A Cooperation System based on Ontologies

Mina Ziani, Danielle Boulanger and Guilaine Talens

University of Lyon – Jean Moulin, Modeme Team, 6 Cours Albert Thomas, Lyon, France

Keywords: Cooperation System, Ontology Alignment, Ontology Evolution, Ontology Design, Hybrid Ontology.

Abstract: We present in this paper a cooperation system based on ontologies. To integrate heterogeneous knowledge,

an hybrid ontology is designed. Besides, in order to reach cooperation between local ontologies, a

computer-aided system is offered to assist experts to build mappings between entities of different

ontologies. An approach is described to capture ontology evolution. We define a relevant proposal relating

to the geotechnical domain involving different businesses and willing to cooperate.

1 INTRODUCTION

Knowledge management is a great challenge for

industries. This involves the representation,

capitalization, sharing and evolution of knowledge.

The use of heterogeneous information sources

distributed across multiple organizations makes

these tasks more difficult.

Ontologies are a promised approach for

knowledge representation in a formal way. In

addition, they are used to ensure cooperation

between heterogeneous information sources.

Since they appeared at the beginning of the 90’s

in the community of Knowledge Engineering,

multiple definitions of ontologies are proposed. The

well-known is defined by (Grüber, 1993): “Ontology

is an explicit specification of a conceptualization”.

(St

üder et al., 1998) adds to this definition that

ontology captures consensual knowledge shared by

an experts’ group. In fact, ontology includes

concepts networks, relationships and axioms to

represent and organize knowledge.

Various approaches established interoperability

between knowledge contained in several information

sources (Wache et al., 2001). We denote the

approach with a single ontology, with multiple

ontologies or with an hybrid ontology. In a single

ontology approach, a global ontology is built to

represent a shared vocabulary between all users

derived from multiple information sources. In a

multiple ontology approach, each information source

is described in its own ontology. Moreover, in an

hybrid approach a shared vocabulary is designed to

allow cooperation between the ontologies. It can be

a terminological resource, a data warehouse or a

global ontology.

Different techniques based on ontologies are

used to yield cooperation between several

information sources and to allow semantic

interoperability. In particular, ontology merging is

the creation of a new ontology from several different

ones. The resulted ontology contains knowledge of

the initial ontologies. In the case of ontology

integration, data from the first ontology is included

in the second one. Ontology alignment is the process

of determining mappings or relationships between

entities of different ontologies.

In this paper, we propose a system based on

ontologies to resolve problems involved in

knowledge management and to allow cooperation

between knowledge bases. Firstly, some related

cooperation systems are highlighted. Then, the

architecture of the offered system and the required

hybrid ontology to represent domain knowledge are

described. Afterward, the developed computer-aided

system is proposed in order to yield cooperation in

the process of mappings creation. Following this, the

consequences of changes in ontology and some

solutions to manage knowledge evolution are

exhibited. We conclude with some perspectives.

2 RELATED WORKS

Many research projects have been proposed to

achieve cooperation. We distinguish the works on

database cooperation using ontologies and those on

knowledge engineering. Their aims are (i) to design

Ziani M., Boulanger D. and Talens G..

A Cooperation System based on Ontologies.

DOI: 10.5220/0003999600900097

In Proceedings of the 14th International Conference on Enterprise Information Systems (ICEIS-2012), pages 90-97

ISBN: 978-989-8565-11-2

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

ontologies for knowledge representation and (ii) to

allow cooperation between multiple ontologies. For

the database cooperation, there are two types of

systems: the first ones are automatic and the second

ones require the experts contribution to resolve

semantic conflicts between information sources.

OBSERVER (Ontology Based System Enhanced

with Relationships for Vocabulary hEterogeneity

Resolution) is a cooperation system for multiple

information sources described in multiple ontologies

(Mena et al.., 2000). Those are designed

independently of others to represent terms in a sub

domain capturing the information in a data

repository. OBSERVER allows to create manually

or semi automatically mappings between terms of

the distributed ontologies. These mappings consist

of synonym relations between terms in the different

ontologies and are stored in a component called IRM

(Interontology Relationship Management).

MOMIS (Mediator environment for Multiple

Information Source) is a semi-automatic system for

integrating structured and semi-structured data. It

provides an environment to manage the users’

requests (Beneventano et al., 2000). MOMIS is

based on a mediator/ wrapper architecture: The

wrapper translates each data source in a conceptual

schema while the mediator provides the user with a

Global Virtual View (GVV). It is described in an

ontology representing the global classes and

attributes, and the semantic relations between them.

Users can send request to the GVV which asks the

data sources.

OntoDawa (Ontology-based Data Warehouse) is an

automatic system for autonomous and evolutive data

sources (Nguyen Xuan et al., 2008). Each source

contains its own local ontology and the semantic

relations that articulate with the shared (global)

ontology. This one is manually built by the experts

while local ontologies are designed from the existing

concepts in the global ontology. Therefore, the data

integration is automatic thanks to the semantic

relations between local ontologies extracted from the

global ontology.

OWSCIS (Ontology and Web Services based

Cooperation of Information Sources) is a

cooperation system which uses two levels of

ontology: The information source level (local) and

the cooperation (global) level (Abrouk et al., 2008;

Poulain, 2010). Information sources are semantically

described using local ontologies, and a “reference

ontology” describes the semantics of the domain. A

semi-automatic method was developed to produce

mappings between two ontologies (local and

reference). In addition, a technique for cooperation

querying was implemented. It is based on

exploitation of the semantic contained in the

ontologies and uses the different mappings created.

OMSys (Ontology-based Mediation System) is an

automatic mediation system based on ontologies

(Maiz et al., 2010). Its aim is to represent and to

integrate heterogeneous data. Local ontologies

describe the structure and the semantics of data

sources. An ontology containing the global

vocabulary is designed by merging the local

ontologies. An algorithm based on techniques of

Agglomerative Hierarchical Clustering (AHC) and

the OWL inference mechanism is implemented. The

AHC techniques classify the entities in the different

ontologies in order to define the elements

representing the global ontology. Users send queries

which will be translated into the global language.

In the field of knowledge engineering, ontologies

are used to support knowledge management and

reasoning. In this context, several systems offer

management of ontologies and cooperation between

them by establishing semantic links between

concepts and relations of two ontologies.

OntoMas (Ontology Matching Assistant) is a

system designed to aid the alignment of

heterogeneous ontologies (Huza et al., 2007). The

project develops a knowledge base using the

MAGDA architecture (Generic Mapping Discovery

Architecture) which supports different alignment

techniques. MAGDA classifies the alignment

methods according to the used technique, the type of

the obtained result and the existence of an algorithm

to optimize the alignment. OntoMas provides

assistance to choose the most relevant alignment

technique in a given context.

TooCom (Tool to Operationalize an Ontology with

the Conceptual Graph Model) is a tool dedicated to

knowledge engineering (Fürst and Trichet, 2009). It

proposes an approach to operationalize ontologies

represented in OCGL (Conceptual Graph Ontology

Language) in order to reason about domain

ontologies and therefore to deduce semantic. It

considers heavyweight ontology containing axioms

to define the semantic of the domain. Thus, the

semantic links between conceptual primitives

(concepts, relations) are deduced from the axiomatic

level of ontologies, and confirmed by calculating

the “likelihood coefficient” of the alignments.

Three approaches are involved in these systems:

An approach with a single ontology is used in

MOMIS, an approach with multiple ontologies is

used in OBSERVER, OntoMas and TooCom , and

ACooperationSystembasedonOntologies

an approach with an hybrid ontology is used in

OntoDawa, OWSCIS and OMSys. They offer both a

significant autonomy to the local ontology and a

shared vocabulary. However, to represent the whole

domain semantic in a global ontology is difficult.

The ontology is manually designed by experts,

except in OMSys which proposes an automatic

approach to design it but do not manage its

evolution. We propose to develop a cooperation

system based on an hybrid ontology. But, instead of

describing the entire domain semantic at a global

level, we represent only common concepts,

properties and the relations which connect them.

Experts can cooperate through the global ontology

which offers a shared vocabulary and a set of

mappings between the local ontologies. The system

offers an assistance to guide experts to generate

mappings between ontologies entities by proposing

to compute similarity measures relatively to the

ontologies characteristics. Contrary to the existing

alignment systems (OntoMas, tooCom …), our

system stores all the calculated similarities in order

to reuse them. In addition, all the relations validated

by the experts are also stored to maintain the

consistency of the created mappings.

Among the presented projects, some study

ontology evolution. In OntoDawa, several versions

of the ontologies concepts can be stored and

manipulated. The “current version” of the ontology

represents the last version used for each concept.

Moreover, the different versions of concepts and

instances are stored in a database. In OWSICS, the

addition or the deletion of a local ontology implies

changes at the global ontology in order to recovers

all the sub-domain of the local ontologies. In

OMSys, only the data source evolution is captured

by the mediator.

(Stojanovic, 2004) defines a process in six steps

to manage the evolution. The first step identifies

necessary changes to the ontologies. The second step

identifies all the updates to the ontology that will be

required. In order to maintain the consistency of the

ontology, the third step provides all the derived

changes involved by the required change. The fourth

step ensures the consistency of the dependent objects

(ontologies, instances, applications). The fifth step

aims to inform the ontology users of the

consequences of the changes, to implement the

changes, and to store all the executed changes.

Finally, the sixth step allows the users to authorize

or refuse the changes with their effects.

(Djedidi and Aufaure, 2010) proposes a system

called Onto-Evoal (Ontology Evolution-Evaluation)

to manage ontology evolution and evaluation. The

system is based on Change Management Pattern

modeling. Based on these patterns and the semantic

relations between them, the system integrates an

automated process which manages change while

maintaining the consistency of the modified

ontology. In addition, OntoEvoal defines an

ontology quality model to evaluate the ontology.

This model is used to resolve inconsistencies by

assessing the impact of the proposed resolutions on

ontology quality. Thus, users can select the best

solution.

(Jaziri et al., 2010) proposed an anticipatory

approach and a tool called Consistology to manage

ontology evolution and versioning. A taxonomy of

types of changes includes all the changes which can

occur in the ontology. The consistency of the

ontology is anticipated by suggesting all the possible

resolutions and their effects on the ontology

according to a set of rules defined by the system.

Finally, the validation of changes implies the

creation of a new version of the ontology. Each

version of the ontology is stored in a log and has a

duration that ends at the application of a new

change.

KAON (Karlsruhe Ontology) is a framework

developed to manage ontologies (Stojanovic, 2004).

It contains some modules for the creation, storage

and application of ontologies. To manage the

ontology evolution, KAON provides a log

containing all the modifications that occur as well as

the concepts and properties concerned. A model of

changes describes some services that manage

ontology evolution. A log model stores the executed

changes, therefore allowing the possibility to go

back at a previous version.

These systems cannot handle the management of

hybrid ontology evolution. Our system has to

consider the impacts of changes to local ontologies

on the global ontology. A way of managing hybrid

ontology evolution is described later.

3 ARCHITECTURE OF THE

SYSTEM

To represent heterogeneous and distributed

knowledge, we design an hybrid ontology on two

levels: local and global. At a local level, business

ontologies are built. Each one describes a sub-

domain specificity respecting the point of view of an

experts’ group which practising a same business. At

a global level an ontology representing a shared

vocabulary allows to connect all the local

ICEIS2012-14thInternationalConferenceonEnterpriseInformationSystems

ontologies. In order to yield cooperation between

some business ontologies, we offer a computer-aided

system to guide the experts in the process of

mapping creation between concepts of local

ontologies (Ziani et al., 2011b). Then, we propose an

approach to support knowledge evolution.

We applied this work to the geotechnical domain

(contract CETU n° 2005_4.69011). Therefore, we

developed a knowledge capitalization system

allowing cooperation between experts. The system is

based on an hybrid ontology and manages its

evolution. The cooperation is ensured thanks to the

global ontology and a set of mappings. The

architecture of this system is showed in the figure 1.

Figure 1: Architecture of the cooperation system based on

ontologies.

The cooperation system contains:

 An interface of ontologies consultation: allows

the experts to see all the ontologies and mappings

existing between them.

 An interface of updating ontologies: to add/

modify/ rename or delete concepts/ properties or

instances of its own ontology.

 A computer-aided system: to help experts in the

process of mappings creation. It includes a

similarities module which implements several

similarity methods and measures.

 A mapping database: stores all the discovered

mappings.

 A similarity database: stores all the calculated

similarities.

 And a module for ontology evolution: includes

three modules. The first one implements a merging

to create or update a global ontology. The second

one supports the local ontologies update and the last

one manages the semantic relations update.

These elements, the locals and the global ontologies

interact with experts in order to create mappings

between concepts of two ontologies and to support

the evolution of the hybrid ontology.

4 HYBRID ONTOLOGY

REPRESENTATION

To represent knowledge from heterogeneous and

distributed knowledge bases, we designed an hybrid

ontology. It consists of local ontologies describing

concepts, properties and instances used by experts in

a given business and a global ontology containing

only concepts and properties shared by all. Each

local ontology is manually built by a group of

experts who share the same point of view, while a

global ontology is automatically designed by the

system. A merging algorithm was developed to

create it (Ziani, 2011a).

The figure 2 shows a part of a class diagram

which describes the hybrid ontology written in

OWL. The diagram represents some RDF resources

(Ressource Description Framework) identified by a

URI (Uniform Resource Identifier) and an object

‘synonym’ extracted from a database.

Figure 2: Class diagram describing a part of the hybrid

ontology.

The figure 2 describes a simplified UML

diagram:

 Global ontology: identified by a URI and

composed by a set of concepts and the conceptual

relations “is a” which connect the concepts.

ACooperationSystembasedonOntologies

Figure 3: Part of the hybrid ontology representing the geotechnic domain.

 Local ontology: identified by a URI and includes

concepts, conceptual relations, semantic relations

(“is equivalent”, “is disjoint”) and instances.

 Concept: identified by a URI and defined by a

syntagm. In addition, each concept may have a

description.

 Property: identified by a URI and described by a

syntagm.

 Synonym: each one has a unique identifier and

consists of a syntagm.

 Instances: identified by a URI and represents a

concept instance. Each concept has zero, one or

many instances. An instance concerns one or many

concepts.

Each concept contains either zero, or one or several

properties and instances. Each property and each

instance concern either one or several concepts.

Concepts or properties may have synonyms and each

synonym concerns 0..N concepts or properties.

The class diagram is simplified because we have

also the class of relation concept which allows to

represent richer semantic relations. An example of

an hybrid ontology that we developed in the

RAMCESH project is given in the figure 3.

5 ONTOLOGY ALIGNMENT

Our system allows the cooperation between experts

through the ontologies alignment. In particular, it

offers a guide to an expert in the process of the

creation of mappings between concepts of different

ontologies. This process is presented in the figure 4.

When an expert wants to create a mapping

between its ontology and another one, he sends a

request through the ‘search interface of mappings’.

ICEIS2012-14thInternationalConferenceonEnterpriseInformationSystems

This one interrogates the hybrid ontology to find the

names of the different local ontologies. Thus the

expert selects the name of the initial ontology and

those of the research ontology. The first one

corresponds to its business and the second one is the

ontology he wills to cooperate. The selected

ontologies and the global are imported. This later is

required to disambiguate the meaning of ontological

entities in different ontologies. All the concepts of

the initial ontology are proposed via the Interface

and the expert selects one of the returned concepts.

The objective is to discover the concepts of the

research ontology to align with the concept of the

initial ontology.

Once all the parameters (initial concept, initial

ontology and research ontology) are submitted via

the ‘search interface of mappings’, a request of

researching similarities is sent to the ‘assistance

module’. This later forwards the query to the

‘mappings database’ where are stored all the

similarities. If synonyms of the initial concept exist

in the ‘similarities database’, they are returned and

proposed to the expert.

Sequence diagram

Results

Resul ts

Com putati on

Computation

Update

Request for updating ontologies

Control

Store

Request for storing mappings

Store

Research

Request for storing the results

Validate mappings

Proposition of similarities

Resul ts

Similarities to calculate

Selection of methods

and measures

Proposition of methods

and measures

Research of methods

and measures

Proposition of similarities

Research of stored similarities

Research of similarities

List of concepts

Research

Research of the concepts in the initial ontology

Selection of the

initial concept

Global, initial and research ontologies

Cooperation query

Importation

Importation query

Selection of the

ontologies to align

List of ontologies

Research

Research of the ontologies names

Expert

Search interface of mappings Assistance module Similarities module Ontol ogy Similarities database Mappings database Concept

[If exists]

opt

[Expert research similarities]

loop

Results

Resul ts

Com putati on

Computation

Update

Request for updating ontologies

Control

Store

Request for storing mappings

Store

Research

Request for storing the results

Validate mappings

Proposition of similarities

Resul ts

Similarities to calculate

Selection of methods

and measures

Proposition of methods

and measures

Research of methods

and measures

Proposition of similarities

Research of stored similarities

Research of similarities

List of concepts

Research

Research of the concepts in the initial ontology

Selection of the

initial concept

Global, initial and research ontologies

Cooperation query

Importation

Importation query

Selection of the

ontologies to align

List of ontologies

Research

Research of the ontologies names

Figure 4: Sequence diagram representing the alignment process.

ACooperationSystembasedonOntologies

While the expert researches similarities, the

‘assistance module’ proposes to compute similarities

measures using the ‘similarities module’ where all

the methods and measures of similarities are

implemented. The scheduling of these proposed

measures depends on the previous returned results

and the characteristics of the ontologies to align

(ontology granularity, number of concept properties

and instances).

Each found similarity is stored in the ‘similarities

database’ and proposed to the experts for validation

from the ‘search interface of mappings’. Then, the

expert can validate the found similarities. The

semantic links proposed and the expert names are

stored in the ‘mappings database’. Finally, a control

in the ‘mappings database’ allows to verify the

consistency of the generated mappings and therefore

requests to update the ontologies (creation,

modification, deletion).

Through this process, the system allows to store

all the calculated similarities in order to reuse them

and the generated mappings to verify their

consistencies. This interactive system aids to select

the best relevant similarity measures and is based on

a computer-aided algorithm to research similar

concepts (Ziani et al., 2011b).

6 HYBRID ONTOLOGY

EVOLUTION

The evolution of the knowledge domain and the use

of the ontologies for different tasks induce changes

in the local ontologies describing the businesses of

the domain. These evolutions induce changes in the

global ontology (updated concepts and the properties

after modifications) and in the mappings created

during the operations of alignment.

At each change, the system has to send

notifications to inform the experts about the

consequences of an asked change. Before taking in

account, the expert has to validate the modifications

involved. Furthermore, the system has to preserve all

the versions of the ontology evolution.

6.1 Management of Ontology Evolution

There are three types of changes: Elementary,

composed or complex. The elementary change

modifies only one entity of the ontology (add,

modify or delete). The composed change creates

modifications in the neighborhood of the ontology,

and the complex changes involving elementary and

composed changes (Stojanovic, 2004).

The elementary changes are directly performed

by the experts. Composed and complex changes

require verification into the locals and global

ontology. They mainly concern, the addition or

deletion of business ontology and the addition of

concepts sharing by all the experts. The solutions

proposed for the verification of the consistency of a

global ontology are:

– For an Addition of Business Ontology. The

addition of a new ontology involves its integration in

the global ontology according to the approach of

designing an hybrid ontology (Ziani et al., 2011a). It

consists to verify if all the concepts and properties of

the global ontology exists in the added ontology. In

the contrary, the concept or property is deleted in the

global ontology.

– For a Deletion of Business Ontology / Addition

of the not Leaf Concepts. The deletion of a

business ontology and the addition of not leaf

concepts into a business ontology can involving the

modification of the global ontology if there are new

concepts common to all the business ontologies.

These concepts must be integrated into the global

ontology and the relations which will connect them

to the other concepts in the global ontology are

deduced from the links which connect this one to the

other concepts in the "target" ontology (the business

ontology which was identified during the creation of

the global ontology). The conceptual graph obtained

is verified with an algorithm which allows to delete

the conceptual relations providing cycle into the

global ontology. This relation is only stored in the

local ontologies (Ziani et al., 2011a).

6.2 Management of Mapping Evolution

The system automatically updates the mappings

validated by the experts.

When an expert suggests to add a semantic link

between two concepts, the relation and the expert

name are stored in the mapping database. Then, the

system verifies if another relation between the

concerned concepts exists in the database. If there is

no relation, the system generates a mapping between

these concepts. On the contrary, if there are one or

several semantic relations between these concepts,

the consistence of the ontology is verified and the

existing mapping can be deleted or modified by the

adding of the new. There are two possibilities: Either

the same alignment exists, in this case there is no

modification to be brought to the ontologies, or there

is a contradictory alignment: In this case, we cannot

create this latter. The alignment previously created is

deleted. It can be recreated only by a third expert

ICEIS2012-14thInternationalConferenceonEnterpriseInformationSystems

who confirms one of the existing solutions or by an

expert who modifies the alignment which he has

previously proposed in the mapping database.

The expert can modify a relation which he has

created in the similarity database. This modification

can involve the updating or the deletion of the

generated mappings.

The expert can also delete a relation which he

has created in the similarity database. As previously,

this operation can modify or delete the generated

mapping between the concerned concepts.

7 CONCLUSIONS

The cooperation system we developed, allows to

represent heterogeneous and distributed knowledge

through an hybrid ontology, and to reach

cooperation between experts with different points of

view. In addition, it manages the hybrid ontology

evolution. This system is generic and can be applied

to all the domains with several identified sub-

domains. In particular, we applied this work to the

geotechnical domain.

Currently, the system of the geotechnical

knowledge management is partially implemented

(the implemented part concerns the hybrid ontology

design and the ontology alignment).

Therefore, our future work is to enable the

system to automatically support the hybrid ontology

evolution and to manage the different versions of the

hybrid ontology. Another perspective of this work is

to estimate all the mappings stored in the similarity

database in order to deduce other semantic relations.

Finally, it would be interesting to study the

scalability of the hybrid ontology and the alignments

between concepts.

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