Annals of Dunarea de Jos University of Galati Fascicle I. Economics and Applied Informatics

Years XVII no1/2011 ISSN 1584-0409 www.ann.ugal.ro/eco

KnowledgeManagementSystemandUserModelingCorneliaNOVACUDUDEC a,VasileMAZILESCUba,bFacultyofEconomicsandBusinessAdministration,DunareadeJosUniversityofGalati,5961NicolaeBalcescuStreet,Galati,Romania

A R T I C L E I N F O A B S T R A C T

Articlehistory:Accepted15January2011Availableonline31March2011JELClassificationC63,C88Keywords:KMS,Knowledgemanagement,Usermodel

Theaimofthepaperistorelevesomeaspectsregardingaknowledgemanagementsystemandausermodelwhichwillbe implementedinthetrainingprocessof thestudents.Theyarealso presented some featuresand the personalization in aKnowledgeManagementSystems.Theporposeistodevelopanontologyfortheusermodellinginthiscontextandlatertousethisinamultiagentsystemfortheknowledgemanagement.

2011EAI.Allrightsreserved.

1.Introduction In the academic environment, the all educational system may be considered as an knowledgemanagement system. Ifwehave inminda some specialization in Economics and the all knowledgewhichmustbegiventostudentsforbecomegoodprofessionals,wecanconsiderthatweoperatewithaknowledgesystemwhichmustbecontolledandmanaged. In the first time, the users of the system are the teachers, which must cooperate between them andstandingenhancethedomainknowledge.Thesecondtime,theusersarethestudents.Inextentthatexistsagoodknowledgemanagemnt,theresultsofthestudentsinthetrainingprocesswillbethebest.Knowledgemanagementhasreceivedincreasingattentioninthelastyearsasmanyorganizationsperceivedtheneedtobe knowledgeoriented or learning organizations. Knowledge in the context of Knowledge ManagementSystems(KMS)consistsofexperience,knowhowandexpertiseofthepeople(tacitknowledge)anddifferentinformationartifactsanddatastoredindocuments,reportsavailablewithintheorganizationandoutsidetheorganization (explicit knowledge). Different knowledge management experts have different views, someemphasize the technology dimension, and some emphasize the intellectual capital while others putcommunitybuilding first.Nowadays thedominant viewofKMS is often focusedon the technology, on theprocess of capturing, organizing and retrieving information based on notions like databases, documents,query languages and knowledge mining [29]. From a technological perspective, the main objective ofKnowledgeManagementSystemsistoprovideuniformandseamlessaccesstoanyrelevantinformationforataskbeingundertaken.Mostofthecurrentknowledgemanagementsystemsintegrateknowledgeprocessesfromanorganizationalperspectiveandtheyarenotnecessarilyaddressingtheindividualneedsoftheusers. TheresearchismotivatedbyourviewthatthedevelopmentofKMSistoolimitative;thattheconceptionofknowledgemanagementsystemsneedtotakeintoconsiderationmorethehumandimension,thehumanneedsanditssocialcontext.Ononehand,bymodelingtheusers,thesystemisabletocaptureandstoretheuser'sneedsandgoalsinordertobeabletobettersupporttheusersandmeettheirneeds.WeagreewithPrusak [25] who emphasizes that the user satisfaction is more important then the performance of thetechnology:"knowledgemanagementsharesinformationmanagement'suserperspectiveafocusonvalueasa function of user satisfaction rather than the efficiency of the technology that houses and delivers theinformation".Moreover,investigationsontheuser'sneedscarriedoutthroughinterviewsorreportedintheliteratureshoweddifferencesinindividualuserneedsandheterogeneitybetweendifferentgroupsofpeople. The generalpurpose KMS usually don't take into consideration the individual users and their specificneeds,whichmightimplytwomainproblems: unnecessaryinformationisdisplayedwhichmightleadtoinformationoverloadoftheusers[9]; needed/desiredinformationismissing. Personalized interaction, personalized views and adaptive features integrationwithinwould enable toovercomesuchproblems.Thekeyelementthatenablestheintegrationofsuchfeaturesistheusermodelandtheusermodelingprocesses. On the other hand, it is essential to focusmore on the people dimension to enable "getting the rightinformationattherightpeople,attherighttime."emphasizingthepeopledimensionitalsomeanstogettoknowbettertheusersandtheircharacteristics(competencies,interests,preferencesetc.).

Emailaddresses:cornelia.novac@ugal.ro(C.NovacUdudec),vasile.mazilescu@ugal.ro(V.Mazilescu)

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Prusak remarks [25] "by definition human capital focuses on the individualwhereasmost knowledgemanagementworkisconcernedwithgroups,communitiesandnetworks."Maintainingusermodelenablestocapture their competencies, their expertise, their interests and implicitly to better manage the tacitknowledgeandthehumancapital.Designingeffectiveknowledgemanagementsystemsrequiresnotonlyaview,whichachievedbyconsideringorganizational imperativesandtechnologicalsolutionsbuttakingintoaccountausercenteredperspective,byconsideringtheindividualneedsoftheusersandotherissueslikeusability,ergonomicsetc. ThispaperfocusesonusermodelsandusermodelingprocessesinaKMS.TodateusermodelingissuesinKMShavenotbeentreatedindepthneitherinusermodelingliteraturenorintheliteraturerelatedtoKMS.Discussions of user profiles integrated inKMS are found at a very general level if at all. However aspectsrelatedto informationoverload inKMSandwhyKMS failhavebeen largelydiscussed in the literatureareoften related to the users goals and needs, their perception of environment etc. The usermodels or userprofiles integrated in theKMS can improve the interactionbetween theusers and theKMSmanifolds.WewanttodevelopausermodelandtointegratethisusermodelinanontologybasedKMSwiththepurposeofbettersupportingtheusersbyprovidingpersonalizedservicesandbyintegratingdifferentadaptivefeatures.Itbringsexplicitand implicituser together ina common frameworkon theontologysbased.Due to theirknowledgerepresentationformalismandassociatedinferencemechanisms,ontologybasedsystemsseemtobeanaturalchoiceforthenextgenerationofKMS. Thepaperpresentssomeofthemechanismemployedforimplicitusermodelingandnamelyfortrackingthe user's behavior in aKMS. The integration of theusermodels enables to combine complex "high tech"features with more "high touch" features. These "high touch" features could be achieved by integratingpersonalizedservices,adaptableandadaptivefeaturesandbysocialprocesses.ThesehightouchfeatureswouldincreasetheefficiencyandthesatisfactionoftheusersandindirectlytheacceptanceandtheusabilityoftheKMS. The integrationof usermodels inKMSopens a large number of research questions some of themarecommontothegeneralobjectivesofusermodeling,somearemorespecifictothetoknowledgemanagementandsomearerelatedtotheuseofontologys forrepresentingusermodels.TheproblemofusermodelingintoKMS requires amultidisciplinary approachand it canbebrokendown into several general questionslike: WhyitisimportanttomodelusersinaKMS? WhatarethemostrelevantcharacteristicsoftheusersinaKMS? Whattypeofuser'sbehaviorcanbedistinguishedinaKMS? What type of modeling techniques can be applied in order to track the users' behavior and the

maintenanceoftheusermodelsinaKMS? Howcanausermodel improvetheinteractionwithaKMS?ormoreexplicitly:Whattypeofintelligent

/personalizedservicescanbeprovidedbasedonthesecharacteristics? Whataretheadvantages/limitationsofapplyingontologysinusermodeling? WhataretheperspectivesofitsuseinthecontextoftheSemanticWeb? Are security or privacy issues an impediment towards the use of such models and how can it be

overcome?

2.SomeaspectsofusersmodelinginthecontextofaKnowledgeManagementSystem

"Thechallengeinaninformationrichworldisnotonlytomakeinformationavailabletothepeopleatanytime,atanyplaceand inanyform,butspecificallytosaythe"right"thingat the"right"timeinthe"right"way"[11].

2.1.Hightouchfeaturesandusermodeling

Thedynamicofchangeinthebusinessenvironmentsrequirestotheorganizationsknowledgeworkerstocontinuously learn and adapt in order to remain competitive. The knowledge is considered the mostimportant asset for the organizations and the effective management of knowledge became an importantissue. The organizational success is considered to be heavy dependent on the effective management oforganizationalknowledge.Themostofthecurrentknowledgemanagementsystemsintegratetheknowledgeprocessesfromanorganizationalperspectiveandtheyenabletocapture,transform,organizeanddistributeinformation.Manyofthemhaveadocumentcentricapproach,whichemphasizesthefunctionalitytoaccessandmanipulatetheexplicitknowledgeassetsoftheorganizationsuchas:storing,searching,classifyingandretrieving documents. Even the knowledge management systems tend to become more complex as theyincorporate more functionality and support more knowledge related processes; they are not necessarilymoresuccessfulfromausabilityperspective. Theinvestigationsoftheuser'sneedscarriedoutthroughinterviewsorreportedintheliteratureshowedsubstantial differences in the individualuserneedsand theheterogeneitybetween thedifferentgroupsofpeople.Thisunderlies the importanceofpersonalized interactionandadaptive features integrationwithinKMS. Themain element that enables the integration of such features is the usermodel. The usermodelsusuallycontainsinformationabouttheuser:hisinterests,hispreferences,hisgoals,workhabits,etc. The challenge for thenextgenerationofKMS is togobeyond traditionaldesignofKMSby integratingadvanced technologies like: ontologys, agents and user modeling. The next generation of KMS needs tocombine complex "high tech" features with more "high touch" features. The "high touch" features couldimprovetheacceptanceoftheKMSandtheirusability.TheKnowledgeManagementSystemsaredesignedto

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allowemployeestoaccessandutilizetherichsourcesofdata,information,andknowledgestoredindifferentforms(tacitandexplicit).OneofthemainfunctionalitiesaKMSshouldsupportisinformationretrieval;thisisanimportantissueasoftenorganizationshavevariousknowledgesources.Makingavailablethedifferentknowledge sources is a problem of structuring and representing knowledge. A knowledge managementsystemusesontologytorepresentandstructurethedifferentknowledgesourcesinitsbusinessdomain[6],[21].Theexistingknowledgesources(documents,reports,images,videosetc.)aremappedintothedomainontologyandaresemanticallyenriched.Thissemanticallyenrichedinformationenablesabetterknowledgeindexingand searchingprocessesand implicitly abettermanagementof explicitknowledge. Inaddition, abettermanagementoftacitknowledgeimpliesaneedtofocusontheemployees,theusersofthesystem,onmodeling,theircompetencies,theirinterestsandtheircharacteristics. The"hightouch"featurescouldbeachievedbyintegratingpersonalizedservices,adaptableandadaptivefeaturesandbyreinforcingsocialprocesses.Inafirstphase,thisimpliesthattheseKMSwouldchangetheirstructureandcontenttomatchtheneedsandpreferencesofauserbasedonausermodelwhichisstoredand updated dynamically. A certain degree of personalization can be achieved by indicating preferences,whichdeterminethe"lookandfeel"ofthesystem.Moresophisticatedsystemswouldenabletoautomaticallyadapt to the user's preferences, user's inferred needs and goals; this type of systems is also known asadaptive systems. The use of personal agents could bringmore interactive and dynamic systems into thesceneandanotherlevelofcomplexityand"touch".Personalagentswouldenablethesystemstoactivelyseekwaystosupporttheusersinachievingtheirtasks. Another challenge in current knowledgemanagement systems is to reinforce the social dimension forincreasedhuman"touch"dimensionintegrationandtostimulateknowledgesharing.Thesocialdimensionisvery important and currently achievedmainly by including collaborative tools for stimulating knowledgeexchanges,knowledgesharingandbyprovidingmotivationalfactors. SomechallengescanbesummarizedasfollowingrequirementsandneedsfortheKMS: a focus on the user himself when designing a system in order to provide himwith functionality and

knowledgethatisbestadaptedtohisneeds; tosupportabetterpersonalizationofknowledgedelivery; tosupportcontinuouslearningandtrainingoftheknowledgeworkers; toaddressaspectsofmotivationforknowledgesharingandknowledgecreation; to include collaborative tools which support social processes for the dynamic exchange of tacit

knowledge; Designing effective knowledge management systems requires not only a view, which is achieved byconsidering organizational imperatives and technological solutions but also by considering the individualneedsoftheusersandotherissueslikeusability,ergonomicsetc.

2.2.Modelingtheuser'sbehaviorinaKMS

2.2.1.Modelingtheinformationuser Theworkat theUniversityofSheffieldconductedbyWilsonhas focusedontheeffectofcognitivestyleoninformationseeking behavior [30], [13] reveals differences in such behavior by peoplewith different learningstyles.Theyhavestartedtheirinvestigationsoninformationseekingbehaviorbyconsideringthecommunicationinorganizationsgenerally.Theyshowedthatbehaviorispredominantlyoralcommunication.Theirinvestigationshave revealed that the time spent in meetings was decreasing tremendous from executive level towardsoperationalstaff.

Table1.TimespentinmeetingsfromWilsonsresearch[30]

LevelofStaffNo.ofstaff

No.ofMeetings

AverageNo.ofHours

Directorate 5 60 16.8LineManager 5 38 13.4Specialist 4 21 6.5Fieldworker 6 19 4.8Administration 2 3 0.6Totals 22 141 9.0

Fromtheseresultsonmanagerialbehaviorheconcludesthatarelikelytoaffecttheusabilityofinformationsystemsandheemphasizesfewlimitationsofthesystems: interactive systems that incorporate speech recognition, as oral communication seems to be

predominant,mightbebetterespeciallyforexecutiveinformationsystems; theyarenotwelldesignedforcommonuseinmeetingsand theynotallowstructuringdebatesaboutcertaintopics; Wilsonmakes reference toanotherperspectiveon cognitive styles introducedbyBenbasat andTaylor(1978).Theyshowedthatanalyticdecisionmakershaddifferentstylesfromthosewhoemployedheuristicmethods, and that there was another division of style into perceptive and receptive, which also hadimplicationsforsystemdesign.

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TheworkbyBenbasatandTayloremphasizesthatcognitiveorlearningstylesdovaryamongindividuals,andthatitishighlyunlikelythatasystemdesignedaccordingtoonecognitivemodelwillfitthebehaviorandexpectationsofauserwhosemodelisdifferent.Wilson[30]arguesthatamodeloftheusermustbeginwithamodeloftheorganizationinwhichtheuserworks,andwithanunderstandingofthevariousorganizationaland interpersonal influences thatmayaffect his or her informationseekingbehavior. The research in thisareaoforganizationalworkandtheeffectoncognitivestyleoninformationseekingbehaviorsuggestedthatthesystemsbasedonamodeloftheuserinteractingwiththecomputersystemislikelytobetoolimitedforthecreationofeffectivesystems.2.2.2.Modelingtheuser'sknowledgesharingbehavior The knowledge sharing is one of the most important factors for the success of KMS. Through theknowledgesharingbehavioristryingtocapturethelevelofadoptionoftheknowledgesharingpractices.Itconsiders the organizational and the behavioral changemanagement to be a critical success factor in theimplementationoftheknowledgemanagementstrategies."Theusersaredescribedasundergoingachangeprocessthatbringsthemfromtheiroldpracticestotheconsciousadoptionoftheknowledgemanagementpractices (e.g. transition from low or nonexisting levels of the knowledge sharing practices to thewidespreadadoptionofthebestbehaviorsinknowledgesharing)."[26] Ondefinesachangeprocessasasequenceofchangeoperationsuponuserstates,leadingtoanend(theacquisition of the desired behaviors). In particular, it defines the change process following the modelproposedbyRogers(1995)inwhich"anindividual[...]passes(1)fromfirstknowledgeofaninnovation,(2)toforminganattitudetowardstheinnovation,(3)toadecisiontoadoptorreject,(4)toimplementationofthenewidea,and(5)toconfirmationofthisdecision"(Fig.1). BorrowingNear's[22]terminologyandmappingitintoRogers'theory[2],thefollowinguserstatescanbe identified: "The firststage isawareness, inwhichthe individual isalerted to theexistenceofsomethingnew.Nextistheintereststage,inwhichtheindividualgathersinformationandanarousedlevelofcuriosity.Thisisfollowedbytheappraisal/trialstage,inwhichthenewideaistriedoutinatrialoperation.Thefinalstageistheadoption,inwhichtheindividualincorporatestheinnovationasapartoftheresourcesheorsheusesonthejob"[22]. The numbers indicate the mapping to Rogers' model. Initially individuals become aware of a newbehavior (for instance, the behavior "acknowledge sources of information"); this corresponds to theachievementofphase(1)ofRogers'model.Becomingawareofabehaviorimpliesknowingwhatthebehavioris,how it canbepracticed,whatare theadvantagesordisadvantagesof thebehavior,whocanhelp in theadoption,andwhatarethemainrepositoriesofinformationforthegivenbehavior.Theawarenessstateisapassivestatethatmayresemblesomeclassic learningphasesinwhichateacher,orabook,supplyasetofbasicinformationonagivensubjecttoalearner.

Figure1.Amodelofthechangeprocess Based on the information acquired while passing from the ignorance state to the awareness state,communitymemberscanformapositiveornegativeattitudetowardstheinnovationbeingintroducedandtheymaymove to an interest state. This corresponds to the positive achievement of phase (2) of Rogers'model.Wheninthisstate,peopleactivelyseekinformationthatisoftenmuchmorespecificandsituatedthanthe informationobtained in theawarenessphase.Thisstate is alsocharacterizedbyanactiveandpositiveresponse to stimulus received regarding the newbehavior, e.g. willingness to participate in conversationsaboutvariousaspectsofthebehaviors. Once the individuals have collected enough information, they are in the position to take the decisionwhether they should try to practice the new behavior. This corresponds to phases (3) and (4) in Rogers'model.Ifthedecisionisfavorablethentheyenterthetrialphase.Inthisphaselearnersactuallyexperimentwith sharing their knowledge. Finally, the trial may result in confirming the adoption of the behavior, inwhich case the individuals enter into the adoption state, otherwise theymay opt for the rejection of thebehavior(phase(5)ofRogers'model). As theknowledge sharing is a critical aspect for the success of a knowledgemanagement system, it isintroducedamodel,whichenabletoassigntheusersintodifferentcategoriesbasedontheirbehaviorrelated

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toknowledge sharing. In the subsequent sectionwill bepresented the adaptive andpersonalized servicesthatcouldbeintegratedintheknowledgemanagementsystems.

2.3.AdaptivefeaturesandpersonalizationinKMS "Ispersonalizationhypeoropportunity?Itakethepositionit'sopportunity,butitmustbedefinedclearlyanditmustbedesignedtobeusefulandusable. Wesuggestthatpersonalizationisnotasilverbullet,butinsteadispairofthefollowingprimedirectiveforbusiness(...)"[27].WeagreewithRieckenthatpersonalizationisanopportunitytoprovidemore"hightouch"featuresandwealsothinkthatpersonalizationisanenablertoincreasetheusabilityofKMS. The knowledge management systems tend to become more complex in order to support the tasksassociatedwiththedifferentknowledgeorientedprocesses.The"hightech"featuresintegratedinKMSdon'tnecessarilyimply"hightouch"featuresthatcouldbeachievedbyintegratingpersonalizedservices,adaptableandadaptivefeaturesinnewinnovativeinterfaces. Adaptiveandpersonalizedservicesarehighlydesirablefeaturesofactualinformationsystemsandtheyhavethepotentialtoincreasethe'joy"oftheusersanditsusability.InordertointegratethesefunctionsandbettersupporttheirusersKMSneedtoconstructandmaintainausermodel. Ingeneral,theinformationinaknowledgemanagementsystemcanbepersonalizedintermsof:selectedcontent,theproposedlinks,differentviewsandlevelofdetailsaccordingtothepreferences,needsandrolesofthedifferentusers.Thiscouldreduce informationalcognitiveoverloadof theusersand improvetheKMsystemsacceptanceandusability. The literature related to adaptation in hypermedia systems [3] and personalization in the context of"online customer relationship" [20] identifies different levels of adaptive features: adaptation of content,adaptationofpresentationandmodalityuserpreferencesandadaptationofstructure. Morerecently,researchonadaptiveWebbasedsystemsistryingtogobeyondtheusercharacteristics.Kobsa [20] suggests adaptation to user data, usage data and environment data. User data containsinformationaboutpersonalcharacteristicsoftheuser,whileusagedataisrelatedtotheuserinteractionwiththesystem,environmentdatacompriseshardware,software,userlocationetc.2.3.1.Adaptivefeatures Inthecategoryofadaptivefeaturestherecanbeidentified:1. Adaptation of structure: personalized view of the corporate knowledge based on interest areas and

competenciesoftheusers.2. Adaptationofcontent: optionalexplanations:relatedtothelevelofexpertise;levelofcompetency(moredetailedexplanations

forthosewithoutcertainbackgroundknowledge); optionaldetailedinformation:relatedtointerestareas,thosewhoareinterestedinthesecategories; personalized recommendations: information (e.g. recommender systems conferences/ workshops attended

otherrecommendations); personalizedhintsformarkingpresumedinterests;

3. Adaptationofpresentationandmodalityrelatedtouserpreferences1. sorting;

addinglinks/shortcutstositespecificresources; differentlayouts,skins,etc.

2.3.2.Personalizedinteraction Personalizationcanbeachievedintwodifferentways: implicitlythroughdifferentintelligentservicesthataretransparentfortheusers explicitlythroughagent'sintervention. In figure2 it isrepresentedasimplifiedstructureofanontologybasedKMSincludingausermodelingsystem.Thepersonalizationmechanismsmakeuseoftheusermodeldataandareconnectedtothedomainontology. ThegoalofpersonalizationinaKnowledgeManagementSystemimplies:1. Collecting information about users, named log data in figure 2, "looking over the user's shoulder" to

observeandgatherasmuchinformationabouttheuserasisavailable;2. Tousethisinformationtoprovidedifferentintelligent/personalizedservicesthatautomatesometasks

andtoleveragethe"cognitiveoverload"oftheknowledgeworker; Personalizedservices(e.g.personalizedviewofthe"knowledgeassets"); Directaccesstocustomizedrelevantnews,contentanditsclassification(e.g.informationfiltering); Provideunobtrusiveassistance(interfaceagents,stimuliagentsintervention); Helpingtofind/recallinformationneededforatask; Offeringtoautomatecertaintasksthroughimplicitorexplicitintervention; Recognitionmechanisms for acknowledging adoption of knowledge sharing behaviors and knowledge

champions.

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Figure2.TheUsermodelsandpersonalizationmechanisms2.3.3.Personalizedviews "Personalizedviewsareawaytoorganizeanelectronicworkplacefortheuserswhoneedanaccesstoareasonablysmallpartofahyperspacefortheireverydaywork."[4].Globalstandardviewsversuspersonalizedview: Differenttypesofpersonalizedviewsofrepositoriescontentscanbeenvisaged: Personalizedviewbasedonthe jobtitle:classifyingusers instereotypicaldescriptionsbasedonthe

jobtitle/roleimpliesdifferentviewsonthesystemsbasedontheroleoftheusersanddifferentlevelofdetailsresearchonthecognitivestyle/modelinginformationuser;

Personalized view based on the interest areas: this implies the possibility of manually orautomaticallyselectingdifferentviewsofthedifferentexistingtaxonomies/ontologies(e.g.anfinancialexpertcouldhavethepossibilityofselectionofthefinanceontology);

Personalizedviewbasedon customizeduser interface layout, selectionof colorsEnhanced userinterfacedesigntakingintoaccountresearchconductedinthefieldofhumancomputerinterface(HCI)basedonprinciplesofusabilityandergonomics.Thepossibilityofcustomizingtheuserinterfaceimpliestoenableuserstocontroltheuserinterface,thelayout,thelevelofdetailsanditsfeatures;

2.4.Multiagentbasedsystems(MAS)andontologys MAS have the traditional advantages of distributed and concurrent problem solving, but have theadditionaldisadvantageofsophisticatedpatternsofinteractions.Examplesofcommontypesofinteractionsinclude:cooperation(workingtogethertowardsacommonaim);coordination(organizingproblemsolvingactivity so that harmful interactions are avoided or beneficial interactions are exploited); and negotiation(coming toanagreementwhich isacceptable toall theparties involved). It is the flexibilityandhighlevelnatureoftheseinteractionswhichdistinguishesmultiagentsystemsfromotherformsofsoftwareandwhichprovidestheunderlyingpoweroftheparadigm. Thedynamicnatureofagentdistributionmotivatesresearchbygroupsworkingonthestandardizationof dynamic collaborative multiagent systems. Some of these groups are the Foundation for IntelligentPhysical Agents (FIPA), theObjectManagementGroup (OMG), the KnowledgeableAgentoriented System(KAoS). Ontologys are built to enable a machine processable semantics of information sources that can becommunicatedbetweendifferentagents(softwareandhumans).Thewholesemanticwebvisionisbasedonthe idea thatontologyswill be a "key enabler forWebagents" andWeb services.The termontologywasintroducedasmeansforestablishingcommunicationbetweenagentsandmachines. Itisforeseenthatthenextgenerationofwebwillincorporatevariousagentbasedservicesbetweentheuserand the semanticallyenriched rawdataavailableon theweb.These serviceswill facilitate intelligentsupport for the users like: information access, retrieval, information filtering, etc. These services will beplacedinthelogiclayerpresentedinFigure3.(SemanticWeblayeredarchitecture)

Figure3.SemanticWeblayeredarchitecture

2.5.OntologysinKnowledgeManagementSystems KnowledgeManagementSystemsaredesignedtoallowemployeestoaccessandutilizetherichsourcesofdata,information,andknowledgestoredindifferentforms.OneofthemainfunctionalitiesaKMSshouldsupport is information retrieval; this is an important issue asoftenorganizationshavevariousknowledgesources. Making available the different knowledge sources is a problem of structuring and representing

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knowledge. Due to their powerful knowledge representation formalism and associated inferencemechanisms, ontologybased systems seem to be a natural choice for the next generation of KMSs. Aknowledgemanagementsystemusesontologytorepresentandstructurethedifferentknowledgesourcesinitsbusinessdomain.Existingknowledgesources(documents,reports, images,videosetc.)aremappedintothedomainontologyandaresemanticallyenriched.Several IST (IntelligentStudentTutoring)projects likeOntologging,OntoknowledgeareexploringtheuseofontologysinKMSspreparingafuturegenerationofKMSs.Ontologysineducationandstudentmodeling Ontologys are a promising research domain to overcome common AI_ED problems emphasized byMizoguchi [16]. Several researchers have approached the use of educational ontologys for intelligentlearning environments. Educational researchers agree on the fact that providing domain knowledge forlearning environments is difficult and time consuming. As for knowledge based systems, a knowledgeengineeror aprogrammerneeds to cooperatewithadomain expert inorder to formalize theknowledge.Oftenthedomainmodelcanbestructuredastaxonomyofconcepts,withattributesandrelationsconnectingthemwithotherconcepts,whichnaturallyleadstotheideaofusingontologystorepresentthisknowledge.Mizoguchi [16] argues that "making systems intelligent requires adeclarative representationofwhat theyknow. Conceptualization should be explicit to make authoring systems literate and intelligent,standardization or shared vocabulary will facilitate the reusability components and enable sharablespecificationofthemandtheoryawarenessmakesauthoringsystemsknowledgeable." AslightlydifferentperspectiveispresentedbyIkeda,whotakestheviewonontologysofametamodel,which facilitates the constructionof a training systems, "The ontologyplays the roleof ametamodel thathelpstheauthorsbuildamodel,thatisatrainingsysteminacertaindomain."[13]. ChenandMizoguchi(1999)proposesa learnermodelontologydefinedasa"learnermodelagent"andthey show how it is incorporated into a multiagent architecture. The ontologys define the necessaryconceptsneeded toexchangemessagesandcontaindomaindependentanddomain independentconcepts.Domain independent concepts are required for the agent communication.Heproposes an implementationbasedonKIF. Kay in1999discusses theproblemofusingontologys for reusableandscrutable studentmodels. Shearguesonthe fact thatareusablestudentorusermodel"needsanagreedrepresentationsothat itcanbeunderstood and used by different user models consumer programs." She also emphasizes the fact thatpowerfulpersonalizedsystemsneedthatmodeltobereused.Synopsis One of the most important aspects in order to achieve certain adaptive features and personalizedinteractionistheusermodel.Personalizedinteractionemergesasanimportantfeatureoftheactualsystems.Creating andmaintaining implicit usermodels is not a trivial process, it is a costly one as it requires theimplementationofadvanced,AIspecificmodeling techniques.Foreliminatingaspectsofuncertaintyof theinferredassumptionsabout theuser, thisdataneedtobevalidatedandacceptedbytheuser.Explicitusermodelsalsorequiretimespentbytheuserinordertofillinhis/herdata.Evenwhenausermodelisacquiredbyoneapplicationitisdifficulttoshareitwithotherapplications.Itisreasonablythatausertoexpecttobepossiblethatasecondsystemreusessomerelevantcharacteristicsofhisusermodel.Thiscouldbeachievedby storing the user model in a form that can be accessed and used by various applications. User modelrepresentation is a knowledge representation task. We argue that ontologys could provide a solution forimplementing "reusable" usermodel,which call be also used in the context of the next generation ofwebservices. Ontologys are a knowledge representationmechanism, which enable to addmetadata to the data inorder to enable automatically processing of dataover theweb. They enable to represent specific domainsbasedonconceptsandrelationshipsbetweenconcepts.OntologysprovidecomplexschemataformetadataandenableaddingsemanticstothecontentTheyseemtobethe rightanswertostructuringandmodelingproblems in knowledge representation by providing the basis for the definition of meaning. It is theunderlyingconceptoftheSemanticWeb. We analyzed the different ontology languages and the actualcandidatewebstandardsavailableformodelingontologys.RDF/RDFSofferverybasicmodelingprimitivesandtheyconstitutethebasiclayeronwhichricherschemaontologylanguagescanbebuilt.WehaveshowedhowWeb standards (XML+RDF/RDFS) can be used as a basic representation language for ontologies andespecially for simple usermodels. One of themajor problemwith ontology languages is the fact that theontologylanguagesdidn'tgettoastandardizedversion.Theresearchersintheareaarestill"struggling"togettopowerfulontologyrepresentationlanguagesothattheycanachievetheenvisagedsemanticwebvision.HowevertheyagreeonthefactthatXML,RDF/RDFSshouldbethebasisforsuchalanguage.Conclusions Our approach is that pedagogical agents should be have some knowledge of the students that goesbeyondthecurrentsession,exemplifycharacteristicswhichwewouldlikeourstudentstopossess(patience,tolerance, appreciative, valuing, kind, concerned, helpful), andbe engaged in their ownwork.They shouldalsohave someknowledgeof thedomain,how to teachand, ideally, someknowledgeof the cognitiveandaffectiveissuesinvolved. Inanotherpaper,wearegoingtopresenttheaspectsofusermodelingwithinknowledgemanagement

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systems (KMSs) and associated user model ontology implemented in an RDF/RDFS compliant languageKAON[14].References

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