AN ALGORITHM FOR BUILDING INFORMATION SYSTEM’S

ONTOLOGIES

Mohamed Mhiri, Sana Chabaane, Achraf Mtibaa, Faïez Gargouri

MIRACL Laboratory, ISIM Institute, BP 1030-3018, Sfax. Tunisia

Keywords: Ontology, Information systems design, concept, semantic relationships, conceptual relationships.

Abstract: Current applications' modelling becomes increasingly complex. Indeed, it requires a hard work to study the

particular studied field in order to determine its main concepts and their relationships. The conceptual

representations (CR) results of the modelling of such applications can contain structural and semantic errors

which are not detectable by current CASE. The solution that we propose is to associate an ontology, for the

studied field, as a help to the designers during IS modelling steps. Building such information system’s

ontologies require the use of an approach allowing the determination of the concepts and the relationships

between these concepts. Using ontologies makes it possible to ensure conceptual representations' semantic

coherence for a given field. In this paper, we propose an algorithm for building an information system’s

ontology based on the comparison between the concepts and using a set of semantic relationships.

1 INTRODUCTION

Information systems are increasingly complex in

particular with the enormous growth of the volume

of data, the extraordinary evolution of the

technologies and the evolutionary requirements of

the users. Consequently, current applications

require an enormous effort of design and

development. In fact, such applications require a

detailed study of their fields in order to define their

concepts and to determine the concepts’

relationships. On the other hand, applications

become more and more cooperatives using data from

heterogeneous sources, in particular in the case of

the Web applications (e-learning, e-commerce,

semantic Web, etc). The designers are also

confronted with different conflits. A typology of

these conflicts is given in (Ouksel, 1999).

In the literature, several approaches were

proposed to solve the previously enumerated

problems in order to assist the designers in their

tasks of modelling, mainly to represent knowledge

during the process of design.

Recently, as an evolution and generalization of

those approaches, several authors proposed the

ontology concept. Ontology is now considered as the

most complete and adequate way for resolving the

major modelling conflicts. Indeed, an ontology is an

explicit specification of a conceptualization (Gruber,

1993).

In this paper, we present an algorithm for

building information system's (IS) ontologies. It is

organised as follows. In the second section, we

present our approach for building information

system's ontologies and the different semantic

relationships used. The third section presents our

framework and the corresponding algorithm for

building IS’s ontologies. We conclude this paper by

giving our future works.

2 OUR APPROACH FOR

BUILDING IS’S ONTOLOGIES

Our ontology building is based on the three steps

proposed by (Leclère, 2002) and which we adapt to

IS (Mhiri

, 2005). The conceptualization step

consists in identifying the knowledge contained in a

particular field. The ontologization step consists in

modelling, in a generic language, the formal

properties of the considered field. The last step, the

operationalization supports an inference mechanism

allowing the reasoning implementation.

An ontology’s modelling requires the

introduction of new semantic and conceptual

relationships which can exist between the concepts.

467

Mhiri M., Chabaane S., Mtibaa A. and Gargouri F. (2006).

AN ALGORITHM FOR BUILDING INFORMATION SYSTEM’S ONTOLOGIES.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - ISAS, pages 467-470

DOI: 10.5220/0002449304670470

 SciTePress

A lexical study of different domains allows as to

identify the following relationships (semantic and

conceptual ones) which can exist between the

concepts. They are described as follows.

- Synonymy relationship, exists between two

different concepts which express the same meaning.

The mutual implication, i.e. the fact that one of the

terms implies the other, and that the second implies

the first, can be seen like a proof of synonymy.

- Equivalence relationship, a concept can express

in a particular context the same meaning with

another concept.

- Identity relationship, exists between two

identical concepts having the same meaning.

- Homonymy relationship, the same concept can

have two different meanings.

- Antonymy relationship, between two concepts

indicates that the negation of the first implies the

assertion of the other; we can’t deny both at the

same time.

- is_a Relationship, it is a relationship of

classification between a general concept and a

specific one.

- Classification relationship, it is a particular case

of the is_a relationship. It can be expressed by the

kind_of relationship.

- Aggregation relationship represents the

set/elements relationship.

- Composition relationship: it is a strong

aggregation in which a concept can be composed of

one or several other concepts.

In the following section, we present an algorithm

of IS's ontologies building.

3 AN ALGORITHM FOR

BUILDING IS’S ONTOLOGIES

Our proposed algorithm is based on four steps

(Figure 1).

These steps are assisted by the designers

allowing the corpus modelling, the checking and the

correction of the obtained results.

3.1 Concepts and Their

Relationships Extraction

The determination of the concepts and the

relationships between them are based on several CR

presenting a given field (e-commerce for example).

The extraction of the concepts consists in the

extraction of the classes’ names, their properties

(attributes and operations) and their relationships.

We define a set of rules, presented at the

following section, allowing the comparison between

the concepts of the different CR.

3.2 Comparison Between the

Concepts of the Different CR

The comparison step is based on the concepts’

semantic. This semantic is expressed by each

concept’s attributes and operations. The following

algorithm determines the semantic relationships

between CR concepts; it is represented by the

following rules, where we consider C1 and C2 as

concepts. We define the following functions :

Name(C) : the concept’s name

Attribute(C) : the set of all C attributes

Operation(C) : the set of all C operations.

Rule 1 :

Identity (C1, C2) ⇔

Name(C1) = Name(C2) ^

Attribute(C1) = Attribute(C2) ^

Operation(C1) = Operation(C2).

Rule 2 :

Homonymy (C1, C2) ⇔

Name(C1) = Name(C2) ^

Attribute(C1) ≠ Attribute(C2) ^

Operation(C1) ≠ Operation(C2).

Rule 3 :

Synonymy (C1, C2) ⇔

Name(C1) ≠ Name(C2) ^

Attribute(C1) = Attribute(C2) ^

Operation(C1) = Operation(C2).

Rule 4 :

Kind_Of(C1, C2) ⇔

Name(C1) ≠ Name(C2) ^

Attribute(C1) χ Attribute(C2) ^

Operation(C1) χ Operation(C2).

Rule 5 :

Equivalence(C1, C2) ⇔

Figure 1: Building steps of IS ontology.

Addition new concepts

The CR source modelling (corpus)

Extraction of the concepts and the

relationships between their concepts

Comparison between the concepts (attributes

and operations)

Ontology

RC i

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468

Name(C1) ≠ Name(C2) ^

Operation(C1) χ Operation(C2).

Rule 6 :

Name(C1) ≠ Name(C2) ^

Attribute(C1) ≠ Attribute(C2) ^

Operation(C1) ≠ Operation(C2)⇔

ambiguity (C1, C2).

We can propose to the designer two

solutions:

1. Add the concept introduced like

a new entry to our ontology.

2. Choose a semantic relationship

in the proposing list of the

concepts composing our ontology.

Rule 7 :

Antonymy(C1, C2) ⇔

Name(C1) ≠ Name(C2) ^

Attribute(C1)= ! Attribute(C2) ^

Operation(C1) = ! Operation(C2).

All these rules are used to elaborate the comparison

algorithm.

Comparaison between two

concepts C1, C2

Comparaison between the attributs

and the operations of C1 and C2

Comparaison between the attributs

and the operations of C1 and C2

Identity (C1, C2)

Synonymy

(C1, C2)

KindOf (C1, C2)

Name (C1) = Name (C2)

YES NO

Attrib(C1) = Attrib(C2) and Operat(C1)=Operat(C2)

Attrib(C1) included in Attrib(C2) and Operat(C1) included in Operat(C2)

Antonom y(C1, C2)

designer

propositions

Homonymy

(C1,C2)

Attrib(C1)=NOT Attrib(C2) and Operat(C1)=NOT Operat(C2)

Equivalence

(C1, C2)

Operat(C1) included in Operat(C2)

Attrib(C1) = Attrib(C2) and Operat(C1)=C2Operat(C2)

Figure 2: Comparison between the concepts of the

different CR.

In the following section, we represent our

ontology with an ontology’s definition language

(ODL).

3.3 UMLOnto Ontology

Representation

The representation of our ontology requires the use

of a comprehensible and semi-formal language. For

that, we proposed a language UMLOnto (Mhiri

2005), based on UML. UMLOnto allows

representing the concepts as well as the relationships

between these concepts.

The diagram obtained is called concepts

diagram. This diagram can be represented by an

XML file by adding new tags supporting the

characteristics of ontologies. Indeed, XML is a

standard language supported by the major current

CASE especially using UML.

We used the tag <concept > representing a

concept which can be a class or an association. A

concept is characterized by its attributes and its

operations. The relationships between concepts can

be:

-conceptual relationship <association>,

<aggregation>, < composition >, < inheritance >,

- semantic relationship which can be synonymy,

homonymy, equivalence, kind_of, antonomy.

The name of this ontology is characterized by a

tag < name_Ontology >.

We also developed a framework to implement

our approach. It is based on ArgoUML

, which is an

open source software using UML (figure. 3).

Figure 3: Framework interface.

In the following section, we present the fourth

step allowing the update of our ontology.

3.4 Ontology Update

The update of our ontology consists in the iterative

using of the algorithm of comparison with the CR

provided by the designer with the previous version

of ontology.

http://argouml.tigris.org/

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469

The ontology update must preserve its structure.

One can have two types of updates:

- The corrective update,

- The evolutionary update.

This step is still in the experimentation in our

group.

4 CONCLUSION

In this paper, we presented an algorithm for building

IS' ontologies. It allows the extraction of the

concepts and the relationships between concepts of

different CR. Then, we used an algorithm of

comparison between the concepts based on semantic

relationships. This comparison makes it possible to

determine the relationships between the concepts of

the different CR. To represent our ontology, we

proposed UMLOnto a language which is

comprehensible by the designers. This ontology is

expressed with XML by adding new tags allowing

the exchange between the CR. Our ontology

building approach is progressive and iterative.

Like prospective for this work, we will formalize

the concepts diagram with language Z and to check

the coherence of our ontology. Then, we will define

functionalities of an ontology maintenance and will

integrate them in CASE.

REFERENCES

Gruber, T.R., 1993.Toward Principles for the Design of

Ontologies, Used for Knowledge Sharing. Stanford

Knowledge Systems Laboratory.

Leclère, L., Trichet, T., Frûst, F., 2002. Construction of an

ontology related to the projective geometry. RFIA 13th

congrès des Reconnaissance des Frames et

Intelligence Artificielle, France.

Mhiri

, M., Mtibaa, A., Gargouri, F., 2005. Towards an

approach for building information

systems’ontologies”. FOMI’2005,1st workshop

Formal Ontologies Meet Industry, Verona, Italy, June,

9-10.

Mhiri

, M., Mtibaa, A., Gargouri, F., 2005. UMLOnto:

Towards a language for the specification of

information systems’ontologies. SEKE’05, the

Seventeenth International Conference on Software

Engineering and Knowledge Engineering, Taiwan,

China, July, 14-16, p.743-746.

Ouksel, A., Sheth, A., 1999. Semantic Interoperablity in

Global Information Systems. ACM SIGMOD Record,

March, Vol 28, N°1, p. 5-12.

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