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This article was downloaded by: [Stony Brook University] On: 25 October 2014, At: 18:24 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Applied Artificial Intelligence: An International Journal Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/uaai20 COMPETENCE ONTOLOGY FOR DOMAIN KNOWLEDGE DISSEMINATION AND RETRIEVAL Bernard Lefebvre a , Gilles Gauthier a , Serge Tadié a , Tran Huu Duc a & Hicham Achaba a a Laboratoire GDAC, Département d'informatique , Université du Québec à Montréal , Montréal, Québec Published online: 23 Feb 2007. To cite this article: Bernard Lefebvre , Gilles Gauthier , Serge Tadié , Tran Huu Duc & Hicham Achaba (2005) COMPETENCE ONTOLOGY FOR DOMAIN KNOWLEDGE DISSEMINATION AND RETRIEVAL, Applied Artificial Intelligence: An International Journal, 19:9-10, 845-859, DOI: 10.1080/08839510500234222 To link to this article: http://dx.doi.org/10.1080/08839510500234222 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms- and-conditions

COMPETENCE ONTOLOGY FOR DOMAIN KNOWLEDGE DISSEMINATION AND RETRIEVAL

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This article was downloaded by: [Stony Brook University]On: 25 October 2014, At: 18:24Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Applied Artificial Intelligence: AnInternational JournalPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/uaai20

COMPETENCE ONTOLOGY FOR DOMAINKNOWLEDGE DISSEMINATION ANDRETRIEVALBernard Lefebvre a , Gilles Gauthier a , Serge Tadié a , Tran Huu Duca & Hicham Achaba aa Laboratoire GDAC, Département d'informatique , Université duQuébec à Montréal , Montréal, QuébecPublished online: 23 Feb 2007.

To cite this article: Bernard Lefebvre , Gilles Gauthier , Serge Tadié , Tran Huu Duc & Hicham Achaba(2005) COMPETENCE ONTOLOGY FOR DOMAIN KNOWLEDGE DISSEMINATION AND RETRIEVAL, AppliedArtificial Intelligence: An International Journal, 19:9-10, 845-859, DOI: 10.1080/08839510500234222

To link to this article: http://dx.doi.org/10.1080/08839510500234222

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

COMPETENCE ONTOLOGY FOR DOMAIN KNOWLEDGEDISSEMINATION AND RETRIEVAL

Bernard Lefebvre, Gilles Gauthier, Serge Tadie, Tran Huu Duc,and Hicham Achaba & Laboratoire GDAC, Departement d’informatique,Universite du Quebec a Montreal, Montreal, Quebec

& In this paper, we present a competence ontology for domain knowledge dissemination andretrieval services, which has been used in the MDKT project (Management and Disseminationof Knowledge in Telecommunication). The main objective of this project is to set up a computerizedknowledge management system related to a specific domain in order to develop the human resourcesexpertise for the needs of the enterprise. In the case of this project, the knowledge is about wirelessnetworking and is expressed in digital documents. Among all the ontologies that implement theknowledge needed by the system, the competence ontology plays a key role. The competence ontologydefines at a meta-level the concept of competence and its relationships with other concepts such asdocument or user. Its instantiation is used to characterize a user model and a document model.This knowledge organization makes it possible to infer which document, or more generally whichdomain knowledge information, is suitable for a given person or to whom specific domain knowl-edge information should be disseminated.

One of the aspects of the Grid is its ability to make it possible to share ser-vices on the Internet through software agents. These services have to bespecified in order to be used by clients of various kinds. This specificationis formally given in a standardized language called WSDL (Web ServicesDescription Language). The published formal description can be used bya service requester to identify a suitable service provider. It makes it alsopossible for both partners to exchange messages allowing the requested ser-vice to be achieved by the provider according to the needs of the requester.

This work is being carried out under the MDKT project (Management and Dissemination ofKnowledge in Telecommunication) by researchers from UQAM (University of Quebec at Montreal)and University of Montreal including Bernard Lefebvre, Jean-Guy Meunier, Gilles Gauthier, Omar Cher-kaoui, Olivier Gerbe, and some M.Sc. and Ph.D. students.

We would like to thank LUB (Laboratoire Universitaire de Bell) and NSERC (Natural Sciences andEngineering Research Council of Canada) for their financial support.

Address correspondence to Bernard Lefebvre, Laboratoire GDAC, Departement d’informatique,Universite du Quebec a Montreal, Case Postale 8888, Succursale Centre-Ville, Montreal, Quebec H3C3P8, Canada. E-mail: [email protected]

Applied Artificial Intelligence, 19:845–859Copyright # 2005 Taylor & Francis Inc.ISSN: 0883-9514 print/1087-6545 onlineDOI: 10.1080/08839510500234222

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The knowledge of the environment in which a service is going to be ren-dered is also a feature that can help to configure the service itself in orderto suit the particularities of a requester. In order to be usable and sharable,the knowledge related to the context of use also has to be modeled by ameta level, which can be implemented by the means of formal and com-mon ontologies. This need for sharable semantic knowledge in the contextof Grid services is analogous to the one that exists for the Web in order tofocus the research of documentary information. It is then appropriate toconsider and apply for the Grid the tools and languages that have beendeveloped for the Semantic Web.

The object of this paper is to present such semantically dependant ser-vices in the case of a system whose main task is the development of humanresources expertises. More precisely, this system integrates documentarydissemination and retrieval services and is part of the MDKT project (Man-agement and Dissemination of Knowledge in Telecommunication).

The following sections present some methodological aspects that arerelated to the ongoing standardization for a formalism and a language toexpress domain knowledge in the context of the Semantic Web. The archi-tecture of the system built for the MDKT project is then detailed, followedby a description of the ontologies that implement the knowledge part ofthe system. The emphasis is given on the competence, document, and userontologies that are used by the dissemination and retrieval services in orderto focus their response.

METHODOLOGICAL ASPECTS

The realization of the MDKTsystem relies on works related to the use ofontologies for enterprises (Barry 1998; Uschold et al. 1998) and domains ofknowledge. Many of those (Abecker et al. 2000; Fox et al. 1996) refer to thework of Abecker et al. (1998) which, within the framework of the project‘‘KnowMore,’’ has developed an architecture of organizational memory,meeting the needs for interrogation in the context of a task.

While the KnowMore project mainly deals with domain knowledge tocharacterize documents, the MDKT project adds another dimension, thecompetence, which is also used to characterize users. The MDKT projectshares with the Ontologging project (Razmerita et al. 2003) the objectiveof integrating agents into the system to contribute to a better personaliza-tion of knowledge delivery and to provide adapted content.

Another original aspect of the MDKT project is its commitment torespect and use the standards proposed for the Semantic Web in orderto benefit from the developments and research made in this framework.

As defined by Tim Berners-Lee and his collaborators, the ‘‘SemanticWeb is not a separate Web but an extension of the current one, in which

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information is given well-defined meaning, better enabling computers andpeople to work in cooperation . . . For the Semantic Web to function, com-puters must have access to structured collections of information and sets ofinference rules that they can use to conduct automated reasoning’’(Berners-Lee et al. 2001). In order to achieve adequate inferences aboutknowledge, computer programs must have, for example, a way to discoverthe common meanings for the terms designing a single concept. A solutionto this problem is provided by another basic component of the SemanticWeb, collections of information called ontologies. ‘‘In philosophy, anontology is a theory about the nature of existence, of what types of thingsexist; ontology as a discipline studies such theories. Artificial-intelligenceand Web researchers have co-opted the term for their own jargon, andfor them an ontology is a document or file that formally defines the rela-tions among terms’’ (Berners-Lee et al. 2001). In order to be used in thecontext of a Grid, ontologies must be published and shared and become‘‘common ontologies.’’

The research in the framework of the Semantic Web have severalobjectives:

. Define languages based on XML in order to represent a knowledge thatcan be processed by computer.

. Build ontologies to describe domains of knowledge.

. Use ontologies to produce semantic annotation for documents on theWeb.

. Define browsers and search engines able to take into account the seman-tic annotations.

The MDKT system realizes and integrates the aspects mentioned. Com-pared to the extent of the Semantic Web, the scope is however considerablyreduced. One other main difference is that the system context of use canbe precisely defined as well as the users’ competences and skills. These par-ticularities are heavily used by the system in order to focus the knowledgeaccess tools.

THE ARCHITECTURE OF THE SYSTEM

The architecture is organized in three blocks (Figure 1): the editingand maintenance tools, the operating services, and the knowledge model.

The knowledge model in the MDKT architecture is organized in threedifferent layers by the mean of ontologies connected to a base of documentresources. The first layer is called the meta structure layer and it is based onspecifications proposed by the W3C for the OWL or the DAMLþOIL lan-guages. The second one is the structure layer, which describes the structureof the knowledge needed by the tools of the MDKT system. The third one,

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called the data layer, is the instantiation of the second layer. The resourcespart contains electronic documents or Web references to documents.

In the current implementation, tools, services and most parts of theknowledge model are on a single server. In the context of the Grid theycould be spread on several providers. One of these could host the operat-ing services and the others could host the two upper layers of knowledge.The data layer is client dependent and should be instantiated on therequester side. The editing and annotation tools can also be Grid providedservices, but the help system for the acquisition of new concepts is toodomain dependent to be in the same situation.

Meta Structure Layer

This layer describes the structure of an ontology according to the W3Cspecifications and standards. It is the ontology meta model. It explains howclasses (concepts) and properties (relations) are related and expresses allthe relationships between concepts in a domain. This model is built aroundthree main concepts: class axioms, property axioms, and individual axioms,and is defined by standard documents published by the W3 Consortium.These documents are imported as name spaces by the other layers. Inaddition to these standard documents, the meta structure layer also definesthe notion of ‘‘concept,’’ which generalizes all the entities of the domainknowledge.

The Structure Layer

The structure layer contains ontologies implementing the model of theknowledge used to perform the dissemination and the retrieval of domain

FIGURE 1 Architecture of the MDKT system.

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knowledge. This layer is built using the meta structure layer and more pre-cisely the class and the property axioms. It contains all the classes1 shown inFigure 2. These classes are organized in six different ontologies: PAT,Knowledge Domain, Document, Competence, Employee, and Company.

. PAT: This ontology contains the description of all the working processesthat exist in a company. The abstract class PAT is specialized into process,activity, and task. This ontology also characterizes all the relationsbetween these items.

. Domain: This ontology describes general elements and properties con-cerning the organization of knowledge on a domain. All these itemsare instances and also subclasses of the class ‘‘concept,’’ which representsall the concepts that can exist in all domains. One of the properties of‘‘concept,’’ which is inherited by all the items of the knowledge domain,is to be quoted in a document resource. Apart from the notion of con-cept, the content of this ontology can be imported from an existingontology about a domain.

. Document: This ontology defines the general nature of documentresources. The abstract class DocumentResource is specialized into twoconcrete classes: document and document part. The other definitionsabout the nature of documents are imported from the DublinCore name-space, which is the most used and known ontology about documents.

FIGURE 2 The knowledge model of MDKT.

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. Competence: This ontology is the center of the knowledge model and isdescribed in greater detail later. It makes it possible to link documents tousers, processes to users, and concepts to users.

. Employee: This ontology contains the concepts allowing to define theprofile of any user of the system that is an employee of a company. Ithelps in personalizing the exchange between the system and a particularuser.

. Company: This ontology describes a company in particular in terms ofroles. A role can be played by an employee and needs some competences.

Data Layer

It represents the third layer of the architecture. It contains assertionsabout the previous ontologies, describing a real company with employeesto whom some specific documentation can be delivered to help themachieve their tasks.

All the instances of the class ‘‘concept’’ are also at this layer. In the caseof the MDKT project, the domain is wireless networking so that instances of‘‘concept’’ like access-point or antenna are in this layer but are also con-sidered as classes in a hierarchy of classes in the upper layer. For example,the former ones are both subclasses of the class ‘‘device.’’ All these classesare also subclasses of the main class ‘‘concept,’’ so that they inherit the pro-perty being quoted in a document. A particular brand of antenna, which isan instance of the class ‘‘antenna,’’ can thus be quoted in a document partas well as the class itself.

Document Resources

The block document resources contains the entire documentationavailable by the mean of the dissemination or retrieval tools. Each of thesedocuments is designated by a URI. The resource can be in one of the manyformats usable on the Internet: HTML, PDF, word, video, etc.

THE COMPETENCE ONTOLOGY

A competence is a know-how, a knowledge or a behavior that candirectly or indirectly be measured, calculated, acquired, indicated, or exam-ined. This behavior makes it possible for a person to carry out a task inaccordance with the requirements of a situation of work.

Competence exists in relation to other concepts describing the capa-bility, the skill, and the expertise of a person. These elements and their rela-tions, in the case of the MDKT project, constitute the model ofcompetences that is illustrated in the example given next.

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The relation between employees and competences in the MDKT pro-ject is not only directly specified, it exists also indirectly through the rolescarried out by the employees. This relation represents the requirements anemployee must fulfill when it takes part in a project or an activity of thecompany.

There are also relations between competences themselves. Four types ofrelations are usually distinguished (of which only the first three are used inthe MDKT project): analogy, generalization, aggregation, and deviation(Nkambou and Lefebvre 1996):

. Relation of analogy: Competences can be similar from the point of viewof their functionality, their result, or their definition.

. Relation of generalization: Two competences bound by this typeof relation, share the attributes of the most general one but the mostspecific one has additional attributes.

. Relation of aggregation: This relation establishes the fact that acompetence is a component of another.

. Relation of deviation: This relation exists if a competence is a deformationof another.

There are several taxonomies of competences depending on the pointsof view and the criteria used. Competences, in the case of the MDKT

TABLE 1 The Skills Taxonomy According to Paquette

Skills taxonomy layers

1 2 3

Receive 1. Pay attention2. Integrate 2.1 Identify

2.2 MemorizeReproduce 3. Instantiate=Specify 3.1 Illustrate

3.2 Discriminate3.3 Clarify

4. Transpose=Translate5. Apply 5.1 Use

5.2 SimulateCreate 6. Analyze 6.1 Deduce

6.2 Classify6.3 Predict6.4 Diagnose

7. Repair8. Synthesize 8.1 Induce

8.2 Plan8.3 Model=Construct

Self-manage 9. Evaluate10. Self-manage 10.1 Influence

10.2 Self-control

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project, are classified in two groups. They can be transverse or specific. Thespecific ones are directly related to the realization of a task while the trans-verse ones refer to competences that can be needed in the whole process ofwork and are often related to domain knowledge.

A competence is associated to a specific level in order to be evaluated‘‘quantitatively’’ and ‘‘qualitatively.’’ Level 1 means beginner while level 5means expert. The combination of a competence and a level is called anexpertise. Moreover, competences can be qualified by skills, which canpresent various levels of complexity (Paquette 2002) organized in ataxonomy (Table 1).

An Example of Assertions about Competences

As previously said, an expertise level is a measurement that allowsto quantify a given competence. The association between an expertiselevel and a qualified competence defines an expertise. In Example 1,the expertise ‘‘CharacterizeAssembly_1’’ associates the competence ‘‘dict-Comp_T002’’ to the level ‘‘Level2.’’ This means that the person who hasthe expertise ‘‘CharacterizeAssembly_1’’ is able to do ‘‘characterizeassembly tools and techniques’’ at the junior level. This competence is for-mally qualified by the skill ‘‘skill_classify,’’ which is at the bottom of the skilltaxonomy. It has at the parent level the skill ‘‘skill_analyze’’ which itself isgeneralized by the skill ‘‘skill_create’’ (the whole taxonomy of skills ispresented in Table 1) (Paquette 2002).

In Example 2, user ‘‘ROBL1’’ has expertises such as ‘‘InstallNetwork_3,’’‘‘InstallInterfaceCard_4,’’ or ‘‘CharacterizeAssembly_2.’’ He has the role‘‘KO995428659670’’ in an unspecified project of the company. The expert-ise ‘‘InstallNetwork_3’’ is at level 3, which means ‘‘Intermediate’’ and isrelated to the competence ‘‘dictComp_S001: be able to install a network.’’In the taxonomy defined in the competence ontology, this general com-petence is decomposed in several more specific ones such as ‘‘dict-Comp_S002: be able to install interface cards’’ or ‘‘dictComp_S003: beable to install software.’’ It will be inferred by the system that user ‘‘ROBL1’’has at least these more specific expertises at the same level than the moregeneral one. This inferred knowledge can be superseded by an explicitassertion. In Example 2, for the competence ‘‘dictComp_S002,’’ the user‘‘ROBL1’’ has in fact a better expertise, which is ‘‘InstallInterfaceCard_4.’’This one is associated with ‘‘Level4,’’ which corresponds to ‘‘Specialist.’’

The role ‘‘KO995428659670’’ is described in Example 3. It requiresitself specific expertises: such as ‘‘InstallNetwork_1,’’ ‘‘InstallInterface-Card_1,’’ or ‘‘CharacterizeAssembly_1.’’ It is inferred that every user havingthis role in the company has at least these expertises.

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EXAMPLE 1 Competence assertions in file ‘‘competenceAssertions.daml.’’

EXAMPLE 2 User assertions in file ‘‘employeeAssertions.daml.’’

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For a given document, in order to be able to understand it, a personneeds expertises (necessary expertise). The document can bring, after beingread, new expertises (acquired expertise). In Example 4, the document‘‘Doc13’’ requires expertises such as ‘‘InstallNetwork_2’’ or ‘‘ProposeTechnol-ogy_4.’’ It helps to acquire ‘‘InstallNetwork_5’’ and ‘‘ProposeTechnology_4.’’

The filtering mechanism is used for documents and users according tothe semantic relations between these concepts by ensuring that the firstone is suitable for the second one. The notion of zone of proximal devel-opment presented in the next subsection is the key feature for theimplementation of this mechanism.

EXAMPLE 3 Role assertions in file ‘‘companyAssertions.daml.’’

EXAMPLE 4 Document assertions in file "documentAssertions.daml."

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The Zone of Proximal Development

The zone of proximal development, according to Vygotsky (Vygotskiiand Kozulin 1986; Vygotskii and Cole 1978), is ‘‘the distance between thecurrent level of development of a person when it solves the problem allalone (without any guided instruction) and the level of potential develop-ment which is that that this person has when it solves the same problemwith the assistance of an adult or when it collaborates with another moreadvanced person (with external assistances).’’

The concept of zone of proximal development enables to determine,with the collaboration of the competence model, the capacity of trainingof a person in a specific field in order to decide if such a document, whichrequires some expertise to be understood and which helps to acquire someexpertise, is adapted or not for such a person.

The distance between the zone of proximal development and the cur-rent development is called the depth of the zone. It can vary according tothe fields. It is not the same for each person either. In the MDKT project,this depth is defined by the difference between the acquired and therequired expertise. It is determine for each document but it does not varyfrom a user to another.

Semantic Reasoning

The ontology languages (RDF, DAMLþOIL) have theoretical basesthat are description logics. In fact, DAMLþOIL is equivalent to theSHIQ-DL (Horrocks 2002; Fensel et al. 2001). A semantic reasoning ismade thanks to the implicit or explicit semantic relations between the enti-ties or the classes represented in an ontology. It is based on the subsump-tion inference process.

For example, a person wanting to buy roses, in a semantic research,can be connected to a florist thanks to a semantic link between the classflower (since the rose is one of its specializations) and the florist who sellsthem.

In addition to subsumption, heuristics can be added in a semanticreasoning in order to have more ‘‘intelligent’’ and ‘‘natural’’ behaviors.

For example, the set of persons having expertise ‘‘InstallNetwork_3’’ isnot only composed by persons having exactly this feature but also by per-sons having the expertises ‘‘InstallNetwork_4’’ and ‘‘InstallNetwork_5,’’which are at a higher level.

Other reasoning heuristics, which are implemented in the system, con-cern the aggregation relation (‘‘isDecomposeIn’’) and the analogy(‘‘sameAs’’) between competences or skills (Figure 2).

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REALIZATION OF THE ACTIVE DISSEMINATIONAND RETRIEVAL SERVICES

The active dissemination must, being given a document, be able to fil-ter and provide to the administrator a list of users who are candidates toreceive the document. It must make it possible to the expert to excludepeople who are nonrelevant and finally it must be able to send a messageto the selected users.

The primary requirements of the service are thus:

. To filter users with respect to a document (and conversely for theretrieval service) by using the three key concepts (competence model,zone of proximal development, and semantic reasoning) describedpreviously.

. To send a message containing information related to the document tothe selected users (active dissemination) or to visualize informationabout the retrieved documents (intelligent retrieval).

In addition to these primary requirements, the service has to:

. Check in order to give access only to the employees of the company.

. Ensure that the dissemination is carried out by the administrators.

. Ensure that filtering is accurate and safe.

. Be configurable by the parameters of a configuration ontology.

. Be achievable on different platforms.

. Allow to visualize and navigate in the graph of ontologies for administra-tive purposes.

For the dissemination, the service must memorize the state of the docu-ments in order not to omit documents in the list of documents to dissemi-nate and not to importune the administrator by suggesting documentsalready disseminated.

Filtering Strategy

As evoked previously, the engine filters the users with regard to a givendocument (or conversely) by taking into account the necessary expertisesto understand the document and those the user will acquire thanks tothe document. The strategy uses not only the direct expertises (thoseexplicitly related to the employee and the documentary resource), but alsothe indirect expertises obtained by semantically reasoning around the fourconcepts: role, level, competence, and skill:

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. Role: When a person is assigned a role and if this role requires someexpertises, then it is inferred that this person possesses the correspond-ing expertises.

. Level: If a person has an expertise characterized by a level and a qualifiedcompetence, then the inference engine assumes that this person also hasall the expertises, which are defined with a lower level and the samequalified competence.

. Competence: If the competence associated to an expertise level is decom-posed in several subcompetences, it is inferred that a person havingthe corresponding expertise also has all the expertises associated to thesubcompetences with the same level and skill qualification.

. Skill: If the skill qualifying a competence associated to an expertise levelis more general than another skill, then it is inferred that each personhaving the corresponding expertise also has the expertise with the com-petence qualified by the less general skill at the same level.

Considering all these criteria, the filtering system operates a closure onthe expertises for a person and compares this closure to the expertise spe-cifications for a document. The document is semantically in the proximalzone of development if the required expertises for the document areincluded in this closure and if at least one of the acquired expertises isnot in this closure.

Intelligent Querying Service

The Intelligent Querying Service (IQS) is an advanced Semantic Web-based information retrieval service. It searches for documents for a usernot only on the basis of concepts in the domain knowledge, but it takes alsointo account the working context and the user model. For example, a usercan ask for documents that can help him to achieve a task. The service willprovide only the documents that are related to this task and are relevantaccording to the competences required. A filtering mechanism analogousto the one used for the dissemination is used. In the case of thismechanism, only the required competences for a document must belongto the competences closure for a person.

CONCLUSION

The MDKT project is aimed to provide enterprises with the means toallow them to address this knowledge acquisition and transmission problemrelated to technological innovation.

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The challenges this project raises are numerous, including:

. Defining a sufficiently explicit representation of the semantic content ofthe documents of the enterprise. From this representation, we should beable to characterize:

� The degree of relevance for a human resource of elements of infor-mation appearing in the document.

� The adequacy of the formulation of the content regarding the expert-ise of this resource.

. Designing the most suitable set of tools to generate this representation ina partially automated way.

. Setting up a user model and the associated competence model in orderfor the dissemination and retrieval tools to adequately permit:� The selection of people.� The selection of the suitable information.� A presentation of this information adapted to every category ofresources.

In order for the dissemination and retrieval services to be published onthe Grid, an agreement must be found on the way knowledge about enter-prises, employees, and competences, which are all needed by these services,must be represented. Shared and common ontologies implemented withthe standard language proposed for the Semantic Web could adequatelytechnically achieve this goal as shown by this project.

The system, which has been built, has been instantiated in order toadequately test and validate the tools. However, in order to have an oper-ational system in the context of a large enterprise and to help in theinstantiation and maintenance task of the system, other tools have to berealized, which are crucial for the long-term life of the system. Amongthe remaining problems is the assistance to the domain ontology mainte-nance. This can be achieved by a statistical analysis of new documents.Such a solution has been considered and is under construction (Gargouriet al. 2003).

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NOTE

1. The concepts of the knowledge domain are also considered as instances in the data layer.

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