Perrine Pittet
email: perrine.pittet@u-bourgogne.fr

Christophe Cruz
email: christophe.cruz@u-bourgogne.fr

Christophe Nicolle Le2i
email: christophe.nicolle@u-bourgogne.fr

Towards Dynamic Ontology Integrating Tools of Evolution and Versionning in Ontology

Keywords: evolution, versioning, Versiongraph, ontology lifecycle, change operations, I

Since Gruber's definition, a lot of works focused on evolution or versioning issues. Not much attention has been paid to integrated solutions which resolve both these two purposes. In this paper we present a new semantic architecture that combines versioning tools with the evolution process. This architecture called VersionGraph is integrated in the source ontology since its creation in order to make it possible to evolve and to be versioned.

INTRODUCTION

Many works have been published about the definition of ontology to bridge the gap of semantic heterogeneity. Literature now generally agrees on the Gruber's terms to define an ontology: explicit specification of a shared conceptualization of a domain [START_REF] Gruber | A translation approach to portable ontologies-Knowledge Acquisition[END_REF]. The domain is the world that the ontology describes. It can be a general domain or a more specific one. This description uses a vocabulary of concepts which is understandable and agreed by people of the domain; here is the meaning of "shared conceptualization". The ontology can be implemented in several languages with a different level of formalization and expressivity, with no ambiguity that's why ontology is an "explicit specification". The development of ontology is becoming a common task and an inescapable supportfor information systems interoperability [START_REF] Moguillansky | &al -A Theoretical Model to Handle Ontology Debugging & Change Through Argumentation[END_REF]. This research domain is mature and the first feedbacks arise. New scientific deadlocks are identified concerning the lifecycle of ontology especially the evolution phase. Discussing about those issues leads us to first ask what part of the ontology definition is concerned by this lifecycle and where the evolution can be situed. Regarding to [START_REF] Klein | Ontology Versioning on the Semantic We[END_REF] Ontology lifecycle depends from changes occurring in the domain, conceptualization and/or specification of the ontology. Moreover, as depicted in Figure 1 (red dotted arrows), a change on one of this identified sources can impact a change in the other sources. Figure 1 shows the causes of changes related to the domain (a), the conceptualization (b) and the specification (c). We can notice that a change cause in (a) and (b) can have a change consequence in (b) and (c). Proposition a new classification of the identified changes in the state of art of [START_REF] Flouris | &al -Ontology Change: Classification & Survey -The Knowledge Engineering Review[END_REF] we have identified two types of change interaction:

Firstly, the domain can impact conceptualization. These changes are similar to changes in database schemas [START_REF] Ventrone | Semantic Heterogeneity as a Result of Domain Evaluation[END_REF]. For example new concepts/relationships must be considered or existing concepts/relationships must be improved or deleted. That's the role of Domain Evolution [START_REF] Klein | A Component-Based Framework for Ontology Evolution[END_REF] or Domain Fusion (Ontology Integration [START_REF] Calvanese | &al -A Framework for Ontology Integration -in Cruz, I[END_REF], Ontology Merging [START_REF] Pinto | &al -Some Issues on Ontology Integration -Proc. of the Workshop on Ontologies and Problem-Solving Methods[END_REF]) proposals. The domain can also affectspecification. For example a complete translation to a new specification corresponds to Ontology Translation_1 [START_REF] Avesani | A Large Scale Taxonomy Mapping Evaluations[END_REF] proposals.

Secondly, the conceptualization can impact the specification. For instance new models in the domain are introduced and require a change in the concept/relationships organization, formalization and expressivity. That's the purpose of Conceptualization Evolution [START_REF] Noy | Ontology Evolution: Not the Same as Schema Evolution -Stanford Medical Informatics[END_REF] and Conceptual Revision (Ontology Debugging [START_REF] Haase | An Analysis of Approaches to Resolving Inconsistencies in DL-based Ontologies[END_REF]) proposals. Nevertheless, we note that four types of change, used to resolve conceptual heterogeneity (conceptualization part), don't impact the ontology itself: Ontology Mapping [START_REF] Kalfoglou | Ontology Mapping: the State of the Art[END_REF], Ontology Matching [START_REF] Hu | Block Matching for Ontologies[END_REF], Ontology Articulation [START_REF] Hu | Block Matching for Ontologies[END_REF] and Ontology Morphism [START_REF] Flouris | Handling Ontology Change: Survey & Proposal for a Future Research Direction[END_REF]. These last ones add an external mapping to bridge the semantic gap. We argue that a change in the specification doesn't impact the conceptualization or the domain when the specification language is enough rich to express this change. It's the case of Description Logics languages [START_REF] Baader | &al -The Description Logic Handbook Theory, Implementation & Applications[END_REF] whichdisplay different levels of expressivity by holding different ontology constructors.So, we can choose one of them depending on the level of expressivity we need.

From this discussion, we deduce that the evolution phase concerns the domain and the conceptualization of the ontology. The Conceptualization Evolution is a direct consequence of the Domain Evolution. The new research area which aims at resolving the impact of change management on ontology is known as Ontology Dynamics [16] Ontology Dynamics deals with all issues concerning changes impacting the ontology (change of the domain, change in the conceptualization, or change in the specification), especially maintenance and evolution. The ontology development is a dynamic and incremental process starting with the creation of a brute ontology which has to be revised, refined and populated [START_REF] Djedidi | Change Management Patternsfor Ontology Evolution Process -Proc. of the IWOD at ISWC 2008[END_REF]. In the literature, a lot of papers have addressed the problem of managing the lifecycle of the existing ontology [START_REF] Ribeiro | Belief Contraction in Web-Ontology Languages[END_REF]. Most of them propose tools dealing with the different causes of change as depicted in figure 1. The major part put the emphasis on the evolutionissues [START_REF] Pan | A Stratification-based Approach for Inconsistency Handling in Description Logics[END_REF]. Some articles cope with versioning solutions to handle different versions of evolved ontologies [START_REF] Allocca | &al -Detecting Different Versions of Ontologies in Large Ontology Repositories[END_REF]. Nevertheless not much attention has been paid to the characterization of an ontology which integrates in its definition the mechanisms to evolve and being versioned. We can cite the proposition of [START_REF] Sassi | &al -From Temporal Databases to Ontology Versioning: An Approach for Ontology Evolution[END_REF]41], which approach is quite similar to ours but differs in its final solution.

This paper focuses on a generic architecture make it possible to combine the definition of ontology withevolution and versioning operators. This architecture can be used with any type of ontology based on description logics and especially OWL-DL formalism [21]. An implementation of this architecture is presented at the end of this paper. It is an extension of the Jena's library [22] by override ofthe existing ontology handling operators. This paper is articulated in three parts. The first part presents a background on ontology evolution and versioning. The second part describes the VersionGraph Architecture. The last part is an example of evolution versioning based on the Wine Ontology [START_REF]Wine Ontology[END_REF].

II. EVOLUTION AND VERSIONNING BACKGROUND

According to [START_REF] Hodgson | The Potential of Semantic Technologies for egovernment-presentation of eGov Open Source Conference[END_REF], ontology lifecycle is divided in seven steps: needs detection, conception, management and planning, evolution, diffusion, use, and evaluation.The needs detection phase starts witha detailed inventory of the domain and the various purposes.Like evolution phase, conception phase needs: knowledge acquisition, shared conceptualization building, formalization (Semantic Web formalisms[25]…) and integration of the existing resources (other ontology, applications…).The phase of management and planning underlines the importance of having a constant monitoring and a global policy to detect or initiate, prepare or evaluate the lifecycle iterations. This work intends to guarantee that an iteration of the lifecycle is activatedwhen an evolution is ready to be completed. The management step requires tools not only to prepare the ontology to adapt the domain changes but also to keep trace of the previous versions of the ontology. These goals can be reached with a versioning system [START_REF] Flouris | &al -Ontology Change: Classification & Survey -The Knowledge Engineering Review[END_REF].Diffusion phase deals with the deployment of the ontology. The use phase encloses all the activities related to the access of the ontology. Finally, the evaluation phase aims at evaluating the ontology state. Moreover, like the needs detection phase,it collects beforehand the knowledge of the domain and can also rely on previous studies or feedbacks. Except for the evolution and management phases, all the steps described can be considered as mature domains. Furthermore, this description of the lifecycle shows that evolution and managementremains the most complex phases. Evolution is the backbone of the lifecycle iterations. Therefore, the change management process is totally based on it. The rest of our state of art is articulated in three parts. According to the literature, we will first define the evolution role, operations and process. Then we'll have a look at the existing solutions for change representation and ontology versioning. We will see how to link the evolution process and a versioning system in order to integrate both of them in existing ontologies.

A. Ontology Evolution

As stated by [START_REF] Flouris | &al -Ontology Change: Classification & Survey -The Knowledge Engineering Review[END_REF], ontology evolution aims at responding to one or several changes in the domain or the conceptualization by applying them on the source ontology.This brief definition looks abstract and leads us to ask: what kind of changes does the evolution apply? How evolution applies them? What are the criteria to respect? How can we manage a goodevolution? Evolution changes are defined in the literature and especially in [START_REF] Avesani | A Large Scale Taxonomy Mapping Evaluations[END_REF] as a succession of simple or complex operations the user wants to apply on the intension (schema) or the extension(data) of the ontology.This evolution aims at adapting the ontology to the changed domain. Applying and propagating thechange are often manual tasks but can be done automatically by synchronization with the domain.According to [START_REF] Tovar | REACTIVE: A Rule-based Framework to Process Reactivity[END_REF] these tasks usually occur during the use phase of the ontology. Ontology Dynamics clearly define the evolution criteria. [START_REF] Atle Gulla | An Ontology Creation Methodology: A Phased Approach[END_REF] and [START_REF] Dividino | Controlled Ontology Evolution through Semiotic-based Ontology Evaluation[END_REF] qualify the maintenance of the ontology as the most important criterion.Evolution has to maintain whatever relies on the ontology.Maintaining the ontology consistent and pertinent, in a consensus is an inescapable issue of evolution [START_REF] Zablith | &al -Using Background Knowledge for Ontology Evolution[END_REF]. Applying changes on ontology can turn the conceptualization inconsistent and irrelevant. That's why an evolution should never be validated before the user has a preview of the impact of the changes on the ontology. This impact can only be estimated if the evolution operations are semantically clearly defined. In order to assure that this process is fully respected, some works propose an approach in six phases. 1. the change detection phase consists in detecting what changes occurred in the domain or in the point of view must be propagated to the conceptualization. Lots of papers in the Ontology Dynamics deal with this phase and propose methods and tools like integrated event handlers [START_REF] Tovar | REACTIVE: A Rule-based Framework to Process Reactivity[END_REF], ontology learning [START_REF] Novacek | &al -Semi-automatic Integration of Learned Ontologies into a Collaborative Framework[END_REF] etc… 2. the representation phase aims at representing the selected changes with ontological operations. [START_REF] Noy | Ontology Evolution: Not the Same as Schema Evolution -Stanford Medical Informatics[END_REF] classifies the evolution operations in two types: elementary (atomic) operations and composed (complex) operations.According to [START_REF] Noy | Ontology Evolution: Not the Same as Schema Evolution -Stanford Medical Informatics[END_REF], elementary operations are simple operations that modify only one entity like addition/suppression of classes/relations, of hierarchy, domain, range links, of class/relation properties like disjoint, transitivity, etc…whereas composed operations are a composition of several elementary operations.The choice of composed operations depends on the granularity of the evolution needs. Therefore, we aimat displaying our proposition to the major part of formal ontologies. So we need to integrate usual operations. Usual operations correspond to operations the ontology that developers are the most expected to use when creating and evolving an ontology. In addition to elementary operations, the literature gives some lists of usual operations (e.g. [START_REF] Stojanovic | &al -User-driven Ontology Evolution Management[END_REF][START_REF] Stuckenschmidt | Integrity and Change in Modular Ontologies[END_REF]). In complement, we have extracted other usual operations like "change the place of an entity"…from the application Protégé. Moreover we make a distinction between operations on the intension and operations on the extension. The cited works on change operations don't specify specific operations for the instances because they argue that an instance can become a class [START_REF] Noy | Ontology Evolution: Not the Same as Schema Evolution -Stanford Medical Informatics[END_REF]. However, we maintain that schema operations can't be confounded with instance operations. Actually, it is impossible to create an instance (instance operation)related to a class if this class is not created. Inversely a class can be created (schema operation) without instances. 3. the semantic phase prevents the user from inconsistency risks by determining the sense of the represented changes. For example, if composed operations have been selected, this phase will allow seeing their decomposition in elementary operations. [START_REF] Jaziri | A methodology for ontology evolution and versioning[END_REF]. the implementation of the changes alerts the user of the impact on data in terms of data gain or loss. [START_REF] Noy | Ontology Evolution: Not the Same as Schema Evolution -Stanford Medical Informatics[END_REF] gives these impacts from a list of 22 usual operations (the elementary ones and some composed). 5. the propagation phase aims at informing all the dependent parts of the ontology (other ontologies, application) of these changes. 6. Finally, comes the validation of the changes. In the following part we will see how our proposition can integrate these operations in the versioning system and follow these evolution phases.

B. Versioning

This section is articulated in three parts. First we define the role of versioning, bringing our new vision on this definition; Then we describe the versioning process of our versioning system based on the 6 phases of evolution process. A state of art on the existing solutions of change representations will help us to build the tools needed in this process. Finally we present our suggestion to permit the identification and the retrieval of a version of an ontology. [START_REF] Flouris | &al -Ontology Change: Classification & Survey -The Knowledge Engineering Review[END_REF] gives in 2007 a very strict definition of the role of versioning : give a transparent access to different existing versions of an ontologyby creating a versioning system.This system identifies the versions by their "Id" and delimits their mutual compatibility. In the past three years, Ontology Dynamics proposals extend its role: manage several chronologic and multitemporal versions [START_REF] Grandi | Multi-temporal RDF Ontology Versioning[END_REF], at local or web level [START_REF] Allocca | &al -Detecting Different Versions of Ontologies in Large Ontology Repositories[END_REF], when collected, distributed, accessed by search engines [START_REF] Allocca | &al -Detecting Different Versions of Ontologies in Large Ontology Repositories[END_REF]. All these definitions correspond to a retroactive versioning because versions of the ontology have to preexist. However in our objective, we want to integrate a versioning system since the creation of the first version of the ontology. Therefore, we need, as the ontology development, a dynamic and incremental process, which could take into account a new version at each evolution phase. That's why we propose to merge the evolution process (following the 6 phases) with the versioning one. First, the user chooses the list of operations to apply (cf. change detection phase). The versioning system formalizes them (cf. representation phase), turn them semantically understandable (cf. semantic phase), records and implements them (cf. implementation phase).Then after the propagation of the changes, (cf. propagation phase), the user validates them (cf. validation phase) and the versioning system applies them and generates the new version of the ontology corresponding to an evolution iteration. Finally the versioning system can give a transparent access to both of the versions with criteria defined by the user [START_REF] Stuckenschmidt | Integrity and Change in Modular Ontologies[END_REF]. It can delimit compatibility by retracing evolution operations [START_REF] Stojanovic | &al -User-driven Ontology Evolution Management[END_REF][START_REF] Stuckenschmidt | Integrity and Change in Modular Ontologies[END_REF]. To follow this process, we need to specify the tools displayed by our versioning system. According to [START_REF] Klein | Ontology Versioning on the Semantic Web[END_REF], a change specification should enclose an operational change specification (our list of operations), then the conceptual relationship between the first version and the new one (the selected operations on the selected entities).The first phase of the evolution process is then completed. The next step is to represent these changes.Several approaches are proposed in the literature to represent changes. Major part of them uses logs.Versioning logs [START_REF] Yildiz | Ontology Versioning and Evolution[END_REF] record the different versions of an ontology by representing each entity at a given time. For each class, relation and instance, a new instance of "EvolutionConcept" class is created. [START_REF] Klein | Ontology Versioning on the Semantic Web[END_REF] argues that metadata should be added to identify this change. In versioning logs, each instance is annotated with metadata (Id, cause, transaction time, state validated or not…).This solution is interesting if the versioning log can be integrated in the ontology. However for our purposesthere is no need to represent each entity if it's not modified by the evolution. Evolution logs [START_REF] Liang | Ontology Versioning and Evolution For Semantic Web-Based Applications[END_REF] don't save the versions but act like a change history. Not each entity but each substitution in the ontology is recorded in order to be reused when the user wants to access a version.Tracing the substitution rather corresponds to our objectives as a substitution contains the selected operations and the entities affected. In order to cope with our evolution process we propose to create a Version concept like in the versioning logs integrated in the ontology that will be created at each evolution iteration. This Version concept encloses: 1/the substitutions operated in the intension or 2/ those operated on the extension and3/ the metadata.Then, the implementation phase can be helped by introducing event detectors on data. In the application Jena supporting the ontology, the idea is to insert methods using "ActionListener" objects. The propagation phase can be performed by generating events activating the "ActionListener" objects. Finally, the validation is similar to the "Commit" operator of a DBMS, can be done by a simple click by the user. Our incremental versioning process following the 6 evolution phases constitutes the first part of our versioning system. The second part corresponds to the transparent access definition. The first issue is the identification of the versions. Most of the versioning systems use "Id" of theontologies to identify them [START_REF] Allocca | &al -Detecting Different Versions of Ontologies in Large Ontology Repositories[END_REF]. Though, it'snot enough to identify in which version a change on a certain entity occurred. As we have introduced metadata and the list of substitutions occurred when a Version is created, those data can serve as search criteria to identify and retrieve the right version. We have chosen to extend Jena operators (access on ontology etc…) in order to take into account the search criteria. This extension can be performed by an override of the access methods. For example, by adding metadata and operation attributes. This state of art permitted us to build the evolution and versioning process of our proposition. We also managed to design the versioning tools in order to represent changes and access the ontology.

III. VERSIONGRAPH ARCHITECTURE

In this section, we present the VersionGraph architecture which implements the choices of our state of art. First, we focuses on the operations corresponding to the evolution operations. Then we describe our versioning system. Finally, we give an example of evolution on the Wine ontology.

A. Evolution Operations

Contrarily to the [START_REF] Jaziri | A methodology for ontology evolution and versioning[END_REF] proposition, the schema and instance operations are differentiated respectively by

SchemaOperation and

InstanceOperation.

SchemaOperationtype operations correspond to the creation and deletion of classes (AddClass) and properties (AddProperty) but also to additions and deletions of restrictions on them. We distinguish restrictions on the classes and properties or properties of the data link hierarchy (HierarchyLink) such as class / subclass, property / sub-property. Also in the class restrictions, limitations like classes / properties such as the relationship between properties and classes (ClassPropertyLink, ClassDataPropertyLink), cardinality (ClassPropertyCardinality) are classified. Also in the restrictions we find domain and range restrictions of attributes (PropertyAttributeLink). Finally TypeProperty operations are used to define a specific constraint of a property (transitive, symmetric etc ...). InstanceOperationtype operations, correspond to operations of and deletion of individuals and statements about these individuals. We distinguish between the assertions relying individuals to the values (DataPropertyAssertion) and those specifying the types for these individuals (ObjectPropertyAssertion).

B. From evolution to versioning

From these evolution operations and the study of the different versioning solutions of our state of art, we derived a versioning system. At each evolution of the ontology, the system stores in the ontology, the changes impacted by the operations used and the context. This versioning system is an independent ontology which intends to be integrated into the existing ontology by a simple addition operation. Then, the user can start a first evolution of ontology in choosing whether to change the schema (intension) or data (extension) using the above operations. Each list of changes chosen by the user during the evolution is kept using a concept SchemaVersionGraph for SchemaOperation operations and

InstanceVersionGraphfor

InstanceOperationoperations on instances by specifying which elements of the ontology are concerned (concepts, relationships...). Contextual information can be added (as version, date, author, description...). These data are traced during the evolution using a concept of context VersionContext.

The set containing

SchemaVersionGraph or InstanceversionGraph and VersionContextis called VersionGraph. Figure 2 depicts an overview of the ontology schema. For more clarity, it only shows concepts and their relationships under 6 th hierarchical degrees. In a transparent way, each application of changes made by the user generates a new VersionGraph.TheVersionGraph definition in Protégé is presented in Figure 3.As depicted in this figure a VersionGraph contains a link with the previous version of the ontology (hasPreviousVersionGraph). It's actually a link to the core ontology (for the first VersionGraph) or to the previous VersionGraph.Because of its nature, our system of evolution and versioning can be integrated into applications using ontologies Jena. The access operations of the library Jena can be overridden by the criteria of change and context. Until now, proposals for versioning are often accompanied by a specific application that the user must install to access the version it wants if the use of URI is not enough (Evolva). However, many ontologies are accessed using a Java API Jena. Indeed, this library supports ontology-based formalisms like RDF, RDFS, OWL and the various DAML + OIL. Jena contains all the methods to access and edit ontologies. In addition, it also implements all the basic operations of evolution and the commonly used composed ones. Overridden access methods are able to take into account the criteria of versions thanks to new attributes. These criteria are integrated into the ontology itself as we saw in the previous paragraph.

C. The Wine Ontology Versionning

The Wine ontology is an ontology example in which international wines are described. For the first step, we import the VersionGraph ontology into the Wine ontology by an addition operation. Then the system creates the first version of the wine ontology with a first instance of VersionGraph. This Versiongraph only has a link with the source ontology. Then, we want to add an individual of Straw Wine type: "Vin Paillé de Corrèze". First, we need to validate the previous changes by a "Commit". Then changes in the schema are recorded and the new schema version is propagated to the ontology. A third VersionGraph is generated for the addition of the individual. This time it contains an InstanceVersionGraph.

Figure 1 .

 1 Figure 1. Causes of changes in the lifecycle of an ontology.Figure 3. VersionGraph definition in Protege.

Figure 3 .

 3 Figure 1. Causes of changes in the lifecycle of an ontology.Figure 3. VersionGraph definition in Protege.

Figure 2 .

 2 Figure 2. Overview of the VersionGraph Ontology

  Then we want to add the "StrawWine" wine which doesn't exists in the Wine ontology. Straw Wine's fruit is selected then dried in the sun so that the juice is very concentrated in flavor and sugar. So it is a dessert style wine sometimes heavy or balanced or straw gold color. It can be made from red grapes Cabernet Franc and Cabernet Sauvignon or Chardonnay white grapes and Sauvignon Blanc. To add this new concept and describe it, the system creates another VersionGraph. This new one islinked with the previous one.The system specifies a SchemaVersionGraph which contains the operations needed to describe and add the concept in the ontology.

		p:hasPreviousVersionGraph<vg:VersionGraph#V
	ersionGraph0>;
		p:hasDate "11/05/2010";
		p:hasAuthor "Perrine PITTET";
		p:hasSchemaVersionGraph
	<vg:SchemaVersionGraph#SchemaVersionGraph1>;
	# AssociatedSchemaVersionGraph1 description
	<vg:SchemaVersionGraph#SchemaVersionGraph1>
		p:hasAddClass <rdfs:class#StrawWine>;
		p:hasAddClassHierarchyLink
	<vg:ClassHierarchyLink#ClassHierarchyLink1>;
		p:hasAddClassDataPropertyLink
	<vg:ClassDataPropertyLink#ClassDataPropertyLink1>;
		p:hasAddClassDataPropertyCardinality
	<vg:ClassDataPropertyCardinality#ClassDataProperty
	Cardinality1>;
		p:hasAddClassDataPropertyCardinality
	<vg:ClassDataPropertyCardinality#ClassDataProperty
	Cardinality2>;
	# Description des SchemaOperation utilisées
	<vg:ClassHierarchyLink#ClassHierarchyLink1>
			<rdfs:class#StrawWine>;
		p:subClass <rdfs:subClassOf#DessertWine>;
	<vg:ClassDataPropertyLink#ClassDataPropertyLink1>
		p:class <rdfs:class#StrawWine>;
		p:dataProperty <owl:DataProperty#hasColor>;
		p:value <rdf:resource#Golden>;
	<vg:ClassDataPropertyCardinality#ClassDataProperty
	Cardinality1>
		p:class <rdfs:class#StrawWine>
		p:dataProperty <owl:DataProperty#hasBody>
		p:value	<rdf:resource#Full>	and
	<rdf:resource#Moderate>
	<vg:ClassDataPropertyCardinality#ClassDataProperty
	Cardinality2>
		p:class <rdfs:class#StrawWine>
		p:dataProperty
	<owl:DataProperty#madeFromGrape>
		p:value	(<rdf:resource#CabernetSauvignon>
	and	<rdf:resource#Carbernetfranc>)	or
	(<rdf:resource#Chardonnay>	and
	<rdf:resource#SauvignonBlanc>)
				<vg :VersionGraph#VersionGraph0>
				p:hasPreviousVersionGraph
				<http://www.w3.org/TR/owl-guide/wine.rdf>;
				# VersionGraph1 description
				<vg:VersionGraph#VersionGraph1>

  Ontology evolution and versioning are recent domains of search. Most of current ontology versioning approaches are not based on the evolution process. Rare are the solutions which integrate these mechanisms since the creation of the ontology. Our proposed architecture Versiongraph is a semantic solution towards the characterization of a dynamic ontology which reaches these objectives. Our ongoing research shows preliminary results on evolution of several ontologies like Wine, FOAF or Pizza. Our short coming plan is to enhance our evolution and versioning process on several projects applied to online press comments, tourism and town heritage ontologies.

		p:individual <rdf:resource#VinPaillé>
		<vg:AddMemberClass#AddMemberClass1>
		p:individual <rdf:resource#VinPaillé>
		p:class <rdfs:class#StrawWine>
		<vg:AddObjectPropertyAssertion#AddObjectPropertyAssertion1>
		p:individual <rdf:resource#VinPaillé>
		p:objectProperty <owl:ObjectProperty#locatedIn>
		p:value <rdf:resource#FrenchRegion>
		IV. CONCLUSION
	# VersionGraph2 description
	<vg:VersionGraph#VersionGraph2>
	p:hasPreviousVersionGraph
	<vg:VersionGraph#VersionGraph1>;
	p:hasDate	"12/05/2010";
	p:hasAuthor	"Perrine PITTET";
	p:hasInstanceVersionGraph
	<vg:InstanceVersionGraph#InstanceVersionGraph1>;
	#AssociatedInstanceVersionGraph1 description
	<vg:InstanceVersionGraph#InstanceVersionGraph1>
	p:hasAddIndividual <vg:AddIndividual#AddInd
	ividual1>
	p:hasAddMemberClass <vg:AddMemberClass#AddM
	emberClass1>
	p:hasAddObjectPropertyAssertion
	<vg:AddObjectPropertyAssertion#AddObjectPropertyAssertion1>
	# InstanceOperationdescription
	<vg:AddIndividual#AddIndividual1>


Romain Picot-Clémente

Christophe Cruz
email: christophe.cruz@u-bourgogne.fr

Christophe Nicolle

Contribution of Semantic Web on Customer Relationship Management in E-Tourism

Keywords: e-tourism, semantic web, adaptive hypermedia systems, profiling

This paper presents an ongoing research on the development of a customer relationship management in e-tourism. Based both on techniques of the Semantic Web and Adaptive Hypermedia Systems, this paper presents a solution for dynamic adaptation of the offer to customers according to semantic criteria characterizing the Internet. A case study about tourism in Côte d'Or department in Burgundy is presented.

Introduction

Since 1970, from reservation systems on computers to today's online reservations, innovations in technology have radically transformed the tourism processes and strategies [START_REF] Buhalis | Progress in tourism management: Twenty years on and 10 years after the internet: The state of eTourism research[END_REF]. During this period: 1/ the link between customer and supplier has been shortened, removing intermediaries, 2/ opportunities for direct access to the offers have multiplied and 3/ the low cost strategy seems to have become the only criterion of quality. However, these innovations have significantly reduced customer support in their choices and consultancy services and customization of the offer as they exist in travel agencies. Managing customer relationships is a strategic thinking based on a watchful and careful analysis of the client, establishing a relationship of trust, involvement of staff and partners, continuous monitoring for continuous innovation. A new technology is booming: profiling (or behavioral targeting). This new area of web marketing is to analyze the requests of users to better understand and target more efficiently expectations and demands. Through the research of a surfer you can not only know what type of service he likes, if he has children or even if it has responded positively to an electronic newsletter. The impact of these new fields of study likely to exceed the current investigations and to help to adapt efficiently demands of users in the area of tourism. Based both on techniques of the Semantic Web and Adaptive Hypermedia Systems, this paper presents a solution for dynamic adaptation of the offer according to semantic criteria characterizing the Internet. The Semantic Web is an evolving development of the W3 Consortium in which the meaning (semantics) of information and services on the web is defined1 .The Adaptive Hypermedia Systems have traditionally attempted to deliver dynamically adapted and personalised presentations to users through the sequencing of reconfigurable pieces of information [START_REF] Levacher | A Framework for Content Preparation to Support Open-Corpus Adaptive Hypermedia, International Workshop on Dynamic and Adaptive Hypertext: generic Frameworks, approaches and Techniques[END_REF]. This paper is articulated in three parts. The first part presents the context. The second part presents a quick state of art on Semantic Web and Adaptive Hypermedia Systems and the third part presents our proposal.

Tourism and technology, a case study.

Since 2006, the company "Côte d'Or Tourisme" is conducting research to develop a website to unite all tourism offers of the department of Côte d'Or. This department is located in the heart of Burgundy (Bourgogne). It has an area of 8 763.21 km2 with a population of about 500 000 inhabitants. The vineyards of Burgundy (Marsannay, Gevrey Chambertin, Chambolle Musigny, Aloxe Corton, Meursault ...) are located on this country area with the "Yonne" and "Saône et Loire". In 2008, the department of Côte d'Or received over 1.8 million tourists (data from hotels, campgrounds and bed & breakfasts in France). Figure 1 shows the impact of technical developments of the website on the Internet crowd. In 2007, the site is developed in HTML with some Java applets for animation. The tourism offer is not structured. Suppliers may not directly fill the website. In 2008, almost 4,000 suppliers are federated in a database. Filling processes are developed. However, the use of these new solutions shows a real digital divide between the suppliers. Oversight mechanisms to help providers have been established. However, site traffic has multiplied by 5. The Access to a complete set of up to date data seems to be a factor of attractiveness. In 2009, the company wants to offer web sites dedicated to specific audiences. This first step results from an analysis of profiling users. The website becomes a portal, providing access to a variety of websites: nearby tourism (http://www.tamtam21.com/), tourism dedicated to vineyards (http://www.divine-comedie.com/), nature tourism (http://www.bouger-nature-en-bourgogne.com/). Professionals also have their website dedicated to the filling process and support process to help them on technical aspects. The new features of Web 2.0 (Social Web) are available such as personalized access, blogs, forums, etc. The number of attendance on these websites has increased by about 15% during this last year. The addition of Social Web features to tourist offers shows real improvements for some Internet users (comments, reviews, tips ...). Currently, the company wants to develop a customer relationship management adapted for its specific customers. The company continues its research work in the field of Semantic Web and Adaptive Systems. The idea is to develop a site for dynamically profiles the user's behavior by providing him a combination of tourist offers according to his profile. For example, if the user wishes to find a restaurant that would welcome him with his children, the system will provide first restaurants with children's menus which are located near a public garden or park.

Background

The Adaptive Web Systems are commonly called Adaptive Hypermedia System (AHS). In the following section the main properties of an AHS are introduced as well as the semantic Web technologies.

Adaptive Hypermedia Systems

The research in adaptive hypermedia system has been very prolific these last 10 years. It results in new terms, models, methodology and systems which prove to be useful and powerful. Some systems have been developed these 10 last years, giving principally solutions for e-Learning which is considered as the first application domain, for example [START_REF] Brusilovsky | Web-based education for all: A tool for developing adaptive courseware[END_REF][START_REF] Cristea | LAOS: Layered WWW AHS authoring model and their corresponding algebraic operators[END_REF][START_REF] Henze | Adaptive hyperbooks: Adaptation for project-based learning resources[END_REF]. Moreover, attempts to define reference models have been made as shown in these projects [START_REF] Cristea | The three layers of adaptation granularity[END_REF][START_REF] Hendrix | Meta-levels of adaptation in education[END_REF][START_REF] Bra | An extensible data model for hyperdocuments[END_REF][START_REF] Bra | AHAM: A dexter-based reference model for adaptive hypermedia[END_REF]. Each system and model brings its own architecture and methods. Most of them are based on a set of several layers also called models which separate clearly the different tasks. It appears that all of them have at least in common a user model, a domain model and an adaptation model. The domain model contains the whole domain knowledge (information, documents, etc.), the user model contains his knowledge, his interests, etc., and the adaptation model manages the user model, matches the domain model with the user model, and provides adaptation. There are three main types of adaptation : Content adaptation: consists in hiding/showing or highlighting (or not) the information.

Navigation adaptation: consists in modifying the hypermedia structure by suggesting links or driving the user to a defined destination.

Presentation adaptation: consists in focusing (or not) on pieces of content or links, and in adapting to the hardware or page setting preferences.

The adaptation model is the central master piece of the Hypermedia Adaptive System because it uses the others models in order to process the adaptation. In addition, most of the model definitions are driven by the Oriented Object Model (OOM). This design model is well adapted to define the architecture and the interactions between the different system components. Nevertheless, the use of OOM for the definition of the user and domain models to define the knowledge of the domain is not adapted in our opinion. Actually, the knowledge management and ontology domain which has been enriched during thirty years of research in artificial intelligence and description logic is of value in AHS in order to model the knowledge. The semantic Web takes also advantages of this previous research in AI by defining languages and associated tools allowing the processing of knowledge as well as assertions on the knowledge.

The semantic Web

The semantic Web led to a standardization of languages like OWL for ontology creation. In the context of computer sciences, an ontology defines a set of representational primitives which allows the knowledge modeling of a specific domain. Ontologies are widely used to capture and organize knowledge on a particular domain. Ontologies formally define relations between terms. A typical ontology for the web has taxonomy and a set of inference rules. The taxonomy defines object classes (concepts) and relations between them, and inference rules provide efficient reasoning on the terms. Thus, associating concepts with documents, keywords, etc. it is possible to reason directly on them. Beyond the possibility to create complex models quite easily, their standardization allows their sharing between applications. However, the challenges in Ontology development and management are great. Ontologies providing the semantics for the Semantic Web have to be developed, managed, and endorsed by committed practice communities. Whatever is the domain of application (meteorology, bank transactions, biology, tourism, etc.), concept definition is required. In addition, the Semantic Web is not just for the Web. Actually, it represents a set of technologies that will work equally well on internal corporate intranets. The Semantic Web will resolve several key problems facing current information technology architectures as Web services [START_REF]The Semantic Web: A guide to the Future of XML, Web Services, and Knowledge Management[END_REF]. Moreover, it is not easy to precisely define the nature of Web 3.0 technologies, but the outline of emerging application has become clearer. The Web 3.0 applications are based on the Resource Description Framework (RDF) that provides a means to link data from multiple web sites or databases. With the SPARQL query language, a SQL-like standard for querying RDF data, applications can use native triple stores and extract RDF data from traditional databases. In RDF, if two data elements are recognized with the same URI, then they can be joined in a merged graph. RDF Schema (RDFS) and the Web Ontology Language (OWL) provide the ability to infer relationships between data in different applications or in different parts of the same application. These Semantic Web languages allow for the assertion of relationships between data elements. The term "linked data" is often used to describe the evolving RDF development space and "Semantic Web" is increasingly being used to describe coupling linked data with RDFS and OWL [START_REF] Hendler | [END_REF].

Semantic modeling of Tourism Offer

The semantic Web technologies can be easily integrated as a data format which allows to define linked data. Our system is based on the semantic Web technologies, and especially on domain ontologies and their inference rules. Thus, the domain model is defined as an ontology of domain where each concept represents an abstract or concrete domain entity (accommodation, hotel, camping, activity, etc.) and where relations between concepts are defined (hierarchical links, possession links, etc.). The user model is constituted of several domain dependant and independent parts. The first part is represented using an overlay model on the domain model; it is an overlay enabling the definition of user interests on the concepts of the domain. The second part is a set of <attribute-value> pairs which is a centric user definition and independent of the tourism domain (age, sex, etc.). The inference rules are defined into the adaptation model to reason over ontology models, the domain model and the overlay part of the user model. The rules are first order rules well known as Horn clauses and use the syntax SWRL, a semantic Web rule language that combines OWL and RuleML. First order logic is simple and powerful and gives solutions to complex problems. The adaptation is processed with the use of an inference engine allowing the assertion of content, presentation and navigation from the domain rules on the domain ontologies. From the realized inferences on the user and domain models, an adaptation model can provide different adaptations. Following the system creator's will, the adaptations are done differently using multiple data mining algorithms in the adaptation model. They consist in diverse algorithms like, clustering algorithms to create clusters of concepts or users, based on same features for example. Content and navigation adaptation are provided according to the results of these algorithms. For the presentation adaptation, only domain independent user features are used, it has no impact on the content (only on the appearance).

Ontology construction process

In order to create and develop our ontology, we processed in three steps. First, the database developed to store all the offers of the supplier was used as a starting point to create the ontology of domain for the domain model in the AHS. Second, the following step consists in enriching the ontology with axioms not present in the database but well known by the professional of the domain. Most of the time, it is materialized by semantic links between concepts which are most of the time lost by the relational model. In addition, the hierarchical structure is refined with the help of the professional in order to define a more appropriate hierarchy related to the links previously added. Finally some axioms that permit to define necessary conditions are added. For instance, the concept "gîte de france" is a restaurant and a hotel. In description logic, this rule is represented by: "GiteDeFrance Restaurant Hotel". Third, the last step consists in populating the ontology with instances from the database. In order to formalize the knowledge, we have distinguished three types of tables in the database: "Data" tables corresponding to touristic objects like hotels, camping, etc.

"Const" tables corresponding to constant tables like touristic labels, languages, etc.

"Relation" tables corresponding to association tables between "Data" and "Const" tables.

For instance, the table "rel_speak_res" models the languages spoken by the restaurants. It connects the touristic object hotel with the constant table Language, and each row defines a language spoken by a hotel. A table "Relation" could also connects two "Data" tables. Due to complete lack of space the complete script of the database is not presented. In order to facilitate the population process, views are created due to the complexity of the base and the useless tables and attributes. These views are listed in a centralizing table where their type is specified ("Data", "Const" or "Relation"). This is done to simplify the base and select useful data for the ontology construction. It is a manual process done with help of experts. All the view names are formatted to be explicit (for instance "res" becomes "restaurant"). A translator program has been developed to translate this set of views in ontology. Each "Data" and "Const" view becomes a concept into the ontology and each "Relation" view becomes relation between two concepts into the ontology. Then the program populates the ontology. This process consists in adding instances to concepts and relations to the ontology (for instance, adding the individual "Hotel Ibis" to the concept "Hotel"). This population is done by using the row of each view. Actually, we consider that a data indexation is done on the database by the ontology. The phases of translation and population are automatic phases.

Interaction between user and adaptation models

User features are captured in a user model. These features are either domain dependant or independent. As said before, independent elements are <attributes, value> pairs and the dependant part is an overlay on the domain model. The overlay consists in a weighting of domain concepts in order to model interests on them. The user model is initialized and kept up to date by the adaptation model following user interactions with the system. The proposed links are all (almost) relied to one or several concepts of the domain ontology. Basically, a click on a link generates a weight increasing of the associated concepts in the user model. Domain independent elements are principally collected by direct questions to the user (age, sex, job, etc.). The adaptation model comprises a set of rules about ontology concepts and relations. These rules are defined by domain experts. Following the user interests on concepts, rules are triggered. They can generate a user model update, which could also trigger other rules, etc. The adaptation model proposes information to the user from the domain model, corresponding to the user overlay model. It is a content adaptation if the content is directly shown to the user, or it is a navigation adaptation if links are proposed. Presentation adaptation is based principally on the non-overlay part of the user model. Indeed, the user context (hardware type, bandwidth, etc.), page setting preferences, etc. are in this part.

Conclusion

A lack of user adaptation and personalization has been shown in tourism oriented systems. To remedy this, we propose the construction of an adaptive hypermedia system based on user model, domain model, and adaptation model using semantic web tools. Indeed, ontology models are used to model complex and sharable knowledge structures and to realize rules based inference. Beyond the use of domain, user and adaptation models, the trend is to use additional models like presentation, goals, context or others models but no generic model for adaptive hypermedia system has already integrated them.

Fig 1 :

 1 Fig 1: Evolution of attendance / Technical trends.

http://en.wikipedia.org/wiki/Semantic_Web

Acknowledgements

This project is supported by the tourism agency "Côte d'Or Tourisme". Many thanks to J. P. Rebourgeon, P. Lambert, C. Legoux, P. Laforêt and all the team of "Côte d'Or Tourisme" for their help.


Romain Picot-Clemente

Christophe Cruz

Christophe Nicolle

TourismKM: a new Web Semantic based approach for E-Tourism

Keywords: Adaptive web system, adaptive hypermedia system, domain ontology, e-tourism

published or not. The documents may come   L'archive ouverte pluridisciplinaire

I. INTRODUCTION

For the past 40 years, technologies have revolutionized the exchanges between customers and suppliers pushing enterprises to continuously adapt their sales strategies. The tourism sector is one of the most striking examples of this trend. Since 1970, from reservation systems on computers to today"s online reservations, innovations in technology have radically transformed the tourism processes and strategies [START_REF] Buhalis | Progress in tourism management: Twenty years on and 10 years after the internet: The state of eTourism research[END_REF]:

With the development of Internet, the number of tourist offers seems to grow exponentially. The link between customer and supplier has been shortened, removing intermediaries. Customers have the opportunities to directly access to the offers. The low cost strategy seems to have become the only criterion of quality. However, these innovations have significantly reduced customer support in their choices and consultancy services and customization of the offer as they exist in travel agencies. Managing customer relationships is a strategic thinking based on a watchful and careful analysis of the client, establishing a relationship of trust, involvement of staff and partners, Manuscript received November 15, 2009. This work was supported by the company "Côte-d"Or Tourisme".

Romain Picot-Clémente is with the Laboratory LE2I (electronics, Image and computer Science) of Burgundy University and with the company "Côte-d"Or

Tourisme", 21000 Dijon, France (e-mail: recherche-tic@cotedor-tourisme.com).

Christophe Cruz is with the Laboratory LE2I (electronics, Image and computer Science) of Burgundy University, 21000 Dijon, France (e-mail: christophe.cruz@u-bourgogne.fr).

Pr. Christophe Nicolle is with the Laboratory LE2I (electronics, Image and computer Science) of Burgundy University, 21000 France (e-mail: cnicolle@u-bourgogne.fr). continuous monitoring for continuous innovation. A new technology is booming: profiling (or behavioral targeting). This new area of web marketing is to analyze the requests of users to better understand and target more efficiently expectations and demands. Through the research of a surfer you can not only know what type of service he likes, if he has children or even if he has responded positively to an electronic newsletter. The impact of these new fields of study is likely to exceed the current investigations and to help to adapt efficiently demands of users in the area of tourism. Based both on techniques of the Semantic Web and Adaptive Hypermedia Systems, this paper presents a solution for dynamic adaptation of the offer according to semantic criteria characterizing the Internet. The Semantic Web is an evolving development of the W3 Consortium in which the meaning (semantics) of information and services on the web is defined (http://en.wikipedia.org/wiki/Semantic_Web). The Adaptive Hypermedia Systems have traditionally attempted to deliver dynamically adapted and personalized presentations to users through the sequencing of reconfigurable pieces of information [START_REF] Levacher | A Framework for Content Preparation to Support Open-Corpus Adaptive Hypermedia[END_REF].

Actually, hypermedia applications (web sites, e-Learning platforms, etc.) are not, or very basically, user centered and adaptive. The lack of user centered adaptation generates a problem called "lost in hyperspace", the user loses himself navigating link to link in the system. Moreover, a phenomenon called "cognitive overload" could occur due to the huge amount of information proposed to the user. This project is inscribed in the context of the customer relationship management enhancement in e-tourism. It consists in the creation of an adaptive system which considers various features mainly deduced from the users" behavior in order to provide them with an efficient search engine.

This paper is made up of three parts. The first part presents the context. The second part presents a quick state of art on Semantic Web and Adaptive Hypermedia Systems and the third part presents our proposal.

II. TOURISM AND TECHNOLOGY, A CASE STUDY

The tourism in France has an important place, it is highly regarded. Indeed, it generates over 72 billion Euros of turnover, representing over 6% of the French GDP (against 3.5% for agriculture and agribusiness and 1% for automotive industry). There are about 200 000 tourist companies and nearly 2 million direct and indirect jobs generated by tourism.

Since 2006, the company "Côte-d"Or Tourisme", which aims to promote tourism, is conducting research to develop a website to unite all tourism offers of the department of Côte-d'Or. This department is located in France in the heart of TourismKM: a new Web Semantic based approach for E-Tourism . Fig. 1 shows the impact of technical developments of the website on the Internet crowd. In 2007, the site is developed in HTML with some Java applets for animation. The tourism offer is not structured. Suppliers may not directly fill the website. In 2008, almost 4,000 suppliers are federated in a database. Filling processes are developed. However, the use of these new solutions shows a real digital divide between the suppliers. Oversight mechanisms to help providers have been established. As a consequence, the site traffic has multiplied by 3. The access to a complete set of up to date data seems to be a factor of attractiveness.

In 2009, the company wants to offer web sites dedicated to specific audiences. This first step results from an analysis of profiling users. The website becomes a portal, providing access to a variety of websites: nearby tourism (http://www.tamtam21.com/), tourism dedicated to vineyards (http://www.divine-comedie.com/), nature tourism (http://www.bouger-nature-en-Bourgogne.com/). Professionals also have their website dedicated to the filling process and support process to help them on technical aspects.

The new features of Web 2.0 (Social Web) are available such as personalized access, blogs, forums, etc. The number of attendance on these websites has increased by about 15% during this last year. The addition of Social Web features to tourist offers shows real improvements for some Internet users (comments, reviews, tips ...). Currently, the company wants to develop a customer relationship management adapted for its specific customers. The company continues its research work in the field of Semantic Web and Adaptive Systems. The idea is to develop a site that dynamically profiles the user's behavior by providing him with a combination of tourist offers according to his profile. For example, if the user wishes to find a restaurant that would welcome him with his children, the system will provide first restaurants with children's menus which are located near a public garden or park.

III. BACKGROUND

The Adaptive Web Systems are commonly called Adaptive Hypermedia System (AHS). In the following section the main properties of an AHS are introduced as well as the semantic Web technologies.

A. Adaptive Hypermedia Systems

The research in adaptive hypermedia systems has been very prolific these last 10 years. It results in new terms, models, methodology and systems which prove to be useful and powerful. Some systems have been developed during these 10 last years, giving principally solutions for e-Learning which is considered as the first application domain, KBS Hyperbook [START_REF] Henze | Adaptive hyperbooks: Adaptation for project-based learning resources[END_REF], APeLS [START_REF] Conlan | Metadata driven approaches to facilitate adaptivity in personalized eLearning systems[END_REF] [5] ,Interbook [START_REF] Brusilovsky | Web-based education for all: A tool for developing adaptive courseware[END_REF], WINDS [START_REF] Sprecht | Adaptive learning environment for teaching and learning in WINDS[END_REF], MOT [START_REF] Cristea | The three layers of adaptation granularity[END_REF], RATH [START_REF] Hockemeyer | RATH-A relational adaptive tutoring hypertext WWW-Environment based on knowledge space theory[END_REF], etc. Moreover, attempts to define reference models have been made as shown in these projects, Tower [START_REF] De Bra | An extensible data model for hyperdocuments[END_REF], AHAM [START_REF] De Bra | AHAM: A dexter-based reference model for adaptive hypermedia[END_REF], Munich [START_REF] Koch | The Munich reference model for adaptive hypermedia applications[END_REF] and LOAS [START_REF] Cristea | LAOS: Layered WWW AHS authoring model and their corresponding algebraic operators[END_REF].

Each system and model brings its own architecture and methods. Most of them are based on a set of several layers also called models which separate clearly the different tasks. It appears that all of them have at least in common a user model, a domain model and an adaptation model. The domain model contains the whole domain knowledge (information, documents, etc.), the user model contains the user"s knowledge, his interests, etc., and the adaptation model manages the user model, matches the domain model with the user model, and provides adaptation (e.g. Fig. 3). There are three main types of adaptation, according to research:

• Content adaptation: consists in hiding/showing or highlighting (or not) the information.

• Navigation adaptation: consists in modifying the The adaptation model is the central master piece of the Hypermedia Adaptive System because it uses the others models in order to process the adaptation.

In addition, most of the model definitions are driven by the Oriented Object Model (OOM). This design model is well adapted to define the architecture and the interactions between the different system components. Nevertheless, the use of OOM for the definition of the user and domain models to define the knowledge of the domain is not adapted in our opinion. Actually, the knowledge management and ontology domain which has been enriched during thirty years of research in artificial intelligence and description logic [START_REF] Baader | The Description Logic Handbook, Theory, Implementation, and Applications[END_REF] is of value in AHS in order to model the knowledge. The semantic Web takes also advantages of this previous research in AI by defining languages and associated tools allowing the processing of knowledge as well as assertions on the knowledge.

B. The semantic Web

The semantic Web led to a standardization of languages like OWL for ontology creation. In the context of computer sciences, an ontology defines a set of representational primitives which allows the knowledge modeling of a specific domain. Ontologies are widely used to capture and organize knowledge on a particular domain. Ontologies formally define relations between terms. A typical ontology for the web has a taxonomy and a set of inference rules. The taxonomy defines object classes (concepts) and relations between them and inference rules provide efficient reasoning on the terms. Thus, associating concepts with documents, keywords, etc. it is possible to reason directly on them. Beyond the possibility to create complex models quite easily, their standardization allows their sharing between applications. However, the challenges in Ontology development and management are great. Ontologies providing the semantics for the Semantic Web have to be developed, managed, and endorsed by committed practice communities [START_REF] Shadbolt | The Semantic Web Revisited[END_REF]. Whatever is the domain of application (meteorology, bank transactions, biology, tourism, etc.), concept definition is required. In addition, the Semantic Web is not just for the Web. Actually, it represents a set of technologies that will work equally well on internal corporate intranets. The Semantic Web will resolve several key problems facing current information technology architectures as Web services [START_REF] Daconta | The Semantic Web: A guide to the Future of XML, Web Services, and Knowledge Management[END_REF]. Moreover, it is In RDF, if two data elements are recognized with the same URI, then they can be joined in a merged graph.

RDF Schema (RDFS) and the Web Ontology Language (OWL) provide the ability to infer relationships between data in different applications or in different parts of the same application. These Semantic Web languages allow for the assertion of relationships between data elements. The term "linked data" is often used to describe the evolving RDF development space and "Semantic Web" is increasingly being used to describe coupling linked data with RDFS and OWL [START_REF] Hendler | Web 3.0 Emerging[END_REF].

C. The contribution of the semantic Web

The semantic Web technologies can be easily integrated as a data format which allows to define linked data. Actually, our adaptive hypermedia system is based on the semantic Web technologies, and especially on domain ontologies and their inference rules.

Thus, the domain model is defined as an ontology of domain where each concept represents an abstract or concrete domain entity (accommodation, hotel, camping, activity, etc.) and where relations between concepts are defined (hierarchical links, possession links, etc.).

The user model is constituted of several domain dependant and independent parts. The first part is represented using an overlay model on the domain model; it is an overlay enabling the definition of user interests on the concepts of the domain. The second part is a set of <attribute-value> pairs which is a centric user definition and independent of the tourism domain (age, sex, etc.).

The inference rules are defined into the adaptation model to reason over ontology models, the domain model and the overlay part of the user model. The rules are first order rules well known as Horn clauses and use the syntax SWRL, a semantic Web rule language that combines OWL and RuleML. The first order logic is simple and powerful and gives solutions to complex problems. The adaptation is processed with the use of an inference engine allowing the assertion of content, presentation and navigation from the domain rules on the domain ontologies. From the realized inferences on the user and domain models, an adaptation model can provide different adaptations. Following the system creator"s will, the adaptations are done differently using multiple datamining algorithms in the adaptation model. They consist in diverse algorithms like clustering algorithms to create clusters of concepts or users, based on same features for example. Content and navigation adaptation are provided according to the results of these algorithms. For the presentation adaptation, only domain independent user features are used, it has no impact on the content (only on the appearance).

IV. APPLICATION

This section presents the developed application. It is based on an ontology, built from an Oracle view on a Tourism database. This section is articulated in three parts. The first part presents an overview of the existing use of the database and an overview of the adjustments made for adaptation. The second part depicts the construction of the ontology. The last part describes briefly the process of building the user model, done by the adaptation model.

A. Overview of the improvement

The system is developed by "Côte-d"Or Tourisme" which aims to promote the tourism in its region called "Côte-d"Or" in France. This company collects a great quantity of data using a website where tourism providers fill in their offers. These data are joined and centralized in an important database. This database is full of relevant information and its structure developed and updated many times is full of knowledge about the domain. Then, using this database, the offers are shown on the website in the same way for each customer (e.g. Fig. 5).

To provide personalized and adapted content, navigation and presentation to customers, processes are inserted between the Database and the Output. Tourism providers don"t change their filling process and data are yet stored in the database. Then, data from the database are used to create an ontology, in order to model domain knowledge. This creation process is explained in the next section, it uses views on the database

B. Ontology construction process

As explained in the previous section, the database is full of relevant information used to create the domain ontology. In addition, people of the company have also a good knowledge about the domain of tourism and the tourists. This section explains the ontology construction process.

In order to create and develop our ontology, we processed in three steps. First, the database was used as a starting point to create the ontology of domain for the domain model in the AHS. Second, the following step consists in enriching the ontology with axioms not present in the database but well known by the professional of the domain. Most of the time, it is materialized by semantic links between concepts which are most of the time lost by the relational model. In addition, the hierarchical structure is refined with the help of the professional in order to define a more appropriate hierarchy related to the links previously added. Finally some axioms that permit to define necessary conditions are added. For instance, the concept "gîte de France" is a restaurant and a hotel:

GiteDeFrance Restaurant Hotel Equation 1. Description logic definition of GiteDeFrance Thirdly, the last step consists in populating the ontology with instances from the database. In order to formalize the knowledge, we have distinguished three types of tables in the database:

• "Data" tables corresponding to touristic objects like hotels, camping, etc.

• "Const" tables corresponding to constant tables like touristic labels, languages, etc.

• "Relation" tables corresponding to association tables between "Data" and "Const" tables.

For instance, the table "rel_speak_res" models the languages spoken by the restaurants. It connects the touristic object hotel with the constant table Language, and each row defines a language spoken by a hotel. A table "Relation" could also connect two "Data" tables.

In order to facilitate the population process, views are created due to the complexity of the base and the useless tables and attributes. These views are listed in a centralizing table where their type is specified ("Data", "Const" or "Relation"). This is done to simplify the base and select useful data for the ontology construction. It is a manual process done with help from experts. All the view names are formatted to be explicit (for instance "res" becomes "restaurant").

A translator program has been developed to translate this set of views in ontology. Each "Data" and "Const" view becomes a concept in the ontology and each "Relation" view Then the program populates the ontology. This process consists in adding instances to concepts and relations to the ontology (for instance, adding the individual "Hotel Ibis" to the concept "Hotel"). This population is done by using the row of each view. Actually, we can consider that a data indexation is done on the database by the ontology. The phases of translation and population are automatic phases.

C. Interaction between user and adaptation models

User features are captured in a user model. These features are either domain dependent or independent. As said before, independent elements are <attributes, value> pairs and the dependent part is an overlay on the domain model. The overlay consists in a weighting of domain concepts in order to model interests on them (e.g. Fig. 7).

The user model is initialized and kept up to date by the adaptation model following user interactions with the system. The proposed links are all (almost) relied to one or several concepts of the domain ontology. Basically, a click on a link generates a weight increasing of the associated concepts in the user model. Domain independent elements are principally collected by direct questions to the user (age, sex, job, etc.).

The adaptation model comprises a set of rules about ontology concepts and relations. These rules are defined by domain experts. Following the user interests on concepts, rules are triggered. They can generate a user model update, which can also trigger other rules, etc. The adaptation model proposes information to the user from the domain model, corresponding to the user overlay model. It is a content adaptation if the content is directly shown to the user, or it is a navigation adaptation if links are proposed. Presentation adaptation is based principally on the non-overlay part of the user model. Indeed, the user context (hardware type, bandwidth, etc.), page setting preferences, etc. are in this part.

V. CONCLUSION

This paper presented a new Web Semantic based approach for E-Tourism. This paper has shown that technological developments in the field of tourism have transformed the relationship between customer and supplier. In addition, experiments conducted within the agency Côte-d"Or Tourism have shown that the association of suppliers in a database contributes to increase the attractiveness of a tourism website. Finally, a Web 3.0 solution has been proposed in this article. This solution allows the customer profiling and dynamically adapts responses according to the website profile. This targeting allows a better combination of tourist offers. This solution is based on a semantic modeling: data from the database and user profiles. An adaptive hypermedia system has been deployed to support this architecture. It is based on three models (user model, domain model, adaptation model) using semantic web tools. Indeed, ontology models are used to model complex and sharable knowledge structures, and to realize rules based inference.

Beyond the use of domain, user and adaptation models, the trend is to use additional models like presentation, goals, context or other models but no generic model for adaptive hypermedia systems has already integrated them. It will be the object of a future article.

Fig. 1 .

 1 Fig. 1. Evolution of attendance -Technical trends

Fig. 2

 2 depicts more precisely the attendance of the website, month by month, since 2007. The first evolution of the web site (federation of suppliers in the database) was deployed in December 2007. From that point, the attendance rate increases until October 2008. In December 2008, the curve decreases until March 2009. During this period, the agency Cote-d"Or Tourism begins uploading new web sites with

Fig. 2 .

 2 Fig. 2. Monthly evolution of attendance

Fig. 3 .

 3 Fig. 3. Overview of an adaptive hypermedia structure

Fig. 4 .

 4 Fig. 4. Web 3.0 applications are Web 2.0 applications based on linked data and web semantic technologies

Fig. 5 .

 5 Fig. 5. Current system work

Fig. 7 .

 7 Fig. 7. Overlay part of the user model

Romain Picot-Clémente, Christophe Cruz, Pr. Christophe Nicolle

ACKNOWLEDGMENT

Many thanks to Jean-Pierre Rebourgeon, Pascale Lambert, Christine Legoux, Pierre Laforêt, and all the team of "Côte-d"Or Tourisme" for their help.


Sylvie Chambon
email: chambon@ifsttar.fr

DETECTION OF POINTS OF INTEREST FOR GEODESIC CONTOURS: APPLICATION ON ROAD IMAGES FOR CRACK DETECTION

Keywords: Detection, segmentation, thin structures, road cracks, point of interest, geodesic contour, auto-correlation

A new algorithm of automatic extraction of thin structures in textured images is introduced, and, more specifically, is applied to detection of road cracks. The method is based on two steps: the first one consists in detecting points of interest inside the thin structure whereas the second step connects the points with a geodesic contour process. The main contribution of this work is the study of automatic detection of points of interest inside thin structures in a high-textured background. The results are compared with a Markovian segmentation.

INTRODUCTION

This work concerns the detection of road cracks. The threshold-based methods are simple but the results contain a lot of false detections [START_REF] Koutsopoulos | Primitive-based classification of pavement cracking images[END_REF].

The methods based on morphology allow to reduce false detections but strongly depend on the parameter settings [START_REF] Tanaka | A crack detection method in road surface images using morphology[END_REF]. The learningbased methods alleviate the problems of the two first categories [START_REF] Oliveira | Supervised strategies for cracks detection in images of road pavement flexible surfaces[END_REF] but they need a learning phase which is not well appropriate to the application. The filtering methods are the most recent ones (Oliveira and Correia, 2009). In [START_REF] Chambon | Road crack extraction with adapted filtering and markov model-based segmentation -introduction and validation[END_REF], we have introduced a segmentation based on a wavelet decomposition and a Markovian modelling. To have a dense detection with less false detections, we introduce a Geodesic Contour-based method with initial automatic detection of Points Of Interest (GCPOID) inside the thin structure. We present, first the data set, second, the method GCPOID, and, the results and conclusions.

DATA SET

Evaluation is done on 14 synthetic images and 42 real ones. The synthetic images were built using, for 8, a random dot texture and, for 6, real images without cracks (2 with a static camera and 4 with a camera embedded on a vehicle and controlled lights). The cracks were randomly added, with a random shape and gray-levels (Figure 1). For real images, estimate a reference segmentation is more complicated but the images are more realistic than the synthetic ones. 4 experts have manually segmented the images and the results are merged [START_REF] Chambon | Road crack extraction with adapted filtering and markov model-based segmentation -introduction and validation[END_REF]. On the 42 real images, 17 were acquired using a static camera and 25 using a dynamic system.

DESCRIPTION OF GCPOID

Existing methods have obtained quite good results, particularly, with multiscale analysis [START_REF] Chambon | Road crack extraction with adapted filtering and markov model-based segmentation -introduction and validation[END_REF]. However, the results still present a lot of false detections and incomplete detection of the cracks, see Detection of POIs for road cracks -We suppose that points inside the cracks are the POIs in the image because the crack is a significant element of the image, and, moreover, it is a high perturbation in the road texture. Some existing detectors are based on grey level, first derivatives of the image [START_REF] Harris | A combined corner and edge detector[END_REF] or second derivatives [START_REF] Lowe | Distinctive image features from scaleinvariant keypoints[END_REF]. In this work, the most adapted detectors seem to be: Harris operator and grey-level auto-correlation (noted AC) [START_REF] Moravec | Toward automatic visual obstacle avoidance[END_REF]. As an important point of this study, we detail and comment the auto-correlation detector whose principle is:

The grey level of a point of interest must be low correlated to the grey levels of its neighbors:

R(p i, j ) = fusion (k,l)∈V (i, j) M(f i j , f kl ), (1)
R depends on f i j and f kl , the two vectors that contain, respectively, the grey levels of the studied pixel p i, j , and a neighbor pixel p k,l and their respective neighborhoods, M corresponds to a similarity criterion (correlation measure), "fusion" is an operator to merge the correlation scores in the neighborhood V (i, j) of the studied pixel. In consequence, we have to determined: (V), the size of the neighborhood, V (i, j), (S), the correlation measure and the size of the correlation window (f i ) and (parameter F), the fusion operator.

Parameter V -For POIs located inside fine cracks on road images, it seems natural and appropriate to take a 3 × 3 square area centered on the studied pixel for the size of V (i, j). A different size can be used in an other kind of application.

Parameter S -

We have studied the influence of different correlation measures based on our work [START_REF] Chambon | Dense matching using correlation: new measures that are robust near occlusions[END_REF]. For reason of space, the experimentation is not described but it reveals the most appropriate measure, the Normalized Cross Correlation (NCC), a scalar product between the vectors f i j and f kl :

NCC(f i j , f kl ) = f i j • f kl f i j f kl .
(2)

The Zero mean NCC (ZNCC) has been used but this centered version of NCC does not afford discriminative values for R(p i, j ).

Parameter F -Three different kinds of fusion have been proposed:

(1) MAX -When the POIs correspond to the highest dissimilarity in the neighbors, equation (1) becomes:

R(p i, j ) = max (k,l)∈V (i, j) M(f i j , f kl ). (3)
(2) MAX-MIN -We suppose that a crack POI has the highest local dissimilarity but also the highest local contrast and equation ( 1) becomes:

R(p i, j ) = max (k,l)∈V (i, j) M(f i j , f kl ) -min (k ′ ,l ′ )∈V (i, j) M(f i j , f k ′ ,l ′ ). (4)
(3) ORIENTATION -We suppose that the contrast should be oriented and equation ( 4) becomes:

R(p i, j ) = argmax o ( max (k o ,l o ) M(f i, j , f k o ,l o ) -min (k o ,l o ) M(f i, j , f k o ,l o )), (5)
where the orientation o corresponds to one possible orientation over [0; π 4 ; π 2 ; 3π 4 ] and o = π + o. For each detector, the N p first responses (global selection) are chosen for extracting the N p selected POIs. The influence of N p is studied in § 4.

Geodesic contour propagation -

The goal is to find a curve that fits an object or a region by iteratively minimizing a cost or energy function, E, of the curve C , defined by [START_REF] Kass | Snakes : Active contour models[END_REF]:

E(C ) = L 0 w 1 C ′ (v) 2 + w 2 C ′′ (v) 2 + P (C (v) dv,
(6) where c(v) represents a curve drawn on a 2D image, [0, L] its domain of definition, and L the length the curve. The two first terms represent the internal forces (regularity of the curve) based on first and second derivatives with w 1 , w 2 the weights of each of these two parts. The third term is the external force, P . These choices can be made : (S 1 ) P based on grey levels: P (C (v)) = I(v);

(S 2 ) P based on the gradients:

P (C (v) = g( ∇I(v) )
where g is a decreasing function.

When the evolution curve is based on a planar curve evolution, this is a particular case, the geometric contours [START_REF] Delagnes | A markov random field for rectilinear structure extraction in pavement distress image analysis[END_REF] that are the most efficient in the presence of topology changes. To combine the advantages of both methods (active and geometric contours) geodesic contours [START_REF] Cohen | Global minimum for active contour models: A minimal path approach[END_REF] have been introduced. In fact, it is a particular case of active contours where equation ( 6) becomes:

E(C ) = ω C ′ (v) 2 + P (C (v) dv, (7)
In examining the shape of the element to detect, it seems very natural to use the geodesic contours algorithm and shape (S 1 ) has been chosen for P . Indeed, the energy function associated to the curve depends on the grey levels (for the external force) instead of the gradients (that are not significant in this kind of images that are highly textured). A Fast Marching algorithm is used for the optimisation. Moreover, we suppose that the cracks are not opened. To rely the N p POIs , denoted by p i , i ∈ {1; . . . ; N p }, we link the points two by two, by pairs of nearest points:

1. G ← {p i } 2. p ← argmin

EXPERIMENTAL RESULTS

Detection of Points of interest -

The results, cf. Figure 3, show that for each method, the best results are obtained when n = 10, and the larger the number of POIs, the larger the errors. The maximal percentage of detections is always found with a window of size 3 × 3. Moreover, auto-correlation always gives better results than Harris, and, the best operator fusion is the maximum. The orientation fusion leads to results similar to those of the maximum. For 38 images (over the 55 tested), we obtained from 80% to 100% of points that are well detected inside the cracks. There is no image (over the 55), with a 3 × 3 window size, where Harris is the best. Crack detection -These two methods are compared: the proposed method GCPOID and the previous method based on Markovian Modelling and denoted by MM [START_REF] Chambon | Road crack extraction with adapted filtering and markov model-based segmentation -introduction and validation[END_REF]. These criteria are used in order to compare the results: (1) the percentage of correct matches (TP), (2) the percentage of false negatives (FN) which corresponds to pixels that are not detected as cracks whereas they are inside a crack, (3) the similarity coefficient or Dice similarity, DICE, defined by: 2T P FN+T P+P , where P represents the number of pixels that have been detected as crack pixels. This criterion illustrates the proportion of correct detections compared to the whole detection and it is important for our application because we want to have an accurate and complete detection of the defaults. The higher the Dice (with maximum 1), the better the result.

the results of GCPOID, for all the images with a percentage of correct detections of POIs of 100% (27), there is very few false detections. Unfortunately, the detection of the crack is incomplete. For the images with more than 80% of correct detections inside the cracks, the results are also interesting, i.e better than our initial method MM, cf. figure 5.

In Figure 6, a global analysis over all the tested images is given. It highlights these two facts: (1) GCPOID outperforms MM for the percentage of correct matches (TP) ;

(2) On the contrary, MM obtains lower percentages of false negatives (FN) than with GCPOID.

This is why the mean Dice similarity is better with MM, 0.54, than with GCPOID, 0.46. However, the result with GCPOID is reliable: the mean of the correct detections reached 74.43% (MM obtained only 50.89%). In consequence, we can consider that we have reached our first goal: to obtain very few false negatives. The second aim (complete detection) can be obtained we try to propagate the results of the detection at the extremities of the crack detection, see image F382 in Figure 5.

Figure 6: Results obtained with GCPOID and MM -These results are represented by boxes with one box each criteria (TP, true positives, and FN, false negatives) and each method. The boxes have lines at the lower quartile, the minimum, the median, the maximum and the upper quartile. These results show that the median of the percentages of correct detections is highly improved with GCPOID, because it becomes 90.54% against 50.68% for MM whereas the percentage of false negatives for GCPOID is higher (the median is 55.63%) than for MM (the median is 15.68%).

CONCLUSIONS

We introduced a new method of road crack detection based on two steps: automatic detection of POIs followed by the estimation of geodesic contours. The study reveals the most appropriate detector of POIs (by introducing the principle and the pa-rameter settings): auto-correlation detector with normalised cross correlation and fusion with the maximum. Moreover, we demonstrate the interest of using geodesic contours for the final detection by comparing to a Markovian segmentation.

Our future work will focus on how adding more POIs (to have a complete/dense detection), with the combination of prefiltered images with autocorrelation detection, and how removing wrong POIs, by adding some constraints (based on the variation of grey levels in the neighborhood or on the shape of the curves determined by each pair of POIs). For the geodesic contours, we will propose a more realistic algorithm for connecting the POIs (possibility to connect one point with numerous others, to remove a point if the energy is low with all the paths passing through this point).

Figure 1 :

 1 Figure 1: Tested images -one synthetic image (a), one real image with a simulated default (b) and one real image (c).

Figure 2 .Figure 2 :

 22 Figure 2: Results obtained with Markovian Modelling (MM) (Chambon et al., 2010) (in red) -This method is based on a first step with adaptive filtering and a refinement step with a Markov model-based segmentation.

  estimate C between p and p 2 (d) p ← p 2

Figure 4 :

 4 Figure 4: Detection of POIs (in red) in road images -The left results are obtained with the detector of Harris whereas the right, with the auto-correlation (AC) operator. For AC, there is no errors, i.e. no POIs detected outside the cracks.

Figure 5 :

 5 Figure 5: Detection of road cracks with GCPOID -The POIs, obtained with AC (NCC), are linked in red with geodesic contours. Detection is incomplete but with very few false detections when there is no wrong POI.