Knowledge-Based Systems 22 (2009) 292–301

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Knowledge-Based Systems

journal homepage: www.elsevier .com/locate /knosys

Advanced ontology management system for personalised e-Learning

M. Gaeta, F. Orciuoli*, P. RitrovatoDipartimento di Ingegneria dell’Informazione e Matematica Applicata (DIIMA), University of Salerno, Via Ponte Don Melillo, 84084 Fisciano (SA), Italy

a r t i c l e i n f o

Article history:Available online 29 January 2009

Keywords:Knowledge representationKnowledge modelingKnowledge maintenanceKnowledge reuseDistance learning

0950-7051/$ - see front matter � 2009 Elsevier B.V. Adoi:10.1016/j.knosys.2009.01.006

* Corresponding author.E-mail addresses: gaeta@diima.unisa.it (M. Gaeta

Orciuoli), ritrovato@diima.unisa.it (P. Ritrovato).

a b s t r a c t

The use of ontologies to model the knowledge of specific domains represents a key aspect for the integra-tion of information coming from different sources, for supporting collaboration within virtual communi-ties, for improving information retrieval, and more generally, it is important for reasoning on availableknowledge. In the e-Learning field, ontologies can be used to model educational domains and to build, orga-nize and update specific learning resources (i.e. learning objects, learner profiles, learning paths, etc.). Oneof the main problems of educational domains modeling is the lacking of expertise in the knowledge engi-neering field by the e-Learning actors. This paper presents an integrated approach to manage the life-cycleof ontologies, used to define personalised e-Learning experiences supporting blended learning activities,without any specific expertise in knowledge engineering.

� 2009 Elsevier B.V. All rights reserved.

1. Introduction

Knowledge modeling represents a significant activity, that isparticularly difficult to perform due to its complexity. Nowadays,in computer and information science, knowledge representation,reuse and sharing are facilitated by the explicit use of ontologies.In [1], an ontology is an ‘‘explicit specification of a conceptualiza-tion”. The term is borrowed from philosophy, where an ontologyis a systematic account of existence. For artificial intelligence sys-tems, what exists is what can be represented. Pragmatically, acommon ontology defines the vocabulary with which queries andassertions are exchanged among agents. Agents can be both soft-ware agents and/or human agents.

Recently, we have seen an explosion of interest in ontologies asartifacts to represent human knowledge and as critical compo-nents in knowledge management, Semantic Web, business-to-business applications, and several other application’s areas. Alsoin the e-Learning area there is a newly great interest in the exploi-tation of knowledge technologies.

Most of the current learning technology specifications are basedon educational metadata: IEEE LOM [2] and ADL SCORM [3], forexample, are the standards proposed for describing (and re-using)chunks of learning content annotated through metadata. Metadatais supposed to enable the reuse of these chunks by detailing theconditions of their initial deployment. However, the authors of[5,4] pointed out that such an approach failed to elicit cognitive

ll rights reserved.

), orciuoli@diima.unisa.it (F.

behaviours and therefore the actual reuse. We believe that theuse of ontologies in e-Learning can overcome these drawbacks.

In [6], some benefits from applying ontologies to e-Learning arewell explained. The authors asserts that an ontology, formally anddeclaratively, represents the terminology of a specific domain, defin-ing its essential knowledge. Ontologies are used to support semanticsearch, making possible to query multiple repositories and discoverassociations, between learning objects, that are not directly under-standable. This is impossible or very complex with simple keyword-or metadata-based search supported by the current standards.

In this paper, we describe methodologies and techniques forsupporting a community of experts in modeling educational do-mains (e.g. mathematics domain, English literature domain, etc.)through the management of convenient educational ontologiesnamely e-Learning ontologies and exploiting them in order to de-fine and execute personalised e-Learning experiences withinblended learning activities.

Blended learning is considered a learning approach defined bythe effective combination of different modes of delivery and mod-els of teaching and styles of learning [7]. Personalised e-Learningexperiences represent a convenient way to complement face-to-face sessions within a whole blended learning experience. A per-sonalised e-Learning experience could be very important whenused for assessing the knowledge acquired by each individual lear-ner during a face-to-face learning session and offering, in case ofnegative results, personalised remedial works able to fill the iden-tified knowledge gaps with learning paths that best fits the needs,the cognitive state and the learning preferences of each individuallearner. In the event that the personalised e-Learning experiencecan be built, packaged and deployed with an automatic process,

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then the whole blended learning activity can become more effec-tive and efficient.

Anyway, knowledge modeling through ontologies is a subjec-tive process essentially, whereby different people that model thesame domain, produce, in most cases, different ontologies depend-ing on their sensitivity, their background, etc. In a distributed envi-ronment, such as communities of experts, harmonizing the work ofall parties can be a relevant activity, in order to enrich and improvethe available knowledge bases. For these reasons, we define a set ofconvenient techniques for versioning and harmonization of e-Learning ontologies. The current methodologies, developed in thee-Learning domain do not allow the integrated management ofknowledge that meets all the requirements above mentioned.The proposed approach is conceived to allow the collaborativeand shared management of the available knowledge, without hav-ing any specific proficiency in knowledge engineering taking intoaccount aspects like ontology harmonization and ontology version-ing. In particular, we focus on the collaboration approaches forontology building and maintenance. More precisely, the most rele-vant contributions of our work are:

� The definition of convenient models to represent and exploit e-Learning ontologies in order to build and deliver personalised e-Learning experiences taking into account different cognitivestates and learning preferences of learners.

� The definition of a set of tools for representing and managing e-Learning ontologies, within a community of teachers, tutors,mentors, etc. (without any expertise of knowledge engineering)through the features of an integrated framework;

The paper is organized as follows: Section 2 presents some re-lated works; in Section 3 we describe our approach to build per-sonalised e-Learning experiences through the use of ontologies;in Section 4 we describe the algorithms and the techniques to man-age the life-cycle of e-Learning ontologies through our advancedontology management system; in Section 5 a case study of anAOMS collaborative ontology construction session is presented. Fi-nally, Section 6 concludes the work.

2. Background and related works

The importance of the knowledge modeling, to effectively orga-nize the available e-Learning resources according to the particularneeds of both teachers and students, has been advocated by manyauthors. Domain knowledge modeling is the basis for constructingdomain concept structures and managing related course materials.Brase and Nejdl [8] have showed the increasing importance, in thee-Learning field, of knowledge modeling through metadata defini-tion standards such as the learning objects metadata (LOM). How-ever, these standards introduce the problem of incompatibilitybetween disparate and heterogeneous metadata descriptionsacross domains, which might be avoided by using ontology as aconceptual backbone in an e-Learning scenario [9,6].

Various researchers (see for example [12]) have observed thate-Learning, even when properly designed and meta-tagged, willnot realize full re-usability without the full benefits from theSemantic Web [10]. A number of systems have been developedto manage learning resources through the use of Semantic Webtechnologies.

A first example of these systems is the Edutella project [11]. It isan open source project based on RDF metadata, for P2P networkusers interested in the exchange of learning resources.

KGTutor, a knowledge grid based intelligent tutoring system[15], proposes a model for the construction of intelligent tutoringexperiences in a more pleasant and effective way. The KGTutor is

designed to provide better support to student centered distributedlearning. Students’ characteristics, such as previous knowledgeand learning styles, are used to choose, organize, and deliver thelearning materials to individual students. During the learning pro-gress, the system can also provide objective evaluations and cus-tomized suggestions for each student according to their learningperformance.

Another representative system that uses Semantic Web tech-niques in an e-Learning environment is the Courseware WatchDog[14]. It is a module of the larger project PADLR (personalised accessto distributed learning repositories) [13] based on a peer-to-peerapproach to support personalised access to e-Learning resource.The purpose of this framework was the creation of a softwarearchitecture helping teaching staff and students in the search andmanagement of learning objects. WatchDog is completely ontol-ogy-based and uses clustering techniques to create personalised‘views’ of the learning objects. Moreover, it has some techniquesfor the management of the evolution of ontologies related to theeducational content.

These works show that the most relevant difficulty in theknowledge modeling for e-Learning is related to the creation andmaintenance of Semantic Web structures (such as ontologies)which can be exploited not only to organize learning objects andto state their inter-relationship but also to build personalisedlearning paths and to maintain up to date students cognitive states.In particular, in the e-Learning area, there is still a lack of method-ologies and techniques that allow the effective management ofontologies in particular addressing the most actual ontology re-search issues such as Ontology Versioning, Ontology Harmonizationand Collaborative Ontology Construction.

The issue of ontology versioning is related to the nature ofontologies that are not static piece of knowledge, but evolve overtime. Changes in domain’s concepts, adaptations to different tasks,or changes in the conceptualization require modifications of theontology. Ontology Versioning is the management of the changeswhich can occur to the ontologies in their life-cycle, more pre-cisely, it is the ability to manage ontology changes and their effectsby creating and maintaining different variants of the ontology. Theevolution of ontologies can cause interoperability problems, there-fore it is important to track changes that may occur to the ontolo-gies because of: (i) changes in the domain, (ii) changes in theshared conceptualizations or (iii) changes in the specifications[16,17]. Ontology Harmonization, instead, is the ability to harmo-nize two or more ontologies in a unique ontology in order to enrichthe available knowledge base. It is strictly related to two main is-sues such as ontology matching [18] for the recognition of corre-spondences between ontologies and ontology merging [19] forthe actual fusion of those ontologies. Ontology Versioning and Har-monization are still open problems in the Semantic Web area, dueto the complexity of ontology management. Our approach does notwant to solve all these problems, but it is focused on the definitionof a set of methodology that can be used to effectively manageontologies in the e-Learning domain. In [20], the authors presenta consistent methodology for the Collaborative Ontology Construc-tion articulated in four different phases: preparation, anchoring,iterative improvement and application. Furthermore, in [29] isillustrated a report about detailed interviews with members often different ontology engineering projects. Interviews were con-ducted either on the phone or in person. Analyzing the report, itemerges the lack and the request for user-friendly tools able tosupport collaborative ontology construction orchestrating single-user asynchronous tasks.

Usually, two main collaboration modes [22] are adopted byontology editing tools to support collaborative ontology develop-ment. The first one is the model where everyone accesses the sameversion of an ontology and changes are immediately visible to

Fig. 1. Semantics of main relations. (a) Semantics of HasPart. (b) Semantics ofIsRequiredBy.

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everyone (synchronous mode). In the second one, users can havetheir personal sandbox space and integrate their changes with themaster version later (asynchronous mode). Several variants andmixed approaches are also used by existing ontology managementtools.

The well known Protégé [21] tool adopts a client-server archi-tecture allowing users to concurrently apply changes on a singleontology. Changes made by a user are immediately visible to theother users. APECKS [24] is mainly thought in order to supportthe collaborative analysis and definition of domain ontologies,meanwhile Tadzebao [25] supports the collaborative developmentenabling users interaction through texts, images and handwritingdesigns exchange. Some tools, like OntoRama [23], use a collabora-tive P2P network instead of a client-server system. Recently a newapproach, based on Wiki engines [27], comes to age. Once few edit-ing rules are given to authors, it is possible to obtain Wikis that areeasily transformable into ontologies and vice-versa. Ontowiki [35]and Semantic MediaWiki [28] are some collaborative wiki-basedontology development tools.

Our idea consists in defining a technique for collaborative ontol-ogy construction based on principles of the methodology pre-sented in [20], that takes care of the lacks and needs emphasizedin the interviews of [29], that aims at overcoming the knowledgeengineering expertise needed by the existent tools and that ex-ploits the typical human–computer interactions provided by Web2.0 applications [32].

3. Personalised e-Learning based on ontologies

In the following subsections, we focus on how to model educa-tional domains in a machine-understandable way using ontologiesand consequently, how these structures can be exploited in orderto personalize e-Learning experiences using information comingfrom learner profiles and content coming from learning objectrepositories (in which learning object are annotated with semanticinformation through standard metadata schema). For the sake ofsimplicity we will consider that e-Learning experiences are repre-sented by sequences of learning objects, but the approach is alsosuitable when e-Learning experiences are made up of complexflows of learning activities. Therefore, the main principles of ourapproach are: (i) modeling of educational domains by means ofe-Learning ontologies; (ii) modeling of learner cognitive state andpreferences (Student Model); (iii) annotation of Learning Objectswith metadata and semantics connections between learning ob-jects and ontologies elements (learning object model) and (iv)modeling of e-Learning experiences (e-Learning experiencemodel).

3.1. Modeling of educational domains

An e-Learning ontology can be modeled with a graph in whichnodes are relevant concepts (arguments, topics, etc.) within theeducational domain of interest and edges are binary relations be-tween two concepts. Our approach mainly foresees three kinds ofrelations: HasPart (in brief HP) that is an inclusion relation, IsRe-quiredBy (in brief IRB) that is an order relation and SuggestedOrder(in brief SO) that is a ‘‘weak” order relation. The restricted set ofrelations does not imply limits to the knowledge representationbut it is a convenient method to improve the computational com-plexity of algorithms that have to navigate and process the graph.Let’s illustrate how to build an e-Learning ontology. Suppose wehave to model the educational domain D, so we try to conceptual-ize the knowledge underlying D and find a set of terms represent-ing relevant concepts in D. The result of the previous step is the listof terms T ¼ C;C1;C2;C3, where T is one of the plausible conceptu-

alizations of D. In order to explain the semantics of HasPart relationwe can refer to the ontology illustrated in Fig. 1 where the threeHasPart relations HasPartðC;C1Þ, HasPartðC;C2Þ and HasPartðC;C3Þmeans, in terms of e-Learning, that in order to learn concept Clearners have to learn concepts C1, C2 and C3 without consideringa specific order.

In Fig. 1a, we note the existence of elements that are not con-cepts nor relations. The new elements to introduce are the learningobjects LO1, LO2 and LO3. You can interpret the connection betweena concept and a learning object, for instance C1 and LO1, as a Has-Resource (in brief HR) relation. The relation HasResourceðC1; LO1Þmeans that the educational content packaged in learning objectLO1 explains concept C1. So, if we assume that our learning objec-tive is C1 then the correspondent assembled e-Learning experienceis composed only of ½LO1�, otherwise if the learning objective is Cthen the assembled e-Learning experience will be composed ofone of the plausible permutation of ½LO1; LO2; LO3�.

Now, consider the ontology shown in Fig. 1b.This ontologypresents two IsRequiredBy relations, IsRequiredByðC1;C2Þ andIsRequiredByðC2;C3Þ. The two relations mean that C1 has to benecessarily learned before C2 and C2 has to be necessarily learnedbefore C3. In this case if C is the learning objective, learners haveto learn the ordered sequence of concepts ½C1;C2;C3� and corre-spondingly they can join the e-Learning experience assembledby the ordered sequence of Learning Objects ½LO1; LO2; LO3�. Alter-native permutations like ½C2;C1;C3� will be invalid. The sequenceof concepts useful to reach a pointed learning objective is calledLearning Path, meanwhile the operation used to construct theconcrete e-Learning experience assembling learning objects se-quence is called Resource Binding.

Above, we have outlined the foundations of our modeling ap-proach, now we want to refine the approach description. First ofall, we state that the same learning object can explain more thanone concept within the same ontology. In general, the relationHasResource can be represented by the functionHasResourceðC1;C2; . . . ;Cn; LO1Þ meaning that LO1 explains all con-cepts C1, C2, . . ., Cn. Otherwise, it is possible to have more thanone learning object explaining the same concept. In general, wecan have at the same time the relations HasResourceðC1; LO1Þ,HasResourceðC1; LO2Þ, HasResourceðC1; LO3Þ, etc.

Lastly, suppose you have a SuggestedOrder relation betweenconcept C1 and concept C2 that is SuggestedOrderðC1;C2Þ, this rela-tion states that the modeler thinks that is preferable to explainconcept C1 before concept C2, but this is not mandatory.

3.2. e-Learning ontologies representation

In order to foster knowledge sharing and systems interoperabil-ity, we need to represent e-Learning ontologies in a widely ap-proved formal language, that is OWL [42]. First of all, you have todefine only one base class called Concept and three binary rela-

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tions (as Object Properties), from class Concept to class Concept,called HasPart, IsRequiredBy and SuggestedOrder. Now, the baseschema is ready and you have to populate the ontology with in-stances (individuals) of class Concept and establish the relationsbetween them.

Though you can use a knowledge representation tool like Proté-gé [21] in order to be supported in writing OWL ontologies we havedefined an ontology editing tool (See Section 4.1) that does not re-quire specific knowledge engineering skills. In the next section, wewill show how e-Learning ontologies can be exploited, togetherwith other information, in order to build personalised e-Learningexperiences through a semi-automatic process.

3.3. Personalised e-Learning experiences

Before describing the steps performed by the process for build-ing personalised e-Learning experiences, we must introduce thethree models which the process relies on.

The Student Model is composed by a cognitive state and a pref-erences state. Cognitive state represents the knowledge reached bya learner at a given time and is composed of a list of concepts eachwith an associated score (values between 0 and 1). If the score for agiven concept is greater then a fixed threshold then that concept isconsidered as known by the learner. Preferences state, for a givenstudent, is a set of couples ðproperty; valueÞ used to represent inmachine-understandable way the learning preferences of the stu-dent. For the sake of simplicity, we consider that properties arecontained in the set (Learning Resource Type, Interactivity Level, Typ-ical Learning Time, Difficulty, Language, Context). We assume alsothat there is one preferences state and one cognitive state for eachlearner.

The learning object model foresees that a metadata is associatedto each learning object existing in the learning object repository.We assume that the metadata includes: (a) a list of explained con-cepts, and (b) a list of couples ðproperty; valueÞwhere property is anelement of the same set defined for the Student Model and value isan admissible value for that property. It is important to underlinethat the explained concepts list represents the semantic link (seeHasResource relation in the previous subsection) between a Learn-ing Object and an e-Learning ontology.

In the e-Learning experience model, e-Learning experiences aredefined as: (a) a set of Target Concepts (TC) i.e. the set of high-levelconcepts to be transmitted to the learner (namely Learner Objec-tive in the previous subsection), (b) a Learning Path (LP) i.e. an or-dered sequence of atomic concepts that is necessary to transfer to aparticular learner in order to let him learn TC (given the personal-ization on a particular learner, the sequence does not contain con-cepts already known by that learner) and (c) a Presentation (PR) i.e.an ordered list of learning objects that the learner has to use in or-der to acquire concepts included in the LP. LP can be automaticallyobtained starting from TC, while PR can be automatically con-structed starting from LP and querying (using metadata informa-tion) the Learning Object repository. TC can be settled by theteacher or directly by the learner (in case of self-directed learning)and can be obtained by manually selecting concepts on ontologiesor by selecting pre-defined groups of concepts.

Excluding the selection of TC and other customization parame-ters, the building process is fully automatic and realized throughthe execution of several algorithms. The most important are:Learning Path Generation Algorithm and Presentation GenerationAlgorithm. The first one determines the ordered sequence of atomicconcepts needed to reach a satisfactory knowledge about selectedTC on the basis of a reference e-Learning ontology, a set of TC and agiven cognitive state. The second one selects and orders a set oflearning objects, explaining all the concepts in the Learning Path,that best fits with the student model preferences state. The algo-

rithm acts minimizing the number of learning objects (a singlelearning object could explain more than one concept) useful tocover the whole Learning Path.

The building process is repeated for each student enrolled to thee-Learning experience. Once the e-Learning experience is ready andpackaged, then the enrolled student can start his/her executionphase. The student accesses the Presentation and executes the pro-posed activities studying the sequence of learning objects providedby the system until a milestone (assessment point within the Learn-ing Path) is reached. At this point the student executes the assess-ment phase and sends the results back to the system. Analgorithm, namely Student Model Update Algorithm, is invoked in or-der to update the student’s cognitive state on the basis of latest testresults. The sequence of previously described algorithms is now re-executed considering the evolution of the student’s cognitive state.In this way a personalised remedial work is automatically provided.The on-line learning process ends, for a student, when his/her cog-nitive state includes all concepts of the Learning Path.

4. Advanced ontology management system

The educational domain modeling approach and the exploita-tion of e-Learning ontologies in the personalised e-Learning expe-rience construction process have been implemented by IntelligentWeb Teacher (IWT) platform [30,31]. In this section we describehow the Advanced Ontology Management System (AOMS) hasbeen built to support the IWT platform in order to improve themanagement capabilities of its knowledge base (e-Learning ontol-ogies). Once an ontology has been constructed and validated,through the AOMS tools, it is annotated with metadata, indexedand archived. IWT teachers can look up AOMS Repository for thedesired ontology and import it in IWT. The ontology exchange for-mat is OWL that also fosters the interoperability of AOMS withthird party knowledge representation tools like, for instance,Protégé.

More precisely, we have placed the focus on a set of tools sup-porting ontology editing and collaborative ontology constructionthat can be used by domain experts (teachers or, in general, per-sons with special knowledge or skills in a specific matter like Phys-ics, Italian art, Economics, etc.) with few (or any) competencies inknowledge engineering. Therefore, domain experts have not tointeract with complex knowledge engineering environments butwith user-friendly visual interfaces, such as the visual ontologyeditor (VOE). The most important AOMS tool is the coordinationand cooperation tool (CCT) useful for the collaborative ontologyconstruction. With a collaborative approach, the ontology con-struction will become an effort reflecting experiences, competen-cies and viewpoints of several persons (participants). Thetangible results of the collaboration is to obtain a richer and morereliable represented knowledge thanks to the collectiveintelligence.

In the end, the AOMS includes other two main components:Ontology Merging Tool and the Semantic Wiki Engine, respectivelyused in order to aid the users to harmonize two or more ontologiesmodeling the same educational domain and to implements theconsensus about a collaboratively constructed ontology.

4.1. Visual ontology editor

Most of the actors moving around e-Learning do not want to beengaged with formalisms that, though expressively powerful, arefar from their habits and their common activities. Therefore, weconstruct an abstraction layer atop of OWL. The abstraction layermaps the e-Learning ontology model, defined in Section 3.1, onthe OWL constructs and offers the user a simple visual language,

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that can be used to graphically design e-Learning ontologies. TheAOMS Visual Ontology Editor (VOE) provides a graphical web-accessible environment (Fig. 2) enabling the use of the visual lan-guage. Domain experts can define a new concept only by drag ovalsinto the design panel and create relations between concepts bydrawing lines between two ovals. The tool enables users to addmetadata for the whole ontology that can be stored in AOMSRepository and subsequently searched and retrieved by the meta-data-based search engine. Within the context of AOMS, ontologiesare stored using an internal representation format, but when theyhave to be exported towards IWT, they are translated in OWL. Theidea is to exploit advantages of OWL (e.g. interoperability, etc.) hid-ing its complexity to the ontology designers that can reason only interms of graphical representations. Third party editors and reason-ers can easily work after the conversion in OWL format.

An extension has been supplied to the VOE: The ontology evo-lution tracker sub-system. Ontologies are not static and mayevolve over time. In our approach, the changes applied to an ontol-ogy have impact mainly on learning objects linked to the ontolo-gies through metadata, and on students’ cognitive states thatstore all concepts known by each learners. Therefore, when anontology evolves, and a new version of that ontology is adopted,several inconsistencies may appear into the system. The ontologyevolution tracker sub-system listens to all update operations exe-cuted on the ontology by means of the VOE, and for each operation,a set of structured information are built, indexed and stored. Theseinformation help us to reconstruct the ‘‘history” of e-Learningontologies and to resolve inconsistencies with the learning re-sources (i.e. learning profiles and learning objects) depending onthem. For instance, thanks to the Ontology Evolution TrackerSub-system, it is possible to re-align (in a semi-automatic way)

Fig. 2. AOMS visual

Learner Profiles after the adoption of a new version of the referenceontology.

4.2. Coordination and cooperation tool

The AOMS coordination and cooperation tool (CCT) is realizedas an extension of an open source platform named GENESIS [33],coming from the homonymous project funded by the EuropeanCommission. The coordination aspects are supported by a Work-flow Engine and an Artifacts Management Module. GENESIS hasbeen specialized for software development processes but is real-ized as a generic platform for the definition and the execution ofcoordination and cooperation processes that can be extended in or-der to support collaborative ontology construction processes.

The collaboration processes executed through CCT functional-ities can be classified in Sequential and Parallel. Sequential pro-cesses contemplate that work is completed and then handed offfrom a participant to another participant. Thus, each participantis guided by what has been done before. A first release of the workresult is provided by the coordinator that states also the guidelinefor the other participants. This approach has more benefits if theprocess starts from a complete draft of the result to be achievedand it needs to obtain an enriched artifact exploiting the collectiveintelligence expresses by a group of persons. Parallel processesforesee the presence of a coordinator in order to stimulate the par-ticipants and put together all the stuffs coming from each memberof the collaboration group. Each participant yields separately itsown artifact meeting the guidelines stated by the coordinator.The coordinator harmonizes all the received artifacts into a com-monly approved schema. This approach is particularly advanta-geous when the coordinator divides the whole work into parts

ontology editor.

Fig. 3. Workflow schemas for sequential (a) and parallel (b) processes.

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with few points of overlapping, assigns each part to a single partic-ipant and, lastly, collects and assembles all the partial results in acomplete result. Workflow schemas representing the sequentialmode collaborative ontology construction process and the parallelmode collaborative ontology construction process are depicted inFig. 3 where the four new human-activity types have this meaning:

� Start-up Activity: this activity is needed in order to edit a firstdraft of the ontology used as an initial version to guide the col-laboration process.

� RefinementActivity: this activity is used to modify a previous ver-sion of an ontology in order to obtain a more refined version tobe stored into Artifacts Management Module. This activitydefines a new version of the ontology.

� Harmonization Activity: this activity is executed when there isthe necessity to harmonize two or more ontologies representingframes of the same domain. The harmonization task is per-formed by a semi-automatic Ontology Merging Tool that sup-ports the user in the merging process. The result of thisprocess can be refined by users using the Visual Ontology Editor.

� Validation Activity: this activity is executed in order to check theconsensus of all participants to the collaboration process and torealize the last concurrent refinements. A specific version of anontology is retrieved from the document repository and trans-formed into a Wiki space where ontology concepts are mappedas Wiki pages and relations are mapped as links between pagesupdatable by all participants. A special user (coordinator) mod-erates the collaboration activity (he/she could perform a roll-back to cancel any modification to the Wiki space) and decideswhen the activity is finished.

The behavior of the CCT handling all the activity types is de-scribed using collaboration diagrams in Fig. 4. The CCT can bestraightforwardly used in order to apply the methodology definedin [20] through the implementation of its four phases:

� Preparation: a domain expert (the coordinator) assembles a com-munity of other domain experts. The community has the aim ofbuilding an e-Learning ontology, modeling a specific educationaldomain. The coordinator defines collaboration objectives (e.g.building a Physics Ontology, etc.), constraints (e.g. deadline,etc.), guidelines (e.g. modeling rules, tools tutorial, etc.) andnotifies them to the community participants that could agreeor not. The coordinator, exploiting the process definition fea-

tures of CCT, selects the workflow schema (sequential or paral-lel) for collaboration tasks, customizes it in order to prepare aRefinement Activity for each participant. The workflow schemawill be executed using the process execution features of CCT.

� Anchoring: the coordinator (both in sequential and parallelmode) provides an initial ontology version executing a Start-up Activity. The coordinator could exploit the reuse of an exist-ing ontology (coming from the Artifacts Management Module,AOMS Repository or from external knowledge representationtools or archives) or edit the seed ontology from scratch.

� Iterative improvement: after the end of the Start-up Activity, thecontrol passes to CCT that follows the selected workflow schemain order to start the first Refinement Activity (for sequentialschema) or the set of parallel Refinement Activities (for parallelschema). CCT continues to execute the process guided by theworkflow schema and events coming from users. In the case ofsequential mode, the latest activity (before the Validation Activ-ity) is represented by a Refinement Activity that provides anontology version, obtained by the execution of all refinementsteps, ready to be validated by all participants. The latest Refine-ment Activity of the sequential workflow schema can beassigned, by coordinator, to a participant or to himself/herselfduring the Preparation phase. In the case of parallel mode, thelatest activity (before the Validation Activity) is represented byan Harmonization Activity in which the coordinator can mergeall the results coming from previous parallel Refinement Activi-ties. The Validation Activity is used to achieve the consensus ofall participants about the e-Learning ontology obtained as arti-fact of the collaboration process. During the Validation Activitythe participants can concurrently apply changes to the ontologyand discuss about it. All changes can be rolled back from thecoordinator that decides also when the activity ends. The ontol-ogy resulted by the Validation Activity is the official artifact ofthe whole collaborative ontology construction process.

� Application: the collaboratively produced ontology is, now,stored in the AOMS Repository and can be imported andexploited in order to produce a personalised e-Learningexperience.

4.3. Ontology merging tool

The Ontology Merging Tool is based on a merging algorithm con-sisting of four steps. The first step simply consists of the selection

Fig. 4. (a) Collaboration diagram for Start-up Activity. (b) Collaboration diagram for refinement activity. (c) Collaboration diagram for Harmonization Activity. (d)Collaboration diagram for Validation Activity.

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(by the user) of two source ontologies. The second step is repre-sented by the matching phase in which couples of concepts, belong-ing to the two ontologies, with a high similarity level (both lexicaland semantical) are identified. A match is identified when twoterms represent the same concept in two different ontologies. Asmentioned above, the computation of similarity will be transparentto the user. The matching algorithm we proposed is based on theintegration of different techniques that are suitable to be appliedfor e-Learning ontologies and without any pre-defined documentcorpus. In particular, we use:

� String-based technique: it is a similarity computation applied toall possible pairs of concepts ðC1;C2Þ, C1 belonging to an ontol-ogy source O1 and C2 belonging to an ontology source O2. Simi-larity Metrics like q-Gram and Levenshtein Distance [34] andtypical operations of Text Processing like Stemming are appliedin order to establish a first set of matches between the twoontologies.

� Graph analysis: graph analysis techniques [37] are applied to thetwo ontologies, taking into account also the first set of matches,in order to enrich the set using information coming from thestructures of the two ontologies.

� Semantic analysis: Normalized Google Distance [40] and Wikepe-dia [41] are used as linguistic resources in order to refine the setof matches using semantic similarity information.

In the third step, starting from the set of matches, the algorithmgenerates a list of suggestions, containing the operations that can

be applied to perform the ontology merging. In the fourth (and fi-nal) step, suggested operations are executed and, moreover, thealgorithm determines and resolves redundancies (for relationsand concepts) that could be generated. The Ontology Merging Toolis fully interactive because we believe that a completely automaticprocess is very difficult to obtain and it would not be very effective.So, each step of the merging algorithm can be validate by the usersthat, eventually, can manipulate the step results following theideas proposed with PROMPT [37].

4.4. Semantic wiki engine

In order to reach a common consensus about the validity of an e-Learning ontology we need to have an open collaboratively envi-ronment where persons, can effectively discuss, cooperate and val-idate an ontology using tools that follow the approach proposed in[36]. The AOMS Semantic Wiki Engine (SWE) is implemented byextending an open source Wiki Engine namely ScrewTurn [38].SWE manages semantic wiki spaces (SWS). A SWS represents ane-Learning ontology, a page in SWS represents a concept of the edu-cational domain of interest and, lastly, a link between two pagesrepresents a relation between two concepts. The link mechanismin SWE is quite different from that of other semantic wiki engines[26]. In fact, our approach foresees the presence of typed links be-tween pages in order to map correctly the different kinds of rela-tions (e.g. HasPart, IsRequiredBy and SuggestedOrder) comingfrom e-Learning ontologies model. In this way, a SWS is exactly anew representation of an e-Learning ontology that is an alternative

M. Gaeta et al. / Knowledge-Based Systems 22 (2009) 292–301 299

to the graphical representation provided by the VOE. The mainadvantage of the SWE with respect to VOE is that SWE is multi-user.The multi-user feature enables more than one domain expert to up-date concurrently the same version of an ontology. An effective col-laborative work is ensured by a tracking feature that stores allinformation about updates to the SWS in order to enable the coor-dinator to perform roll-back operations. In the context of AOMS, theSWE is used to implement the Validation Activity handled by theCCT. The e-Learning ontology, coming from the previous task inthe collaboration process, is converted into a SWE where the con-sensus can be reached by all participants driven by the coordinator.When the Validation Activity comes to end, the updated SWE isconverted into an e-Learning ontology (AOMS XML-based internalrepresentation), annotated with metadata, indexed and storedand available for IWT users.

5. Case study: C-programming ontology

We have tested the aforementioned methodologies and toolsin The Faculty of Engineering at University of Salerno. ProfessorM. Gaeta taught Fondamenti di Informatica (Computer Program-ming Foundations) for the class 2006/2007 of ElectronicsEngineering. The course programme includes a module aboutC-programming, so professor M. Gaeta, asked his three laboratoryassistants to participate to the collaborative construction of theC-programming ontology. Professor M. Gaeta and his assistantsused AOMS to collaborate to the ontology building in order to de-fine and publish the personalised e-Learning experience on Intel-ligent Web Teacher, that is still the e-Learning platform adoptedby the Faculty of Engineering at University of Salerno. In orderto simplify the description and the graphical representation ofontologies fragments within this work we will show only top lev-els of ontology. Professor M. Gaeta chooses to set up a collabora-tive environment able to support a parallel process. Then, hedraws a seed ontology using the VOE within the Start-up Activity.The seed ontology has only four concepts representing a firstcoarse-grained conceptualization of the C-Programming domain:Programmazione_C (C_Programming), Principi_basilari(Basic_principles), Costrutti (Constructs) and Tipi_dati_strutturati(Strctured_data_types). More in details, the seed ontology pre-sents the following relations:

� HasPart(Programmazione_C, Principi_basilari)� HasPart(Programmazione_C, Costrutti)� HasPart(Programmazione_C, Tipi_dati_strutturati)

Once the seed ontology is ready, the Professor signs the Start-upActivity as finished. In this way, the seed ontology is stored into theArtifacts Management Module and the CCT can activate the threerequired Refinement Activities that will be managed by the labora-tory assistants of the Professor. In particular, the first assistant hasto refine the Principi_basilari concept, the second assistant has toexplode the Costrutti concept and the third assistant has to detailTipi_dati_strutturati concept. Once received his task specifications,the first assistant starts his Refinement Activity and accesses tothe VOE that automatically loads the seed ontology. He designsand stores the ontology as version 1.1 (Fig. 5) that is obtained byrefining the seed ontology and in particular by exploding the Prin-cipi_basilari concept.

Once, the ontology has been stored, the first assistant closesthe own Refinement Activity. Concurrently, second assistant en-gages his Refinement Activity, with the objective of explodingCostrutti concept, and produces the version 1.2 starting from pro-fessor Gaeta’s seed ontology. Even the third assistant has carriedout his task. He refines the Tipi_dati_strutturati concept on the

seed ontology and builds the version 1.3. Once all parallel Refine-ment Activities have been closed, the Harmonization Activity canstart. Professor Gaeta starts the Harmonization Activity and sup-ported by the AOMS coordination and cooperation tool, he har-monizes versions 1.1, 1.2 and 1.3 executing two differentmerging sessions. In the first session, he uses the Ontology Merg-ing Tool to merge the first assistant’s ontology with the secondassistant’s ontology. During a first matching phase, the tool iden-tifies the following matches:

Version 1.1

Version 1.2

Principi_basilari(Basic_principles)

Principi_basilari(Basic_principles)

Funzioni (Functions)

Funzioni (Functions) Operatori_aritmetici(Arithmetics_operators)

Operatori_aritmetici(Arithmetics_operators)

Costrutti (Constructs)

Costrutti (Constructs) Tipi_dati_strutturati(Structured_data_types)

Tipi_dati_strutturati(Structured_data_types)

Dichiarazione (Declaration)

Dichiarazione (Declaration) Invocazione (Invocation) Invocazione (Invocation) Parametri (Parameters) Passaggio_parametri

(Parameters_passing)

Programmazione_C(C_Programming)

Programmazione_C(C_Programming)

The second matching phase, that considers Graph Analysis andNormalized Google Distance, discovers a further match: Implementaz-ione (Implementation) in version 1.1 matches with Definizione (Def-inition) in version 1.2. The term Definizione, in this case, is used as asynonymous of Codifica (in english Coding). The result of the mergingoperation, applied on versions 1.1 and 1.2, is temporary harmonizedontology. The main structure of the seed ontology is unchangedwhile the refinements for Principi_basilari concept (coming fromversion 1.1) and Costrutti (coming from version 1.2) are added assub-graphs of the seed ontology. The overlapping zones of versions1.1 and 1.2 are harmonized with respect to the identified matches.Now, it needs to include version 1.3 into the harmonization process.So, professor Gaeta uses the Ontology Merging Tool on the tempo-rary harmonized ontology and version 1.3. In a first phase, thematching algorithm discovers the following matches:

Temporary

Version 1.3

Programmazione_C(C_Programming)

Programmazione_C(C_Programming)

Principi_basilari(Basic_principles)

Principi_basilari(Basic_principles)

Costrutti (Constructs)

Cotrutti (Constructs) Tipi_dati_strutturati(Structured_data_types)

Tipi_dati_strutturati(Structured_data_types)

Tipi (Types)

Tipi_dati (Data_types) Puntatori (Pointers) Puntatore (Pointer)

No other matches are deduced by applying Graph Analysis andNormalized Google Distance algorithms. The merging algorithm,using the identified matches, produces the result stored as ver-sion 2.0, the Harmonization Activity ends. A last step is requiredin order to obtain a fully validated C-Programming ontology: allparticipants have to give their consensus to the harmonized re-sult. For this sake, professor Gaeta loads version 2.0 into a Seman-tic Wiki Space (predisposed trough the Semantic Wiki Engine)

Fig. 5. The result of the first Refinement Activity.

300 M. Gaeta et al. / Knowledge-Based Systems 22 (2009) 292–301

and notifies to his three assistants the possibility to access theWiki in order to approve or reject the elements of the referenceontology (version 2.0). The Professor fixes a deadline of threedays. During the validation phase the four participants discoverthe necessity to define two additional ( SuggestedOrder) relationsbetween:

� Array and Costrutto_struct (Struct_statement)� Passaggio_per_valore (Passing_by_value) and Passaggio_per_riferi-

mento (Passing_by_reference)

The final C-Programming ontology (version 2.1) is ready to beimported in IWT in order to support personalised e-Learningexperiences.

6. Conclusion

In this work we have proposed an approach for the educationaldomain modeling through the use of ontologies. We have de-scribed in details how to construct e-Learning ontologies andhow they are exploited in order to define and execute personalisede-Learning experiences. The personalization allows to executemore efficient and effective e-Learning processes. The approach isfully implemented into a commercial e-Learning platform, namelyIWT. Further improvements have been thought, designed and pro-totyped in the context of ELeGI UE Integrated Project [39] from thedelivery infrastructure (based on Grid Technologies) and the peda-gogical (several educational methods are implemented in order toenrich the IWT e-Learning experiences) points of view. The neces-sity to improve knowledge management aspects, with more fea-tures, tools and methodologies, comes from the results ofnumerous experimentation cases carried out using the IWT plat-form. Therefore, investigating the state of the art of SemanticWeb, with respect to ontology management, and experimentingexisting software tools we have matured the following ideas:

� We cannot force the e-Learning actors to have knowledge engi-neering competencies and use complex existing tools. Then, avisual, drag and drop based, and user-centric ontology editingtechnique has been defined.

� If we want to really exploit the potentiality of personalised e-Learning, also within blended learning processes, we have tohelp the e-Learning actors to effectively reuse knowledge andmanage inconsistencies due to the knowledge natural evolution.Then, ontology harmonization, versioning and changes trackingtechniques have been defined.

� Ontology development is often a collaborative process. Further-more, it can be very difficult to use these tools without compe-tencies in knowledge engineering. Then, a collaborativeontology construction technique has been defined. The tech-nique foresees the exploitation of a workflow engine in orderto support the tasks coordination. The technique provides a val-idation phase based on a semantic wiki engine allowing the col-laboration participants to reach a consensus about the finalontology. Without the validation phase there is no assurancethat all participants agree with the final artifacts. Conversely,using only the semantic wiki engine in order to construct theontology does not satisfy the simplified task coordinationrequirement.

Future works will consist of other validation and testing activi-ties in order to improve the potentiality of Semantic Web in the e-Learning area.

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