Ontology Modification in a Multi-User Concept

Conflict Resolution

Fatma Chamekh, Guilaine Talens and Danielle Boulanger

Magellan - IAE- Jean Moulin University, 6 cours Albert Thomas – BP 8242, Lyon, France

Keywords: Ontology Modification, Multi-Agents System, Multi-User Context, Conflict Situation.

Abstract: The task of ontology evolution has been a topic of several research works leading to a number of tools

facilitating the process of ontology evolution. These tools often propose different approaches without taking

into account the multi-user context. It is either assumed that an ontology engineer changes the ontology, or

that experts modify the ontology in asynchronous way. In this paper, we propose an approach to update

ontologies for keeping knowledge up to date. This approach is based on agent’s paradigm. The relevance of

agents is that they can interact with each other to assist the expert to upgrade dynamically knowledge. In

multi-user context, experts are able to modify simultaneously the same ontological entity which can

generate conflict situations in the system. This one could generate ontology inconsistency. To control the

conflict situation each agent uses predefined rules.

1 INTRODUCTION

In the last decade, the information technology

explosion has led to changes in the organization and

management of companies. In order to improve their

reaction and adaptation period, companies have

interested in tools supporting capitalization and

management (Gandon, 2002). Knowledge

management systems have helped organizations to

capture, to capitalize and to share their knowledge.

They enrich their in-house knowledge by capturing

statements extracted from various sources of

information (documents, databases, websites...).

These statements could be transformed into ontology

compatible elements or axioms, to describe domains

in a formal way. However, the dynamic facet of

domain has brought about ontology evolution to

keep the knowledge up to date.

Handling the ontology evolution process in a

distributed, decentralized environment is time-

consuming and complex process. Therefore, we

propose a framework to modify the ontology from

document using the agent’s paradigm to reduce the

interventions of experts

Using multi-agent system (MAS) is motivated by

the following reasons: i) The MAS runs when a new

document is added, modified or deleted. Agents use

its reactive behaviours to begin this incremental

process. ii) To keep ontology consistency, agents use

its local knowledge. iii) Interactions between agents

support the ontology evolution process.

In a multi-user environment, experts can

simultaneously modify the ontology. Collaborators

change ontology in asynchronous mode, each one

modifies the same version without knowing the

modification made by the other one, which leads to

conflict situation. Conflict is natural phenomenon in

every field of human word, hence it express the

divergence of interests or needs. Because the solving

of ontology evolution’s problem is divided among a

number of agents, the conflicts between them

happens in the case of incompatible goals.

Therefore, it makes sense to examine the conflict

situation, with the aim to achieve the ontology

modification well.

In traditional way, collaborative conflicts are

usually handled by mechanisms of locking or

branching/merging provided by version management

system (eg, cvs, subversion, Git, etc) (Chen et al.,

2009). In this case, a conflict occurs when two

developers modify the same source file. However, in

the case of ontology evolution, users modify

concepts and relationships between them. So, the use

of the mechanisms listed is not possible.

In this paper, we try to answer the research

questions: How design a multi-agent system to

support ontology evolution in a multi-user context?

This research question breaks down into the

296

Chamekh F., Talens G. and Boulanger D..

Ontology Modiﬁcation in a Multi-User Concept - Conﬂict Resolution.

DOI: 10.5220/0005081602960303

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2014), pages 296-303

ISBN: 978-989-758-049-9

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

following sub-questions: (i) how to guide the expert

to evolve ontology by keeping its consistency? (ii)

How to define and manage the conflict situations?

The reminder of this paper is organized as

follows: the section 2 presents some existing

ontology evolution approaches. In the section 3 we

discuss a use case scenario behind this work. In

section 4 we present an overview of our framework.

In section 5 we show our methodology for keeping

consistency and resolving conflicts in the context of

ontology evolution process supported by multi-agent

system. Future works and conclusion are presented

in section 6.

2 RELATED WORK

According to (Stojanovic, 2004), ontology evolution

is defined as the “timely adaptation of ontology to

the arisen changes and the consistent propagation of

these changes to dependent artefacts”.

Many ontology evolution frameworks rely on

inconsistencies control lists that define the

consequences of each change. To resolve

inconsistencies of KAON (KArlsruhe ONtology)

ontology, (Klein, 2004) propose a set of

preconditions and post-conditions. Consistology

(Jasiri et al., 2010) is a tool where providers

ontology consistency using change kits (a set of

rules and suggestions) which control the

inconsistencies generated by each type of change.

Other approaches specify the consistency

between components of the semantic web. Among

others, (Luong et al., 2006) present CoSWEM

(Corporate Semantic Web Evolution Management)

as a system to manage effects of the ontological

changes to the semantic annotations. They define

rule-based approach for solving inconsistency, and

(Rogozan et al., 2005) propose an approach for

ontology evolution relatively to educational

semantic web. They implement the Semantic

Annotation Modifier system to keep consistency

between ontology and resources.

Other frameworks introduce the patterns for

maintaining overall consistency. Onto-Evoal

(Ontology Evolution-Evaluation) (Djedid et al.,

2010) present inconsistency resolution patterns.

EVOLVA (Zablith et al., 2014), is an ontology

management framework. It explores background

knowledge sources (Wikipedia, WordNet, online

ontologies….). The authors use a pattern-based

approach to verify the relevance of the change

against the base ontology.

Ontology evolution may consider also the multi-

user context. Experts could modify ontology

simultaneously. (Noy et al., 2006) propose two

plugins: the change management plugin and the

PROMPT plugin, integrated in PROTEGE to

support the ontology evolution in the collaborative

environment. The system use CHange and

Annotation Ontology (CHAO) to describe the

changes between versions. Each user annotates the

change made.

There are few approaches investigating the

problem of ontology evolution coupled with MAS.

DYNAMO is adaptive multi-agent system (AMAS)

for building and evolution of Terminological and

Ontological Resource (TOR) from texts (Sellami et

al., 2012). Each term and each concept try to find its

right place in the AMAS organization that is the

ontology. A set of behaviours for each type of agent

is defined. Local rules are adopted to detect non-

cooperative situation and actions to be taken to go

back in a cooperative state. The ontology engineer

uses inconsistence sheets which contain the

inconsistency code and the changed term, to reach

the modification. Another study of ontology

evolution and MAS is given in (Rahman et al,.

2012). The authors present MAEKM (Multi Agent

Enterprise Knowledge Management) based on

ontologies modelling functional domains and multi-

agent architecture performing the data retrieval and

managing the changes that may occur within the

data sources.

Our work presented in this paper can be

compared with some similar existing studies.

(Sellami et al., 2012) has presented DYNAMO as

MAS for building and evolution TOR from texts.

Nevertheless, this approach considers the agent as

term and the MAS as ontology and it not has been

mentioned the ontology evolution process. In

(Rahman et al., 2012), the MAS manage the

ontology consistency. However, they do not use

predefined rules to solve the consistency and the

collaborative context did not dealt with. Our

evolution management system designs the ontology

evolution process from documents based on agent’s

paradigm. Our work differs from the MAS system in

DYNAMO for assigning each step of the process as

agent’s role. Relying on rule-based approach, our

framework, encapsulate these rules as agent

knowledge base to reach with inconsistency.

Regarding to ontology evolution in a multi-user

context, .(Noy et al., 2006) has presented CHange

and Annotation Ontology. Despite that, this

approach only presented a way to annotate the

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297

change of each user but they do not specify

techniques to verify and solve it.

3 USE CASE SCENARIO

As an example to illustrate concepts discussed so

far, we propose an ontology describing aspects

related to a healthcare domain especially general

medicine, which acts as information based from

Ontologos corporate (www.ontologos-corp.com).

This information base contains medical reports and

ontology. In order to describe the general medicine

domain, the doctors define terms and relations.

The medical report is a document in which the

doctor registers a clinical case study. This one is

related to research work, for example the report

number83 explains the role of the obesity in growing

the asthma.

The first state of the system is to implement a

semantic research engine. Due to the increase of the

medical report, the existent ontology could be

changed. Further to this, the expert needs a system to

guide him to modify the ontology. Given this, the

system requires to capture these changes for it to

efficiently serve the expert and reduce

inconsistencies.

Suppose that users add a new document. The

system may analyse the expert’s request in order to

identify the change that could be done to the

ontology (add concept, add instance, add property).

Once we have the type of modification, the system

analyses the change by studying the effect on the

consistency of ontology and between ontologies and

documents. To identify the adequate operations

related to each type of change, it is necessary to

determine the type of change and inconsistencies. If

different possibilities exist, i.e., different additional

operations can be applied with different effects, the

users have to choose the appropriate additional

changes to implement. Various operations are

displayed to experts in order to assist them. Experts

validate the operation of change. The system checks

the consistency of the dependent artefacts

(application, document) after each change.

At the end, changes are merged from different

experts. The system checks the coherence of the

modifications by using a set of conflict resolution.

When the system reaches the consensus, the new

version of ontology (Vn+1) is created. The old

version is saved in the log of version and all changes

are backed up in the log of changes.

4 PLATFORM ARCHITECTURE

Our approach is based on the ontology evolution

process identified by (Stojanovic, 2004) and (Klein,

2004). This process is made up of four steps:

identification of changes, analysis of changes, and

propagation of changes and management of

versions. We assign to each agent a process step.

Agents interact to run different processing steps.

The architecture of the proposed framework is

shown in Figure 1. The system is composed of four

agents: the user agent, the ontology agent, the

consistency agent and the version agent, and three

components: The Learning module, the log of

changes and the log of versions (Chamekh et al.,

2013).

We focus our ontology model to the OWL DL

which is an axiom-oriented language. Classes and

properties have structural descriptions specified

through some defined constructors (Horrocks et al.,

2003). Satisfying ontology within an interpretation is

constraint by the satisfaction of all ontology axioms.

For convenience we will adhere to more compact,

traditional SHOIN(D) syntax. For the

correspondence between this notation and various

OWL DL syntaxes see (Horrocks et al., 2004).

Definition 1: an ontology O is a set of concepts,

properties and individuals.

O={C, P, I}

C= {c

,....c

n-1

}

P= {p

,....p

n-1

}

Example 1: As a running example, we will consider

an extract of general medicine domain, consisting of

the following axioms:

Non inflammatory pathology ⊆ Pathology,

inflammatory pathology ⊆ Pathology, (Non

inflammatory pathology and inflammatory

pathology are pathologies)

Infection inflammatory pathology ⊆ Non

inflammatory pathology

4.1 User Agent

It detects the change to be realized by analysing the

user request. In order to perform all tasks related to

the management expert request, we have provided

several behaviours: analyse the user request,

communicate with the other agents and user, trigger

learning module and verify the existence of

documents.

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Figure 1: Platform architecture.

When the user agent analyses the request, three

alternative operations exist : Add document, Delete

document, Modify document:

4.2 Learning Module

This module receives a document as input. Using

Text2Onto (Maedche, et al,2000) which is a

framework for data-driven change discovery by

incremental ontology learning. It uses natural

language processing and text mining techniques. The

output generated is XML/OWL file. This one

contains the extracted terms. These terms are

categorised as concepts, instances and taxonomic

relations.

As example of concept presentation:

<a:Concept rdf:ID="non infection inflamatory pathology_c">

<a:Ratingrdf:datatype="http://www.w3.org/2001/XMLSchema#d

ouble">0.0136986301369863</a:Rating>

<owlx:Labelrdf:datatype="http://www.w3.org/2001/XMLSchema

#string">night</owlx:Label> </a:Concept>

Example2. Let’s consider a change made by user 1

which adds a new medical report. The user agent

verifies if the document exists. If it is true, the user

agent informs the user that the document exists and

he must drop this operation. If it not, the user agent

triggers the learning module, and extracts:

The concept: Non infection inflammatory pathology.

4.3 Ontology Agent

It analyses a change. In order to perform all tasks

related to the management of the user request, we

provide this agent by various behaviours: detect the

ontology to modify, search similar entities and

define the changes to be proposed.

Definition2: An ontology change operation is a

set of change operation assigned to the ontological

entity OC ={oc

,oc

....oc

n-1,

When the user adds a document, the ontology agent

detects the ontologies to modify. The ontology is

founded by similarity measure between the name of

ontology and the topic of document. If ontology

exists the ontology agent seeks similarities between

extracted terms and the ontological entity.

Depending to the level of similarity, the ontology

agent proposes the changes based on its knowledge

base (Chamekh, et al, 2013).

Example 3. Let’s take again the change made by

user 1. The ontology agent1 detects the ontology to

modify and seeks similarities between extracted

terms and the ontological entities of the ontology O.

In this case, it uses its knowledge base and proposes

that the concept Non infection inflammatory

pathology can be a sub concept to the concept

inflammatory pathology.

The management of the ontology consistency

and the conflict resolution are ensured by the

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consistency agent and the version agent. A detailed

presentation is given in section5.

4.4 Consistency Agent

The identification of types of change to apply on the

ontology, expresses formally the needs of evolution

required by users. When they are applied, the

ontology changes from a current version to another

one. However, the application of a type of change

could generate inconsistencies on the new ontology

version. In our framework, we assign the task of

keeping consistency to the consistency agent.

Two types of OWL consistency are

distinguished: structural consistency and logical

consistency. Structural consistency refers to

syntactic conditions of OWL DL constructors and to

the constraints specified on its elementary axioms

and their combinations (Djedidi el al., 2010).

Logical consistency refers to the formal semantic of

the ontology and thus, to its satisfiability in the

meaning that ontology is semantically correct and

does not present any logical contradiction.

Definition 3: we define consistency Q as a set of

consistency condition: Q= {q

.....q

n-1,

}. An

Ontology O is consistent if it satisfies all the

consistency conditions Q(O).

Many reasoners are available to verify logical

inconsistency. However, some of them like Pellet

detects inconsistency but does not precise the axiom

that cause inconsistencies neither how to resolve the

detected inconsistencies.

In our work, we propose to detect and resolve

inconsistencies before they have occurred. So, in this

case the reasoner cannot be used.

To guarantee a logical and structural consistency,

we identify a set of rules. They are implemented as a

knowledge base of consistency agent (Chamekh, et

al, 2013).

Definition4: we define an additional change as a

set of operation that can keep the consistency of

ontology, AOC={ aoc

aoc

,.....aoc

The role of the consistency agent is keeping the

consistency of ontology during the process. Each

consistency agent is connected to another ontology

agent and another version agent in accordance with

the negotiation protocol. The negotiation allows the

consistency agent to perceive its environment. The

consistency agent has to achieve three objectives:

 To denote a type of change by

perceiving the message sent by the ontology agent.

 To study the effect of changes and

propose additional changes. The agent uses

consistency rules encapsulated as a knowledge base.

 To propose the additional changes to the

user by communication with the user agent.

The general behaviour of consistency agent is

presented through the algorithm1.

The consistency agent perceives the proposed

change and the ontological entity from the ontology

agent. This one checks the consistency of ontology:

if inconsistency exists, the consistency agent

proposes additional change (based on its knowledge

base) to the user, if he validates the consistency

agent launches the task of creation new version;

otherwise, the change is cancelled.

Algorithm 1: general behaviour of consistency agent.

Begin

Perceive ()

OC={ oc

,oc

};

O ontology

C= {c

,....c

n-1

}

Process verification of consistency ();

If (not Q(O) )

Process of denotation of consistency problem ();

If (aoc

€ AOC exist)

Process proposes additional change ();

Process to the user ();

If user validate

Process creation new version ();

Else

Undo change ();

End if

Else

Process to the user ();

End if

Stop agent ();

End if

End

Example 4: Let’s consider again a required

change presented up.The inconsistency agent1

studies the impact of this change (the concept Non

infection inflammatory pathology can be a sub

concept to inflammatory pathology) to the ontology

consistency by using the rules R1 and R2.

R1: if we add a sub-concept A to a non-leaf

concept B, we must compare the property of concept

A with the property of concept C (which is the sub-

concept of concept B). If there are common

properties between concept A and concept C, we

must verify R2, else (if not) we must verifyR3

R2: If there are common properties between the

concept A and the concept C

R3: If there are not common properties between

the concept C and the concept A

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4.5 Version Agent

In a multi-user context, users add simultaneously the

new document. Agents run the process of ontology

evolution until the creation of new version. There

are two types of conflict situations: The simple

conflict situation and the complex conflict situation.

Definition5: we define a conflict situation as a

set of simple and complex conflict.

C={c

simple

, c

complex

}

simple

={c

,......c

}

complex

={c

,.....c

}

The agent behaviour is illustrated by algorithm 2.

Firstly, the agent receives the change, the concerned

ontological entity and the version of ontology.

Secondly, it verifies the current version; it applies

the change and creates the new version, if it is the

same version. Else the version agent verifies in the

log of changes the modifications that bound to the

ontological entity. The version agent searches in the

local rules the concerning conflict situation. If it is a

simple conflict situation, it resolves the problem, and

creates the new version and if it is a complex

situation conflict, the version agent sends a message

to the inconsistency agent with a proposed change.

When the conflict situation doesn’t exist in the rules

base, the version agent requests the validation of the

user and updates its knowledge base.

The simple conflict situation that can be directly

solve by the version agent.

Simple conflict situation 1:

The user 1 links or adds the instance I to the concept

A in the ontology O version X and in the log of

changes the property Y of the concept A has been

renamed in the property Y’. The version agent adds

or links the instance I to the concept A and creates

the new version.

Simple Conflict situation 2:

The user 1 rename the property Y (property Y’) to

the concept A in the ontology O version X and in the

log of changes the concept A has been renamed to

the concept A’. The version agent renames the

property Y (property Y’) to the concept A’ and

creates the new version.

Simple Conflict situation 3:

The user 1 links or adds the instance I to the concept

A in the ontology O version X and in the log of

changes the concept A has been renamed to the

concept A’. The version agent adds or links the

instance I to the concept A’ and creates the new

version.

Simple Conflict situation 4:

The user 1 adds the property Y or renames the

property Y (property Y’) to the concept A in the

ontology O version X and the concept A has been

renamed to the concept A’. The version agent

renames the property Y (property Y’) or adds the

property Y to the concept A’ and creates the new

version.

Simple Conflict situation 5:

The user 1 renames the property Y (property Y’) to

the concept A in the ontology O version X and in the

log of changes the property Y of the concept A has

been renamed to the property Z. The version agent

renames the property Z in to the property Y’ and

creates the new version.

Simple Conflict situation 6:

The user 1 adds upper-concept/sub-concept B to

concept A in the ontology O version X and in the log

of changes the concept A has been renamed to the

concept A’. The version agent adds upper-

concept/sub-concept B to the concept A’ and creates

the new version.

Simple Conflict situation 7:

The user 1 adds a synonym concept B to concept A

in the ontology O version X and in the log of

changes the property Y of the concept A has been

renamed to the property Y’. The version agent adds

a synonym concept B to the concept A and creates

the new version.

The complex conflict situation cannot be resolved

by the version agent. It sends proposed changes to

the inconsistency agent. This one uses the

consistency rules to solve the problem.

Complex Conflict situation 1:

The user 1 links or adds the instance I to the concept

A in the ontology O version X and in the log of

changes new instances have been added to the

concept A. The version agent sends a change

<add/link the instance I to the concept A> to the

inconsistency agent.

Complex Conflict situation 2:

The user 1 links or adds the instance I to the concept

A in the ontology O version X and in the log of

changes the instance I has been linked to the concept

B. The version agent sends change <add/link the

instance I to the concept A> to the inconsistency

agent.

Complex Conflict situation 3:

The user 1 adds upper-concept/sub-concept B to

concept A in the ontology O version X and in the log

of changes the sub-concept C or upper-concept D

has been added to the concept A. The version agent

OntologyModificationinaMulti-UserConcept-ConflictResolution

301

sends a change <adds upper-concept/sub-concept B

to concept A > to the inconsistency agent.

Complex Conflict situation 4:

The user 1 adds upper-concept/sub-concept B to

concept A in the ontology O version X and in log of

changes the property Y has been added to the

concept A. The version agent sends a change<adds

upper-concept/sub-concept B to concept A >to the

inconsistency agent.

Complex Conflict situation 5:

The user 1 adds upper-concept/sub-concept B to

concept A in the ontology O version X and in the log

of changes, the property Y has been renamed to the

property Y’. The version agent sends a change<add

upper-concept/sub-concept B to concept A >to the

inconsistency agent.

Algorithm 2: general behaviour of version agent

Begin

Perceive ()

OC= { oc

,oc

};

Vn ontology version;

Process verification version ();

If (Vn=current version)

Process create new version ();

Else

Process check change ();

If (OC not exist in log of change)

Process create new version ();

Else

Process search conflict situation ();

If (C

simple

)

Process create new version ();

Else

Process sends change ();

End if

Stop change ();

End.

Example 5.To illustrate a possible instantiation

of this conflict situation and let’s consider again a

required change Ch1 made by user 1 defining non

infection inflammatory pathology as a sub concept

of inflammatory pathology. After verifying the

consistency of ontology behind the change made by

user 1, the consistency agent sends the change, the

corresponding entity and the version of ontology.

The version agent verifies if it is the current version.

The current version is not the same version sent by

the consistency agent. The version agent verifies in

the log of changes if the entity inflammatory

pathology has been changed. It detects that this

entity has been renamed by another user to

inflammatory pathologies. The version agent detects

a conflict situation. It found that it can use the

simple conflict situation 6. The version agent creates

the new version.

5 CONCLUSIONS

In this work, we treat ontology evolution from

concepts extracted from documents. The different

steps of evolution phases are decomposed inside

different agents. We try to deal with the problem of

conflict situations in a multi-user context. So the

modifications on concepts (and ontologies) may be

performed in a parallel way.

Some conflict situations can be directly solved

by agents. Some of them must be sent to the

concerned agent who is in charge of modifications.

The first perspective is to formalize the different

conflict rules. Secondly, to define the process of

decision of each agent and finally, to confirm our

assumptions by the implementation of the system.

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