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SearcherSearch for Course in BrokerAnonymous SearchGet Course DescriptionGet Course ServicesFIGURE 2CORE USE CASESCustomized SearchRefine Search• Identification of the aim and the scope of the ontology.• Consider to reuse existing vocabularies (in our case, wemake use of the elements defined on the data modelsidentified by the LT standardization process).• Enumerating the most important terms in the ontology.• Defining the classes and their hierarchy.• Defining the properties of the classes.• Defining the features of the properties.• Creating instances.In this way, the development of the ontology is aniterative process, centred on the architecture and driven by usecases, where each stage refines the previous one. As the usecases mature and are refined and specified in more detail,more of the ontology terms are discovered. This, in turn, canlead to new use cases. Therefore, both the ontology and theuse cases mature together.Users OntologyThis sub-ontology includes the properties and classes directlyrelated to the characterization of the users of the brokeragesystem. The terms identified in this namespace have beenmainly extracted from the data models Learning InformationPackage [16] and Accessibility [17], both developed by theIMS Consortium. The first of these standardized modelsidentifies the necessary elements to describe the characteristicsof a (potential) student, whereas the second one extends theprevious model with elements that allow us to specify certainuser preferences. The existence of the user’s ontology inELEARNING-ONT makes it possible to the intermediationsystem the accomplishment of searches adapted to the userSession S3Hneeds and preferences in order to obtain more relevant resultsfor the client.Courses and Educational Resources OntologyMetadata is one of the most prolific fields in the LTstandardisation process. Almost all the institutions andorganizations involved in this process have made their ownproposals in this field. Currently, the Learning ObjectMetadata [18] model, developed jointly by several of theinstitutions involved in this process, is already an officialstandard of the IEEE. This standard, and in particular its RDFbinding, developed by Nilsson et al. [19], has been used as thebasis for the sub-ontology of ELEARNING-ONT that includesthe needed classes and properties to characterize academiccourses.This ontology is composed of 10 namespaces that groupclasses and properties related to a particular feature of thecourses: lom-base (which includes general classes used in theother namespaces), lom-general (with properties that allowspecifying the aggregation level and the type of structure ofthe educational resources), lom-lifecycle (with classes andproperties for the description of the resource life cycle), lommetametadata(that contains classes and properties to describethe metadata scheme used), lom-technical (which referencesthe technical requirements for the execution of the educationalresource), lom-educational (this is the most importantnamespace. It defines classes and properties which describepedagogical aspects of the resources), lom-rights (referringbasically to the costs and legal restrictions of the educationalresource), lom-relation (it contains a only property,isBasisFor), lom-annotation (that will be used add commentabout courses) and finally lom-classification (whose maintarget is to show the classification system in which theresource can be categorized).Educational Services Providers OntologyThis sub-ontology gathers some terms that allow makingdescriptions about educational services providers. These areentities or organizations that deliver courses on-linethroughout a particular e-learning platform. Due to the lack ofstandardized conceptual models in the e-learning domainrelated to this topic, we have taken from the e-commercedomain common use schemes that allow describingenterprises. Particularly, our sub-ontology is based on theEnterprise Ontology [20], developed by the ArtificialIntelligence Applications Institute from the University ofEdinburgh.Educational Platforms OntologyOn-line courses are offered to students throughout e-learningplatforms. We assume that an e-learning platform is a Webapplication that includes Internet tools and services within anenclosed space specifically configured and organized toprovide learning in a convenient and satisfactory way.0-7803-9077-6/05/$20.00 © 2005 IEEE October 19 – 22, 2005, Indianapolis, IN35 th ASEE/IEEE Frontiers in Education ConferenceS3H-21
Session S3Hlip :QCLlip:Activitylip:Preferencelip : Goallip : qcllip:activity lip:preferencelip : goallip:Interestlip:interestPersonlip:competencylip :identificationCompetencylip : Identificationrdfs: subClassOfrdfs:subClassOflip : Namelom-edu:LearingResourceTypelom- edu: InterativityTyperdfs:subClassOfadquireslom-edu: InterativityLevelLearnerlom-edu:learningResourceTypecoveredBylip :Addresslom-edu:interativityTypelom-edu: ContextinteractsWithlom-edu:interativityLevellom-edu:contextlom-edu: AgeRangelom-edu:ageRangeCourserdfs:subClassOfLearningResourcelom-edu:dificultylom- edu:Dificultylom-edu:languagehasSchedulingprovideslom-gen:structurelom- edu: LanguageCalendarimpartedWithEducationalServiceProviderlom-gen:aggregationLevellom-life:statuslom-gen: StructureTypeuseslom- gen :AggregationLevelEducationalPlatformlom-life: StatusTypeFIGURE 3SOME CLASSES AND PROPERTIES IDENTIFIED IN ELEARNING-ONTMany educational platform surveys have been used toelaborate the sub-ontology that allows the characterization ofthese applications and the terms considered to be moreconvenient have been taken from them. The experience of theauthors related to the construction of e-learning platforms hasbeen essential in this field. Mostly, the terms in this subontologyallow defining the available tools in a platform.Other Ontologies and TaxonomiesBesides the mentioned sub-ontologies, some othervocabularies and taxonomies have been used. Among them,we can mention a subset of the Universal DecimalClassification [21] scheme, to use it as a vocabulary of severalof the properties defined in ELEARNIG-ONT. The DAML-Time [22] ontology has also been imported to representtemporal concepts (for example, course calendars). Severalother data models are currently under study, like ontologiesthat let us to describe user’s devices.SUMMARY AND FUTURE WORKSThe Semantic Web is not a separate Web but an extension ofthe current one, in which information is given well-definedmeaning, better enabling computers and people to work incooperation. In this paper, we have outlined the basic issues ofan intermediation system in the e-learning domain that makesuse of the Semantic Web techniques in order to improve thesearching and location processes. This Broker can performqueries in its knowledge base taking into account the userprofile, i.e. the needs of the user and its preferences regardingtime availability, difficulty level, obtained degree, etc.This paper have briefly introduced the underlyingontology that is required for that, introducing several subontologiesabout, for instance, courses and learning objects,on-line service providers, content providers, learners, etc. Thisontology, named ELEARNING-ONT (c.f. Figure 3), providesthe needed semantics to let computers automatically deal withadaptive intermediation in the e-learning domain.Some work is taking place and will be developed in thefuture: Finalization of the prototypes of the architecture0-7803-9077-6/05/$20.00 © 2005 IEEE October 19 – 22, 2005, Indianapolis, IN35 th ASEE/IEEE Frontiers in Education ConferenceS3H-22
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