies used for knowledge
sharing. International Journal of Human Computer Studies, 43: 907-928.
(Guarino, 1998) Guarino N., Formal Ontology and Information Systems. Proceedings of
FOIS’98, Italy, 6-8 June 1998. Amsterdam, IOS Press, pp. 3-15.
(IEEE, 1990) IEEE Standard Computer Dictionary.
(Léonard, 1999) Léonard, M., M7: An evolutive approach for the IS de-sign: the Gavroche
model, Conf. Inforsid, La Garde (F), May 1999.
(Léonard, 2003) Léonard, M., IS Engineering Getting out of Classical System Engineering. In
Proc. ICEIS’03, Angers, France, pp. 35-45, 2003.
(Mega) http://www.mega.com
(Missikoff, 2002) Missikoff M., Harmonise: An Ontology-Based Approach for Semantic
Interoperability. ERCIM News No. 51, October 2002.
(Muller, 1997) Muller P.A, Modélisation Objet avec UML, Eyrolles 1997.
(Mylopoulos, 1990) Mylopoulos J., Borgida A., Jarke M. , Koubarakis M., Telos: Representing
Knowledge About Information Systems. ACM Transactions on Information Systems,
October 1990.
(Petri, 1962) Petri C.A., Communications with automata, New York 1966, translation of
Kommunikation mit Automaten, University of Bonn, 1962.
(Roche, 2003) Roche C., Ontology : a Survey, 8th Symposium on Automated Systems Based
on Human Skill and Knowledge. IFAC, 2003, Göteborg, Sweden.
(Sowa, 1984) Sowa J.F., Conceptual Structures: Information Processing in Mind and Machine.
Addison-Wesley, 1984.
(Sowa, 1991) Sowa J.F., Principles of Semantic Networks – Explorations in the representation
of knowledge. Morgan Kaufmann, 1991.
(Turki, 2003) Turki, S., Léonard, M., Arni-Bloch, N., From Hyperclasses to IS Components. In
Proc. of CE'2003, July 2003, Madeira, Portugal.
(Valente, 1995) Valente A., Breuker J., ON-LINE : An architecture for modelling legal
information. In Proc. of the Fifth ICAIL, 1995.