OTTO-VON-GUERICKE-UNIVERSITÄT MAGDEBURG

FAKULTÄT FÜR INFORMATIKInstitut für Wissens- und Sprachverarbeitung

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Master’s Thesis

Conceptualization of TeachingMaterialBhavani VeeramachaneniOctober 31, 2005

SupervisorsProf. Dr. Dietmar RösnerDipl.-Inf. Manuela Kunze

Otto-von-Guericke-Universität MagdeburgFakultät für InformatikUniversitätsplatz 239106 Magdeburg

Abstract

The Internet and World Wide Web are being used as support aids tofacilitate the delivery of teaching and learning materials. The contentof the related courses taught at different universities and organizationstend to be strikingly similar. The gains resulted by sharing the teachingmaterial are high. The problem here is that most systems use differ-ent formats, languages and vocabularies to represent and to store theseresources. Hence there is no way for two different applications to inter-operate even if their teaching contents belong to the same domain andso the knowledge exposed by one cannot be used by another. A possi-ble solution to the problem of sharing and reuse of learning resources isto have a shared vocabulary. Ontologies provide a shared and commonunderstanding of a domain that can be communicated between peopleand heterogeneous application systems. An important aspect of interop-erability of learning objects is a common format for describing content.

In this thesis, ontologies for the teaching material is developed. Ontolo-gies for the content and metadata of teaching material is developed.Metadata helps people organize, find, and use resources effectively.The IEEE Learning Object Metadata (LOM) was developed to providestructured metadata descriptions of learning resources called LearningObjects in order to enable semantic interoperability among applicationson the e-learning domain. The metadata properties adequate for this ap-plication are used from IEEE LOM. If the applications share the com-mon ontology of teaching material then the teaching material of one ap-plication can be used by another, it also provides intelligent integrationsuch as sharing, searching and reusing information among applications.

Acknowledgements

On this page, I would like to express my gratitude to all those who gaveme the possibility to complete this thesis.

Firstly, thanks to my supervisor Dipl.-Inf. Manuela Kunze who gaveme this topic to work on it. All the inspiration and motivation behindthis work is due to her. She proved to be an ideal supervisor during thethesis. Her deep blue remarks on the first draft helped me a lot to learnand improve my thesis report. The words are simply not enough to ex-press my regards for her. I would like to profusely thank my supervisorProf. Dr. Dietmar Rösner for giving me an opportunity to work underhis esteemed guidance.

Declaration

I herewith declare that I have completed this work by myself and onlywith the help of the stated references.

Bhavani VeeramachaneniMatrikelnummer : 167731Magdeburg, August 17, 2005

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Contents

1 Introduction 7

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . 7

1.2 Scope and Goal of Thesis . . . . . . . . . . . . . . . 10

1.3 Principal Results . . . . . . . . . . . . . . . . . . . . 11

1.4 Organization of Thesis . . . . . . . . . . . . . . . . . 12

2 Learning Objects and Metadata for Annotation of LearningObjects 15

2.1 Learning Objects . . . . . . . . . . . . . . . . . . . . 15

2.1.1 Reusability of Learning Objects . . . . . . . . 16

2.1.2 Factors Effecting Reusability of Learning Objects 17

2.2 Metadata for Annotation of Learning Objects . . . . . 19

2.2.1 Learning Object Metadata Standard (LOM) . . 19

2.2.2 Bloom‘s Taxonomy . . . . . . . . . . . . . . . 22

2.2.3 Summary . . . . . . . . . . . . . . . . . . . . 23

3 Ontologies and Representation 25

3.1 What are Ontologies? . . . . . . . . . . . . . . . . . 25

3.1.1 Ontology Components . . . . . . . . . . . . . 27

3.1.2 Design Criteria and Reasons for Developing On-tology . . . . . . . . . . . . . . . . . . . . . . 29

3.1.3 Application Areas for Ontologies . . . . . . . 30

3.2 Ontologies and Semantic Web . . . . . . . . . . . . . 31

3.2.1 Topic Maps . . . . . . . . . . . . . . . . . . . 32

3.2.2 RDF Schema . . . . . . . . . . . . . . . . . . 36

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3.2.3 OWL . . . . . . . . . . . . . . . . . . . . . . 38

3.2.4 Comparing OWL and Topic Maps . . . . . . . 41

4 Ontology for Teaching Material and Modelling with Protégé 43

4.1 Model of Lecture Material . . . . . . . . . . . . . . . 44

4.2 Model of Exercises . . . . . . . . . . . . . . . . . . . 46

4.3 Modelling with Protégé . . . . . . . . . . . . . . . . 58

5 Conclusion 61

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1 Introduction

Technology extends our abilities to change the world. Internet hasbrought about drastic changes in the way people work, communicateand entertain themselves. It is also poised to bring about paradigm shiftin the way people learn. There are a number of advantages of using theInternet and the Web as a teaching tool. Increasingly, the Internet andWorld Wide Web are being used as support aids to facilitate the deliveryof teaching and learning materials [4]. The Web is becoming the worldvirtual library, where information on any subject is available. This ismore efficient and cost effective compared to traditional classroom en-vironment since students can access learning materials at any time andeven students whose geographical reach have prevented them can nowaccess the learning material. Educational domain is often engaged inmassive and senseless duplication for re-creating the existing teachingmaterials. The content of the related courses taught at different univer-sities and organizations tend to be strikingly similar. The gains resultedby sharing the teaching material are high.

1.1 Motivation

Anyone who has had to create learning materials from scratch knowsjust how labor intensive and time consuming the process can be, evenwith the existence of a detailed course descriptions and lesson plans.This creative process can be made easier by the reuse of existing teach-ing and learning materials [8]. If course content is either partially orcompletely delivered using learning objects, there is a great potentialfor reusing these resources within one organization, and more impor-tantly, between organizations [12]. A learning object is a digital learn-ing resource that facilitates a single learning objective and which maybe reused in a different context. The term digital learning resource canbe defined as a digital resource that has a specified educational purposeor context [40].

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A large number of digital learning resources currently reside on theWeb, located at Web sites in educational institutions and different orga-nizations. Many of these resources like presentations, course outlines,etc. are created to support class room instruction. Most of them do notconform to the definition of learning objects and it is difficult to reusecontent from one learning system to another. It is possible to convertthese learning resources into learning objects by breaking them up intosmaller chunks of content [30].

The problem here is that most systems use different formats, languagesand vocabularies to represent and to store these resources. Hence thereis no way for two different applications to interoperate even if theirteaching contents belong to the same domain and so the knowledge ex-posed by one cannot be used by another. In other words, the ontologicalsupport in applications today is poor, because they have not been de-signed with automatic knowledge sharing and reuse in mind. Industrieshave attained today’s high productivity due to standardization of basiccomponents, say, nuts and bolts. Using standardized basic components,one can easily design their own model by configuring them. Standard-ization mainly provides us with a common vocabulary for understand-ing what have been done to date with less ambiguity [28]. Humans cancommunicate with each other because we have a common platform torely on .We can express our ideas using concepts in the common plat-form.

A possible solution to the problem of sharing and reuse of learning ob-jects is to have a shared vocabulary. Specification of functional com-ponents should be described in terms of shared vocabulary. Ontologyprovides a shared vocabulary, which can be used to model a domain,that is, the type of objects and/or concepts that exist, and their proper-ties and relations [19].

In the context of knowledge sharing, ontology means a specification of aconceptualization [18]. That is, ontology is a description (like a formalspecification of a program) of the concepts and relationships that canexist for an agent or a community of agents. This definition is consis-tent with the usage of ontology as set-of-concept-definitions, but moregeneral. And it is certainly a different sense of the word than its use inphilosophy. Ontology should capture domain knowledge and providea commonly agreed upon and shared understanding of the domain. Itshould make explicit which are objects of the domain, that we can talkabout, what are the relations linking them together and which are theaxioms governing their behavior.

These shareable ontologies merely serve to standardize and provide in-terpretations for the contents and the Web-based teaching material [9].

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In order to make contents of any application machine-understandableand machine-interpretable, it is necessary to annotate the applicationscontent appropriately. This means that content of learning object mustbe semantically marked-up i.e., if computers have to understand andinterpret the teaching materials, the application pages need to have se-mantic tags established on the defined terms for one or more ontologies.These notes enable structured search to be performed though the materi-als formed by learning objects. Traditional Web technologies are basedon syntactic markup; implementation of services like semantic markupis promising on Semantic Web [5].

The Semantic Web is an extension of the current web in which informa-tion is given well-defined meaning, better enabling computers and peo-ple to work in cooperation. The Semantic Web is a current project underthe direction of Tim Berners-Lee of the World Wide Web Consortiumto extend the ability of the World Wide Web by developing standardsand tools that allow meaning to be added to the content of web pages.The goal of the Semantic Web is to create a universal medium for theexchange of data by allowing meaning to be given, using tools and tags,to the content within web pages.

The main task of Semantic Web is "Expressing Meaning". In order toachieve this there are several layers in Semantic Web [24]. The follow-ing layers are the basic ones:

Figure 1.1: Layers inSemantic Web

• XML layer - for representing data.

• RDF layer - for representing the meaning of data.

• Ontology layer - for representing the formal common agreementabout the meaning of data.

• Logic layer - enables intelligent reasoning with meaningful data.

The Semantic Web vision is based on two main ideas: addition of se-mantic markup to information resources on the Web and creation of in-telligent services (agents) capable to understanding and operating withsuch resources at the semantic level. The Semantic Web introduces a

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better semantic interoperability of web resources. Using the SemanticWeb, we can easily find the existing learning material, understand theirdescriptions, locate related materials, etc [14]. In that way, SemanticWeb improves learning object’s reusability. Building the shared ontol-ogy using the Semantic Web tools (XML, RDF, and OWL etc) providesautomation for the web services interoperation [10].

The discovery, management, and exchange of learning objects can beconsiderably simplified by providing standardized information on eachlearning object [29]. This information is called metadata. Metadatais data about data here about learning object, it facilitates the search,evaluation, acquisition, and use of learning objects by learners, instruc-tors, or automated systems. If we have ontologically annotated learningobjects metadata, it helps in finding relevant learning objects. Learningobjects can further enhanced by providing ontology-based knowledgefor their content. Using ontologies as shared vocabulary and seman-tically marking up the content of the learning objects, interoperabilityand reusability can be improved.

So there can be two different kinds of ontologies for learning objects:

• Ontologies that describe learning objects metadata [7].

• Ontologies that describe learning objects content and metadata.

The Learning Object Metadata (LOM) standard provides a set of meta-data elements for describing learning objects: this facilitates findingrelevant learning objects [22]. If we want to use a part of learning ob-ject rather than whole, current approach is to copy and paste in orderto reuse specifically those parts of the documents that are relevant. Butthis approach is tedious and time consuming. But it’s advantageous ifauthors were released from the task of reusing the learning objects man-ually by automating the process as much as possible [14]. Therefore,we need a learning object content format that includes explicit defini-tion of the structure of the learning object. So it’s useful to develop anontology that describes learning objects content.

1.2 Scope and Goal of Thesis

Teaching material can be anything that teachers can use to help learnerslearn. The teaching materials which are dealt here are lecture materialand exercises which are designed for traditional classroom instructionand are available online. In this thesis, ontologies for the content andstructure of the lecture material and exercises are developed. The aimof this thesis is to conceptualize teaching material.

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Conceptualization:It is impossible to represent the real world, or even some part of it,

with all details. To represent some phenomenon or part of the world,that we call domain, it is necessary to focus on a limited number ofconcepts that are sufficient and relevant to create an abstraction of thephenomenon in hand. Thus, a central aspect of any modelling activityconsists of developing a conceptualization: a set of informal rules thatconstrain the structure of a piece of reality, which an agent uses to iso-late and organize relevant objects and relations.

A body of formally represented knowledge is based on a conceptualiza-tion: the objects, concepts, and other entities that are assumed to existin some area of interest and the relationships that hold among them. Aconceptualization is an abstract, simplified view of the world that wewish to represent for some purpose [16]. It contains the relevant con-cepts of that domain, relation between the concepts, and the axiomsabout these concepts and relations.

The process of conceptualizing is crucial in design of computer supportsystems. Conceiving problems and forming ideas, and abstracting ideasfrom particular instances is the heart of the matter in both producing andcommunicating about designs. The main aim of building the model isnot only to capture design information, but serves more importantly asa means to communicate and come to understand a design as it evolves.

Conceptualization of teaching material is to create a model or a designfor the teaching material, such simplification allows computer and hu-man alike to communicate. Lecture material and exercises are learningobjects in this model. Lecture materials which are considered here arematerials which are used to explain about a topic in the class room, theyare generally PowerPoint slides. Exercise sheet is a set of exercises andare intended to be done by students in order test and increase skill. Thelecture material and exercises available online are discussed here. On-tologies for the content and structure of lecture material and exercisesare to be developed;so that they can be used as shared vocabulary. Theontologies are developed using Semantic Web tools. The learning re-sources are annotated based on ontology.

1.3 Principal Results

In this thesis, ontologies for the teaching material is developed. The on-tologies are developed for the lecture material and exercises. There areseveral Semantic Web tools available to develop the ontologies. Someof them are compared and OWL is chosen for representing the ontolo-gies [23]. The teaching materials are semantically marked up using it.The ontologies are developed using the Protégé-OWL libraries [27].

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If the applications share the common ontology of teaching material thenthe teaching material of one application can be used by another, it alsoprovides intelligent integration such as sharing, searching and reusinginformation among applications. The ontologies of the teaching mate-rial can be used by authoring tool developers. Developing ontologiesis an important aspect of the Semantic Web [36]. To be useful ontolo-gies must be shared so that there is a common understanding among thelearning object producers about what the terms mean. This increasesthe reusability and interoperability of the learning objects. Since it islikely that different group of people will use different ontologies forlearning objects, mappings between these ontologies is also an impor-tant requirement

1.4 Organization of Thesis

The thesis is organized as follows:

Chapter 2Learning objects and Metadata for Annotation of LearningObjects

We begin with a general chapter on introductory chapter on learningobjects and existing standards. In the first part of the chapter, learningobjects are introduced and we mainly focus on reusability of the learn-ing objects. In the second part of the chapter, existing technologies areexplained briefly. We discuss about IEEE Learning Object Metadata(LOM) Draft Standard specification which was developed to providestructured metadata descriptions of Learning Objects in order to enablesemantic interoperability among applications on the e-learning domain.This standard is also briefly discussed in this chapter. Some propertiesfrom this standard are used in our ontologies. Bloom’s taxonomy forlearning levels is used to classify the questions in our exercise sheets isalso briefly explained in this chapter.

Chapter 3Ontologies and Representation

Here ontologies are explained, and several other topics like componentsof ontology, different types of ontologies , about the design criteria ofthe ontologies and various applications of ontologies are discussed. Andthen several Semantic Web tools available for representing ontologiesare introduced and they are compared. The comparison is mainly be-tween OWL and Topic Maps.

Chapter 4Ontology for Teaching Material and Modelling with Protégé

In this chapter the ontologies developed for the teaching material are di-cussed. The ontologies of lecture material and exercises are explained.

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Some properties from IEEE LOM which are adequate for the modeland Bloom’s taxonomy are also integrated in our model. They are alsoexplained briefly here. Protégé an open source Java tool is used for de-veloping the ontologies. A brief description about that software is alsogiven in this chapter.

Chapter 5Conclusion

In this chapter the results are summarized and the future work is pre-sented.

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2 Learning Objects andMetadata for Annotation of

Learning Objects

Learning objects enable and facilitate the use of educational contentonline. Internationally accepted specifications and standards make theminteroperable and reusable by different applications and in diverse learn-ing environments. The metadata that describes learning objects facili-tates searching and renders them accessible. In this chapter, the basicsabout learning objects and metadata for annotation of learning objectsare covered. In the first part of the chapter, learning objects are intro-duced and we mainly focus on reusability of the learning objects. In thesecond part of the chapter, existing standards and metadata for annota-tion of learning objects are briefly explained. After this, the relevanceand usage of the standards for this concrete application is discussed.

2.1 Learning Objects

The term learning object is defined by Wiley [40] "A learning objectis a digital learning resource that facilitates a single learning objectiveand which may be reused in a different context" .

Learning objects are a new way of thinking about learning content. In-stead of providing all of the material for an entire course or lecture,a learning object provides material for a discrete lesson or sub-lessonwithin a larger course. Examples of learning objects include based on anelectronic text, a simulation, a web site, a .gif graphic image, a Quick-Time movie, a Java applet or any other resource that can be used inlearning. In general, learning objects:

• are self-contained – each learning object can be consumed inde-

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pendently,

• are reusable – a single learning object may potentially be used inmultiple contexts for multiple purposes on multiple campuses,

• can be aggregated – learning objects can be grouped into largercollections, allowing for their inclusion within a traditional coursestructure,

• are tagged with metadata – every learning object has descriptiveinformation allowing it to be easily found by a search, – whichfacilitates the object being used by others in the department inparticular and the discipline in general.

Learning objects allow for learning that is:

• Just enough – if you need only part of a course, you can use thelearning objects you need.

• Just in time – learning objects are searchable; you can instantlyfind and take the content you need.

• Just for you – learning objects allow for easy customization ofcourses for a whole organization or even for each individual.

In this thesis, we are dealing with the conceptualization of teachingmaterial and our main aim is to reuse the existing material rather thandeveloping it again. Hence the reusability of the learning objects playsan important role, so we discuss about it in the following sections.

2.1.1 Reusability of Learning Objects

All learning objects have certain qualities. It is the difference in thedegree to which (or manner in which) they exhibit these qualities thatmakes one type of learning object different from another. The reusabil-ity of learning object also vary based on these qualities. A 5-layer tax-onomy of learning objects proposed by Wiley is given below [40]:

• Fundamental learning objectsare individual digital resourcesuncombined with any other.

• Combined-closed learning objectsare simple combination of fun-damental or combined closed objects. They typically mix dif-ferent resources. The resulting structure could be complex butshould not aggregate information to the content.

• Generative-presentation learning objectsprovide logic and struc-ture for combining or generating and combining fundamental andcombined-closed objects.

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• Generative-instructional learning objectsprovide logic and struc-ture for combining fundamental, combined-closed and generative-presentation objects. Those objects support student interactionevaluation instructional strategy instantiation. A generative-instr-uctional object should result in an instructional simulation capa-ble of generating instruction, problems, and evaluating answers.

• Combined-open learning objectscombine any types of object.They provide rich transitions between their components but donot modify them.

Qualifying this taxonomy in terms of granularity, fundamental and com-bined-closed learning objects are fine-grained material while other learn-ing objects are generally coarse-grained material.

The reusability of learning object deals with the potential of the objectto be reused. Educational material reusability is tied with its granular-ity. Next, we describe the reusability of each element of the taxonomyabove.

• Generative-presentation and generative-instructional learningobjectscan be reused in different contexts - the variety of thosecontexts is proportional to components adaptability. However,those objects are as adaptable as they are hard and costly to build[40].

• Combined-open learning objectshave a limited scope of reusesince they are instructionally specific composition of material.Nevertheless, in a very similar context, this type of learning mate-rial can directly be reused. Consequently, the reuse of such learn-ing objects could be automatic [41].

• Combined-closed and fundamental learning objects have a goodpotential for reuse since their internal structure should not be do-main specific. The paradox is that such fine-grained objects arestill hard to reuse [42].

There is no standard for the size (orgranularity) of a learning object.Larger learning objects are typically harder to reuse, and smaller learnerobjects save less work for those who are reuse them. Per the literatureof pedagogy, the best medium has been estimated as between five andfifteen minutes of learning material.

2.1.2 Factors Effecting Reusability of Learning Objects

There are several arguments for designing and developing material tobe reused as learning objects, including the following:

• Flexibility. If material is designed to be used in multiple con-texts, it can be reused much more easily than material that has

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to be rewritten for each new context. It’s much harder to un-couple an object from the context of its parent course and thenrecontextualize it than it is to contextualize as part of design anddevelopment.

• Ease of updates, searches, and content management.Meta-data tags facilitate rapid updating, searching, and management ofcontent by filtering and selecting only the relevant content for agiven purpose.

• Customization. When individual or organizational needs requirecustomization of content, the learning object approach facilitatesa just-in-time approach to customization. Modular learning ob-jects maximize the potential of software that personalizes contentby permitting the delivery and recombination of material at thelevel of granularity desired.

• Interoperability. The object approach allows organizations toset specifications regarding the design, development, and presen-tation of learning objects based on organizational needs, while re-taining interoperability with other learning systems and contexts.

• Facilitation of competency-based learning.Competency-basedapproaches to learning focus on the intersection of skills, knowl-edge, and attitudes within the rubric of core competency modelsrather than the course model. While this approach has gained agreat deal of interest among employers and educators, a peren-nial challenge in implementing competency-based learning is thelack of appropriate content that is sufficiently modular to be trulyadaptive. The tagging of granular learning objects allow for anadaptive competency-based approach by matching object meta-data with individual competency gaps.

• Increased value of content. From a business standpoint, thevalue of content is increased every time it is reused. This is re-flected not only in the costs saved by avoiding new design anddevelopment time, but also in the possibility of selling contentobjects or providing them to partners in more than one context.

The ideal reusable learning object content is

• modular, free-standing, and transportable among applications andenvironments,

• nonsequential,

• able to satisfy a single learning objective,

• accessible to broad audiences (such that it can be adapted to au-diences beyond the original target audience),

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• coherent and unitary within a predetermined schema so that a lim-ited number of meta tags can capture the main idea or essence ofthe content and

• not embedded within formatting so that it can be repurposed withina different visual schema without losing the essential value ormeaning of the text, data, or images.

In recent years, the concept of a learning object has received consid-erable attention in e-learning. Because it can be very expensive andtime-consuming to develop the content for an e-learning course. Beingable to reuse learning objects created by others reduces the time andcost to develop learning materials.

2.2 Metadata for Annotation of Learning Objects

Metadata helps people to organize, find, and use resources effectively.Adopting standard practices for metadata is part of a good informationmanagement policy. Using standard way of representing metadata thenthe metadata will be correctly understood and interpreted by others.

The existing technologies IEEE LOM, Bloom‘s Taxonomy are brieflyexplained in this section. After this, the relevance and usage of thesestandards for this concrete application is discussed.

2.2.1 Learning Object Metadata Standard (LOM)

Designers of online materials have a number of software tools to cre-ate learning resources. They are very useful in allowing learning re-sources creation that might otherwise require extensive programmingskills. Nevertheless, common agreement upon standards is needed inorder to design instructional material that can share common mecha-nisms to find and use it.

The IEEE Learning Object Metadata (LOM) Draft Standard specifica-tion, approved on June 12, 2002 [22], was developed to provide struc-tured metadata descriptions of learning resources calledLearning Ob-jectsin order to enable semantic interoperability among applications onthe e-learning domain.

According to the LOM specification, a learning object is any entity, dig-ital or nondigital, that may be used for learning purposes. Examples oflearning objects are multimedia content, instructional content, learningobjectives, instructional software and software tools, persons, organiza-tions and events referenced during technology supported learning. Thisspecification defines a conceptual model of the metadata structure in-

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cluding a set of elements to be used in learning objects metadata de-scriptions, such as the element name, author, owner and prerequisites,but does not include information on how to represent these metadata ina machine-readable format. Rather, it is intended to be referenced byother standards that define such implementations. The purpose of thestandard is to facilitate search, evaluation, acquisition, and use of learn-ing objects, for example, for learners, instructors or automated softwareprocesses. Likewise, it is intended to facilitate the sharing and exchangeof learning objects by enabling the development of catalogs and whichthe learning objects and their metadata are reused. Following is thedetailed list of purposes of LOM [22]:

• to enable learners or instructors to search, evaluate, acquire, andutilize learning objects,

• to enable the sharing and exchange of learning objects across anytechnology supported learning systems,

• to enable the development of learning objects into units that canbe combined and decomposed in meaningful ways,

• to enable computer agents to automatically and dynamically com-pose personalized lessons for an individual learner,

• to compliment the direct work on standards that are focused onenabling multiple learning objects to work together within an opendistributed learning environment,

• to compliment the direct work on standards that are focused onenabling multiple learning objects to work together within an opendistributed learning environment,

• to enable a strong and growing economy for learning objects thatsupports and sustains all forms of distribution; non-profit, not-for-profit and for profit,

• to enable education, training and learning organizations, govern-ments, public and private, to express educational content and per-formance standards in a standardized format that is independentof the content itself,

• to provide researchers with standards that support the collectionand sharing of comparable data concerning the applicability andeffectiveness of learning objects,

• to define a standard that is simple yet extensible to multiple do-mains and jurisdictions so as to be most easily and broadly adoptedand applied,

• to support necessary security and authentication for the distribu-tion and use of the learning objects.

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LOM Data Model

The base schema of LOM was envisaged to be extended and representedin different syntax forms by different communities of users. By speci-fying a common conceptual data schema, the LOM Standard specifica-tion ensures that different representations of Learning Object Metadatacompliant with the standard will have a high degree of semantic inter-operability.

The conceptual data schema of LOM groups metadata elements intonine categories intended to contain different kinds of metadata, namedGeneral, Life Cycle, Meta-Metadata, Technical, Educational, Rights,Relation, AnnotationandClassification, whose purpose is [22]:

• TheGeneral category is intended to group the general informa-tion that describes the learning object as a whole;

• TheLife Cycle groups the features related to the history and cur-rent state of the learning object;

• The Meta-Metadata groups information about the metadata in-stance used to describe the learning object;

• The Technical groups the technical requirements and technicalcharacteristics of the learning object;

• The Educational groups the educational and pedagogic charac-teristics of the learning object;

• TheRights groups the intellectual property rights and conditionsto use the learning object;

• TheRelation groups features that define the relationship betweenthe learning object and other learning objects;

• TheAnnotation category provides comments on the educationaluse of the learning object and provides information on when andby whom the comments were created;

• TheClassificationcategory describes the learning object in rela-tion to a particular classification system.

The following metadata items were also defined for each metadata ele-ment:

• name: the name by which the data element is referenced;

• explanation: the definition of the data element;

• size: the number of values allowed;

• order: whether the order of the values is significant;

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• example: an illustrative example.

For leaf nodes on each hierarchy, the LOMv1.0 Base Schema alsodefines:

• value space:the set of allowed values for the data element, typ-ically in the form of a vocabulary formed of a list of values or areference to another standard in which the list is defined;

• datatype: indicates whether the values are a strings of characters(LangString), specifications of a point in time (DateTime), or thespecification of an interval in time (Duration);

• vocabulary: indicates the structure of a vocabulary item.

2.2.2 Bloom‘s Taxonomy

A taxonomy of learning levels was proposed by Benjamin Bloom. Thistaxonomy can be used to classify the questions. This is used in ourmodel to classify the exercises. Each exercise is classified based on thelevel of difficulty. Such an classification helps when we want to searchbased on the level of difficulty. The following is a brief description ofthe taxonomy of learning levels, popularly known as Bloom’s Taxon-omy [6]. This taxonomy contains the following 6 learning levels:

• Knowledge: This is the ability to recall knowledge and informa-tion presented during an instruction. Being able to define domaintesting-related terms such as equivalence class analysis, boundaryvalue analysis and all 46 pairs combination is an example of thisability. This is not an intellectual ability. The next five learninglevels require intellectual skills.

• Comprehension: This is the ability to understand and grasp theinstructional material. The ability to understand what is meant byboundary values of a variable is an example of this learning level.

• Application: This is the ability to use the knowledge and skillslearned during the instruction by putting it to practice in real sce-narios or situations. Being able to identify variables of a realprogram and apply equivalence class analysis to the variables tocome up with equivalence classes for the variable is an exampleof this sort of learning level.

• Analysis: This is the ability to see patterns, correlate differentinformation and identify components of a problem. Being ableto realize when just applying boundary value analysis is a goodidea and when finding additional test cases based on special valuetesting is a better idea is an example of this kind of ability.

• Synthesis:This is the ability to use different pieces of informa-tion and put them together to draw inferences and possibly create

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new knowledge and concepts. Being able to put all different con-cepts in domain testing together and correctly apply them to anygiven program or software is an example of this ability.

• Evaluation: This is the ability to make judgements about theknowledge acquired and concepts learned through an instruction.This is also the ability to compare the learned concepts with othersimilar concepts and make informed decisions about their value,perhaps even being able to determine to what extent the instruc-tional material addresses the higher-level objectives of the in-struction. Being able to evaluate the effectiveness of the domaintesting method relative to other testing techniques or being ableto judge when one method is more applicable than others to asituation is an example of the highest level in Bloom’s taxonomy.

2.2.3 Summary

Metadata plays an essential role, when the goal is to discover, exchangeand reuse web-based learning material. Metadata is used in so manydifferent ways and is so important for effectively searching out, organiz-ing, and using learning resources that many different approaches havebeen developed. The use of standards for metadata representation forthe educative domain to describe the background and meaning of learn-ing objects is necessary to achieve semantic interoperability on the Web.

In our model, the taxonomy of Bloom is integrated. The LOM meta-data describes various properties of learning materials. According tothe usage of the model, we must be decide which of the metadata dataare relevant for the application in future. If all the metadata of the LOMstandard are used for annotation of learning objects, the author, whomakes the annotation must fill out a lot of data slots. For an effectiveannotation, only relevant metadata (according to the usage of learningobject) should be described. In this thesis, LOM educational propertiesthat were found to be adequate to represent elements of the teachingmaterial ontology are used. These properties from IEEE LOM that areused in our model are described in chapter 4.

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3 Ontologies andRepresentation

The World Wide Web is like a virtual library at one’s fingertips; Web isalso becoming an important medium for distribution of learning mate-rial. A large number of learning materials are available online. Learningmaterials of the courses offered at university are placed also availableonline. The contents of the courses offered at different universities arestrikingly similar. Benefits from sharing and reusing of learning mate-rials between applications are high. But we have a great problem to:

• find information about learning materials.

• easily reuse the existing material without producing new materialall the time.

A possible solution to this problem is the development of educationalplatforms where the annotation of learning material should be ontologybased. Basics of ontologies like components of ontology, different typesof ontologies, about the design criteria of the ontologies and also thevarious applications of ontologies are discussed in the first section. Inthe second part of the chapter Semantic Web tools for representation ofontologies are discussed.

3.1 What are Ontologies?

The word "ontology" has a long history in philosophy, in which it refersto the subject of existence. Since Aristotle’s time there has been an in-terest to represent the existing knowledge of the world with a methodol-ogy that identifies classes of objects with common properties in a hier-archical structure where some classes are specializations of others. Thisway to represent knowledge was calledOntology.

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Research on ontology is becoming widely spread in computer sciencecommunity. Ontology research has begun in early 90´s in the knowl-edge base community; the research activity has been accelerated by theSemantic Web movement in last few years.

There are many definitions of an ontology in computer science.

One of the most widely accepted definitions is the following by Gru-ber [18]: "An ontology is a formal, explicit specification of a sharedconceptualization. The term is borrowed from philosophy, where anOntology is a systematic account of Existence. For AI systems, what"exists" is that which can be represented. When the knowledge of adomain is represented in a declarative formalism, the set of objectsthat can be represented is called the universe of discourse. This setof objects, and the describable relationships among them, are reflectedin the representational vocabulary with which a knowledge-based pro-gram represents knowledge. Thus, in the context of AI, we can describethe ontology of a program by defining a set of representational terms.In such an ontology, definitions associate the names of entities in theuniverse of discourse (e.g., classes, relations, functions, or other ob-jects) with human-readable text describing what the names mean, andformal axioms that constrain the interpretation and well formed use ofthese terms. Formally, an ontology is the statement of a logical theory.A conceptualization is an abstract, simplified view of the world that wewish to represent for some purpose."

Ontologies in Computer Science evolved from semantic networks [37]and were proven to be quite useful in representing and facilitating thesharing of the knowledge about a domain by human and automaticagents. Ontologies have been used in Configuration Systems, SoftwareEngineering, Information Retrieval, Conceptual Modeling, Interoper-ability, Enterprise Modeling, Electronic Commerce, and many otherfields in the research and production areas.

Ontologies can be of different types depending on factors such as thedomain intended to be modelled or the use for which they are con-structed or the complexity they need to have. There are several clas-sifications of Computer Science’s ontologies, based on different pa-rameters. These classification’s help in deciding type of ontologiesneeded to be designed for any particular application. There are a lot ofapproaches for classification of ontologies.Van Heijst, Schereiber andWieringa classify them according to the the amount and type of struc-ture of the conceptualization [21], Gomez-Perez, Fernáandez-Lopezand Corcho classify ontologies based on the level of specification of re-lationships among the terms gathered on the ontology [17].

Guarino classifies them by their level of generality as [20]:

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• Top-level ontologies, which describe domain-independent con-cepts such as space, time, etc., and which are independent of spe-cific problems;

• Domain and task ontologies, which describe, respectively, the vo-cabulary related to a generic domain and a generic task;

• and, finally, application ontologies, which describe concepts de-pending on a particular domain and task.

Fensel [13] take a slightly different approach at distinguishing typesof ontologies. They make a distinction between static knowledge andproblem-solving knowledge. The levels of generality distinguished forstatic knowledge ontologies correspond roughly to the levels distin-guished by Guarino.

In this thesis, the domain ontologies for lecture material and exercisesare developed.

3.1.1 Ontology Components

Despite the representation language being used, ontologies share a com-mon set of characteristics in order to make knowledge representationand inference tasks possible. The main components of an ontology areconcepts, relations, instances, axioms and ontology operations.

Concepts: A concept (also called class or frame) is the description ofthe common features that a set of individuals have. A concept can beanything of which anything can be stated that could be relevant to theintended purpose of the ontology. It can be a physical or a digital ob-ject. An object can be a procedure description, a functionality, actionor strategy, among others. The idea behind concepts may be viewedas similar to the idea behind classes in the object-oriented modellingparadigm.

Each concept has an associated term as its name, a description in naturallanguage, and a set of properties (also called slots or roles) that charac-terize it. Concepts can be defined by extension, i.e., enumerating theirelements, or by intension, i.e., giving restrictions that their elementsmust maintain.

Relations: Relations describe the interactions between concepts or aconcept’s properties. They are the basis for the hierarchical structure ofthe ontology. Relations also fall into two broad kinds:

1. Taxonomiesthat organize concepts into sub-super-concept treestructures. The most common forms of these are:

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• Specialization relationships commonly known as the ‘is akind of’ relationship.

• Partitive relationships describe concepts that are part of otherconcepts.

2. Associativerelationships that relate concepts across tree struc-tures. Commonly found examples include the following:

• Nominative relationships describe the names of concepts;

• Locative relationships describe the location of one conceptwith respect to another;

• Associative relationships that represent, for example, thefunctions, processes a concept has or is involved in, andother properties of the concept;

• Many other types of relationships exist, such as ‘causative’relationships. Such as Component-integral object composi-tion, Material-object composition, Portion-object composi-tion, Place-area composition, Member-bunch composition,Member-partnership composition, etc.

Relations also have properties that capture further knowledge about therelationships between concepts. These properties can be used to expressuniversitality, optionality, cardinality, transitivity, etc. of the relationsbetween concepts.

Axioms: Axioms contribute to specify the definition of the ontologyelements constraining their interpretation. They state facts that mustalways hold which are useful to verify correctness on creation time ordeducing new information on query time.

Instances: Instances are the ‘things’ represented by a concept. Strictlyspeaking, an ontology should not contain any instances, because it issupposed to be a conceptualization of the domain. The combination ofan ontology with associated instances is what is known as a knowledgebase. However, deciding whether something is a concept of an instanceis difficult, and often depends on the application.

Ontology Operations: Ontological representation languages enablethe execution of a certain basic set of operations to cover updating andquerying tasks on ontologies. The simplest queries an ontology cananswer, despite the representation language used and the purpose it wasconstructed for, are:

• What are the individuals of a given concept?

• Given an individual, what are the concepts to which it pertains?

• Which individuals have a given value in a given property?

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• Which individuals are related to a given individual by a givenproperty?

Similarly, new concepts can be defined, properties related to conceptsand values changed or added during the entire life of the ontology.At editing time, the consistency of the ontology can be automaticallychecked, for example, to reject a value that was intended to fill a prop-erty for a given concept if it is not in concordance with the restrictionsdefined on the property values for this concept. At query time, inferencecan be made by using explicitly stated facts and the ontology axioms toinfer implicit new facts.

3.1.2 Design Criteria and Reasons for Developing Ontology

A set of design criteria to help in the ontology design task is presentedin [19]. They are:

• Clarity: An ontology should effectively communicate intendedmeaning, should be without any ambiguity by giving appropriatenecessary and sufficient conditions.

• Coherence:An ontology should maintain internal consistency. Atthe least axiom definitions should maintain logical consistency.As axioms determine the competency of an ontology.

• Extendibility: Ontology should give a scope to extend the exist-ing terms in such a way that it does not require much revision ofexisting definitions.

• Encoding bias: An encoding bias results when representationchoi ces are made purely for the convenience of notation or imple-mentation. This should be minimized because knowledge-sharingagents may be implemented in different representation systemsand styles of representation.

• Ontological commitment:An ontology should make as few claimsas possible about the world being modelled.

The following are some reasons for developing an ontology:

Sharing common understanding of the structure of information amongpeople or software agentsis one of the more common goals in develop-ing ontologies.

Enabling reuse of domain knowledgewas one of the driving forces be-hind recent surge in ontology research. If one group of researchers de-velops an ontology in detail, others can simply reuse it for their do-mains. Additionally, if we need to build a large ontology, we can in-tegrate several existing ontologies describing portions of the large do-main. We can also reuse a general ontology, and extend it to describe

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our domain of interest.

Making explicit domain assumptionsunderlying an implementation ma-kes it possible to change these assumptions easily if our knowledgeabout the domain changes. In addition, explicit specifications of do-main knowledge are useful for new users who must learn what terms inthe domain mean.

Separating the domain knowledgefrom the operational knowledge isanother common use of ontologies. We can describe a task of configur-ing a product from its components according to a required specificationand implement a program that does this configuration independent ofthe products and components themselves.

Analyzing domain knowledgeis possible once a declarative specifica-tion of the terms is available. Formal analysis of terms is extremelyvaluable when both attempting to reuse existing ontologies and extend-ing them.

Often an ontology of the domain is not a goal in itself. Developing anontology is akin to defining a set of data and their structure for otherprograms to use. Problem-solving methods, domain-independent appli-cations, and software agents use ontologies and knowledge bases builtfrom ontologies as data.

3.1.3 Application Areas for Ontologies

The following are different areas where ontologies are useful:

1. Knowledge engineering, knowledge representation, knowledgemanagement, knowledge sharing, knowledge integration. Knowl-edge Interchange Format (KIF) is designed as a framework for theinterchange of knowledge among disparate programs and whichcomprehends modules designed to allow the representation of, forexample, mathematical and physical information. The Ontolin-gua, the KIF translation language, is used as a working tool forthe construction of medical ontologies.

2. Information retrieval and extraction. Common Web ontologiescould in principle provide means to navigate diversity in a waywhich would involve not only producers but also the consumersof on-line information.

3. Natural language translation. One goal of applied informationsystems ontology is the provision of an Interlingua, a commontarget language for natural language translation which would al-leviate the need to construct ad hoc translators for each pair ofnatural languages by constructing for each such language a singletranslator into the common target.

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4. Database design, conceptual modeling, information systems de-sign, and information systems integration. The Tower of Babelproblem arises above all in the realm of database construction.In constructing software tools for merging large databases, it hasproved fruitful to develop common ontologies in terms of whichdivergent bodies of data derived from different sources can beunified together into a single system. In any given field there ischaracteristically a wide variety of databases using their own cate-gories for storing data objects. Different databases may use iden-tical labels but with different meanings; alternatively the samemeaning may be expressed via different names.

5. Enterprise integration. A common ontology may provide a com-mon framework of communication, and taxonomy of business ob-jects.

3.2 Ontologies and Semantic Web

We are on the brink of a new generation of World Wide Web (WWW):the Semantic Web. Unlike the existing WWW, where data is primarilyintended for human consumption, the Semantic Web will provide datathat is also machine processable. This will enable a wide range of intel-ligent services such as information brokers, search agents; informationfilters etc., a process that Tim Berners-Lee describes as "bringing theWeb to its full potential" [5]. The development of ontologies will becentral to this effort. As we are developing ontologies for materialsavailable online, role of ontologies in Semantic Web is discussed.

Ontologies are metadata, providing a controlled vocabulary of terms,each with an explicitly defined and machine processable semantics. Bydefining shared and common domain theories, ontologies help both peo-ple and machines to communicate more effectively. They will thereforehave a crucial role in enabling content-based access, interoperabilityand communication across the web, providing it with a qualitativelynew level of service: the Semantic Web. The Semantic Web will alsobe crucial to the development of web applications such as ecommerce,providing users with much more sophisticated searching and browsingcapabilities as well as support from intelligent agents such as shopbots(shopping "robots" that access vendor web sites, compare prices , etc.).Examples of the use of ontologies/taxonomies to support searching andbrowsing can already be seen at Yahoo Shopping and amazon.com.Even in growing industry like e-Learning ontologies are useful specify-ing vocabulary and support reusability and interoperability of learningmaterials. Ontologies are also useful in developing authoring tools.

The development of an ontology implies the representation of the knowl-edge of the domain being modeled into the syntax of a formal knowl-edge representation language. There are many knowledge representa-

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tion languages such as KL-ONE, Ontolingua, Classic, LOOM, Knowl-edge Interchange Format (KIF), Cycl, etc. that preceded Semantic Web.The ontologies of teaching materials are mainly designed to be sharedby different applications hence semantic mapping is very important.The Semantic Web languages provide greater support for semantic map-ping. And the Semantic Web languages are built on XML which isvery useful for interchange purposes so Semantic Web languages arechosen to represent ontologies. Semantic Web standardization providestwo standards for representing ontologies they are Topic Maps and RDF(Resource Description Framework). The Topic Maps standard "has itsroots in traditional finding aids such as back-of book indexes, glossariesand thesauri" [34] and is usually not directly linked to ontologies,mainly due to its lack of an advanced ontological vocabulary. Also,Topic Map advocates claim that Topic Maps are "ontology-agnostic"[38] and are therefore suited for representing any ontological vocabu-lary. RDF with OWL, on the other hand, "has its roots in formal logicand mathematical graph theory" [34] and has gained much popularitythrough the Semantic Web vision statement. Basics of both of thesestandards are described below and compared.

3.2.1 Topic Maps

Topic Maps are an ISO standard for the representation and interchangeof knowledge, with an emphasis on the findability of information. TopicMaps are designed to manage the infoglut, build information networksover any kind of information resources, and enable the structuring ofunstructured information. A Topic Map can be considered as an elec-tronic super index, having its roots from back-of-book index paradigm.Infoglut refers to the state of having too much information to make adecision or remained informed about a topic. The key features of atypical index are thus: topics (identified by their names, of which theremay be more than one); associations between topics; and occurrences oftopics (pointed to via locators). A Topic Map can also represent infor-mation using topics (representing any concept, from people, countries,and organizations to software modules, individual files, and events), as-sociations (which represent the relationships between them), and occur-rences (which represent relationships between topics and informationresources relevant to them) [33]. There is a standard XML-based inter-change syntax called XML Topic Maps (XTM [1]) for Topic Maps. Thekey concepts of Topic Maps like topics, associations, and occurrencesand other concepts are explained briefly in the following sections.

Topics: A topic, in its most generic sense, can be any "thing" what-soever - a person, an entity, a concept, reallyanything- regardless ofwhether it exists or has any other specific characteristics, about whichanything whatsoever may be asserted by any means whatsoever. Theterm "topic" refers to the object or node in the Topic Map that repre-sents the subject, the term used for the real world "thing" being referred

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to. There should be a one-to-one relationship between topics and sub-jects. Topic and subject can be used interchangeably to certain extent.

Topic types: Topics are categorized according to their kind. In aTopic Map, any given topic is an instance of zero or moretopic types.The relationship between a topic and its type is a typical class-instancerelationship. For instance Magdeburg would be a topic of type "city",Germany a topic of type "country", etc. Topics in any particular appli-cation will vary according to the needs of the application, the nature ofthe information, and the uses to which the Topic Map will be put. Topictypes are themselves defined as topics by the standard. The topics whichare needed to be used as types must be declared explicitly as topics inTopic Maps.

Topics have three kinds of characteristics: names, occurrences, androles in associations.

Topic names: Topics have explicit names. Topics don’talwayshavenames. A topic may even have multiple names, for instance to list syn-onyms or translations in different languages.

Occurrence: Topics are linked to one or more information resourcesthat are deemed to be relevant to the topic in some way. Such resourcesare calledoccurrencesof the topic. There are two different ways touse occurrences: if an external resource like a web page or a documentexists, the occurrence is basically a reference to that resource (e.g. thiscan be used to connect a picture to the topic representing a certain per-son). If some kind of "simple" data should be linked to a topic (like"22.12.1961") the data itself is stored directly within the occurrence el-ement (and therefore within the Topic Map).

Occurrence Roles: Concepts ofoccurrence roleandoccurrence roletype help to distinguish different types of occurrences. The role is sim-ply a mnemonic; the type, on the other hand, is a reference to a topicwhich further characterizes the nature of the occurrence’s relevance toits subject. The occurrence "22.12.1961" (occurrence role) may be in-stance of the class "birthdate" (occurrence role type).

Associations: To describe the relationships between topics Topic Mapstandard provides a construct called thetopic association. A topic asso-ciation asserts a relationship between two or more topics. For exampleMagdeburgis in Germany.

Association types: The associations between topics can be groupedaccording to their type. The typing of topic associations greatly in-creases the expressive power of the Topic Map, making it possible to

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group together the set of topics that have the same relationship to anygiven topic.

Association roles: Every topic that participates in an association playsa role in that association called theassociation role. Associations areassertions and have therefore no inherent direction; instead, every as-sociation specifies roles, and the topics to be connected through thatassociation are said to play these roles (role players). Association rolescan also be typed and the type of an association role is also a topic.

Subject identity (and published subjects): The aim of Topic Mapis to achieve a one-to-one relationship between topics and the subjectsthat they represent, so that all knowledge about a particular subject canbe accessed from a single topic. Sometimes the same subject is rep-resented by more than one topic, especially when two Topic Maps arebeing merged. In such a situation it is necessary to have some wayof establishing the identity between seemingly different topics. Theaddressable information resourcesubject identitywill be used here toidentify whether or not two topics are referring to the same subject. Onthe other hand, for a non-directly-addressable subject,subject indica-tors are being used as the identification.

Facets: Facets basically provide a mechanism for assigning property-value pairs to information resources. A facet is simply a property; itsvalues are called facet values. This could include properties such as"language", "security", etc.

Scope: The Topic Map model allows three things to be said aboutany particular topic: its names, associations in which it takes part in,and its occurrences. A scope represents a certain point of view on theinformation in a Topic Map, which means that the validity of assertionscan be limited to certain circumstances. Scopes can be specified forall three topic characteristics: these characteristics are then said to bescoped. If no scope is defined for a topic characteristic, the defaultunconstrained scope applies.

Reification: Topic Maps provide a feature which allows Topic Mapauthors to make statements about statements. We learned that associ-ations and occurrences do not have characteristics. But by definitiontopics can be occurrence, association or resource in Topic Map. Thetechnique of reification allows us to assign the topic characteristics toall addressable subjects including objects of the Topic Map itself.

Merging Topic Maps: If two Topic Maps are to be merged, it is es-sential to know if two topics represent the same subject: if yes, they

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must be merged into one topic that contains the union set of all charac-teristics of the original topics. There are three possibilities for two top-ics to represent the same subject: if the topics have the same base namein the same scope, the Topic Naming Constraint requires a merger; if thetopics use the same subject address to refer to an electronic resource; ifthe topics use the same subject identifier to refer to a subject indicatorfor any subject without an electronic address. The resulting topic willbe the set union of all names, occurrences and associations of the orig-inal topics (without doubles). Thus, any Topic Map corresponding tothe standard will never contain two topics representing the same sub-ject [35].

Topic Maps as Ontologies

To get a simple Topic Map up and running, very little is needed: a fewtopics that represent subjects, and may be a number of assertions to linkthem or occurrences that the topics can link to. There is actually noneed for defining classes or types of topics since topics, associationsand roles are not required to be typed. Associations can have any num-ber of roles, since they are n-ary by default, and even undirected. Infact, almost no "ontological overhead" has to be added to a Topic Map,if it is not needed. This freedom of expression is certainly one of thebiggest strengths of Topic Maps, as there are almost no constraints im-posed through the underlying data model. It is also that principle thatmade the concept of (formal) ontologies rather unpopular in the TopicMap community.

With respect to Topic Maps, ontologies generally use a more constrain-ing data model with a defined vocabulary to represent concepts andtheir relationships. The ontological vocabulary allows for the creationof inference engines and the automatic semantic validation of underly-ing data, but on the other hand, it sometimes also limits the expressivepower of the system. Such limitations have never been accepted bymost Topic Map advocates. Instead, Topic Maps are preferably referredto as being able to represent arbitrary ontological vocabularies. BernardVatant states that "both Topic Maps specifications and literature haveimplicitly or explicitly presented the standard as ’ontology-agnostic’,meaning they are able to support, represent and manage any kind ofknowledge in any kind of ontological context, and even independentlyof the constraints imposed by any ontology" [43]. For instance, theTopic Map standard does not define properties of associations such astransitivity; if one needs such a property, he has to define it (e.g. bycreating a topic "transitive association" and using it as type for otherassociations). Indeed, most Topic Maps can be found to be driven by(and thus at least partially representing) implicit ontologies: "Classesof topics, association types, role types, occurrence types, seem care-fully chosen, and one will find out some sensible and recurrent patternslinking association types to specific role types, occurrence types to topictypes, role types in association types to topic classes, implicit rules ofcardinality, etc." [39]. Due to the expressivity provided by Topic Maps

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they can be used to represent ontologies.

3.2.2 RDF Schema

RDF (Resource Description Framework) is a framework for metadatadescription developed by W3C (WWW Consortium) [3]. It employsthe triplet model <subject, attribute, object>, in which object is calledresource representing a web page. A triplet itself can be a subject andan object. Value can take a string or resource. Subject and object areconsidered as a node and attribute as a link between nodes. Thus, anRDF model forms a semantic network. RDF has an XML-based syntax(called serialization) which makes it resembles a common XML-basedmarkup language. But, RDF is different from such a language in that itis a data representation model rather than a language and that the XML’sdata model is the nesting structure of information and the frame-likemodel with slots [31].

RDF does not require that resources are retrievable on the web. RDFresources may be physical concepts, abstract concepts; in fact anythingthat has identity. Thus, RDF defines a language for describing about anything. RDF just enables assertion of simple statements consisting of asubject, a attribute and an object. But it does not describe the meaningof subjects, objects or attributes or relation between them [25].

RDF Schema Features:

RDF Schema introduces some simple ontological concepts they are ex-plained briefly below.

Classes and Subclasses:A class represents a collection of resources.Classes are themselves resources and are identified by URI’s. A re-source may be stated as a member of a class using the rdf:type property.Classes are represented using rdfs:class property. The rdfs:subClassOfproperty is used to represent one class being subclass to another. Classextension represents all the members belonging to that class, a classmay be a member of its own extension.

Properties and SubProperties: RDF properties or attributes are resour-ces, so they also form a class. RDF Schema defines rdf:Property to bethe class of all RDF properties. One property may be a subproperty ofanother and this can be represented using rdfs:subPropertyOf. Propertyextension is the set of pairs of objects related by the property.

Domain and Range: RDF Schema can state that all subjects of aproperty belong to a given class. It can also state that all objects ofa property belong to given class. The rdfs:domain property is used tostate that all the subjects of a property belong to a class, if more than one

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domain property is given, then subjects are members of all the classes.The rdfs:range property is used to state that all the objects of a propertybelong to a class, if more than one range property is given, then objectsare members of all the classes.

Datatypes: rdfs:Datatype class represents the class of all datatypes.It is a subclass of rdfs:Class and all datatypes have class extensions.

Other Properties: The rdfs:label is used to represent a human read-able name for a resource, rdfs:comment is a property used to providefurther information about the resource. The rdfs:seeAlso property helpsto link one resource to another which may provide further informationabout the resource. rdfs:isDefinedBy is subproperty of rdfs:seeAlso andusually used to link a resource to a RDF Schema which describes theresource.

Limitations of the Expressive Power of RDF Schema

RDF and RDF Schema may be used to represent some ontological knowl-edge. The main modeling primitives of RDF/RDF Schema concern theorganization of vocabularies in typed hierarchies: subclass and sub-property relationships, domain and range restrictions, and instances ofclasses. A number of other features are missing [25]. Some of themare:

Local scope of properties: rdfs:range defines the range of a property,for all classes, it is not possible to apply to only some classes.

Disjointness of classes: It is not possible to express that classes aredisjoint using RDF Schema.

Boolean combinations of classes: Sometimes new classes may bebuilt by combining other classes using union, intersection and comple-ment. RDF Schema does not allow such definitions.

Cardinality restrictions: Sometimes we wish to place restrictions onhow many distinct values a property may or must take, these are impos-sible to express in RDF Schema.

Special characteristics of properties: Sometimes it is useful to saythat a property is transitive, unique, or the inverse of another property.These are not possible in RDF Schema.

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Compatibility of OWL with RDF/RDF Schema

The Web Ontology Working Group of W3C identified a number of char-acteristic use-cases for Ontologies on the Web which would requiremuch more expressiveness than RDF and RDF Schema and introducedWeb Ontology Language (OWL). OWL is an extension of RDF Schema,in the sense that OWL uses the RDF meaning of classes and properties(rdfs:Class, rdfs:subClassOf, etc), and added language primitives to in-crease expressiveness. The richer the language is, the more inefficientthe reasoning support becomes. A compromise is needed, so that a lan-guage that can be supported by reasonably efficient reasoners, whilebeing sufficiently expressive to express large classes of ontologies andknowledge [2]. OWL is described in next section.

3.2.3 OWL

The Web Ontology Language (OWL) is a W3C standard which is builtupon RDF Schema and enriched with semantics. The features of OWLare given below.

The OWL Language features

Classes: The owl:Class element which is an extension of rdfs:Classis used to denote classes in OWL ontologies. The owl:disjointWith isused to express the fact that two classes must be disjoint i.e., they do notnot have any common instances. The owl:equivalentClass expresses thefact that two classes have exactly the same set of instances, althoughthe classes themselves are not equal. There are two predefined classes,owl:Thing and owl:Nothing. The former is the most general class whichcontains everything; the latter is the empty class. Thus every class is asubclass of owl:Thing and a superclass of owl:Nothing [27].

Property elements: In OWL there are two kinds of properties:

• Object properties: The owl:ObjectProperty which relate objectsto other objects.

• Datatype properties: The owl:DatatypeProperty which relate ob-jects to datatype values.

These properties are disjoint classes: i.e., either a property is subprop-erty of owl:ObjectProperty, or it is subproperty of owl:DatatypeProperty.In OWL more than one domain and range may be declared for proper-ties. In this case the intersection of the domains, respectively ranges, istaken. Equivalence of properties can be defined using a owl:equivalent-Property element. OWL also allows to relate properties using inverseand can be defined using owl:inverseOf. Domain and range can be in-herited when owl:equivalentProperty, owl:inverseOf (here domain andrange are inter-changed) are used.

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Property restrictions: A owl:Restriction defines an anonymous classwhich has no id, is not defined by owl:Class and has only a local scope:it can only be used in the one place where the restriction appears. InOWL we have: classes that are defined by owl:Class with an id, andlocal anonymous classes as collections of objects that satisfy certain re-striction conditions, or as combinations of other classes, as we will seelater. The latter are sometimes called class expressions. An owl:Restrict-ion element contains a owl:onProperty element, and one or more restric-tion declarations. The owl:onProperty is used to specify a property. Onetype of restriction declarations are those that define restrictions on thekinds of values the property can take:

• owl:allValuesFrom which is used to specify the class of possiblevalues the property specified by owl:onProperty can take.

• owl:hasValue states a specific value that the property, specified byowl:onProperty must have.

• owl:someValuesFrom which is used to specify the class of possi-ble values the property specified by owl:onProperty can take. Aparticular class may have a restriction on a property that at leastone value for that property is of a certain type. Another type iscardinality restrictions:

• owl:minCardinality which is used to specify minimum Cardinal-ity on a property with respect to a particular class.

• owl:maxCardinality which is used to specify maximum Cardinal-ity on a property with respect to a particular class.

• owl:cardinality is useful when both minimum and maximum car-dinalities need to be specified.

Special properties: Some properties of property elements can be de-fined directly:

• owl:TransitiveProperty defines a transitive property.

• owl:SymmetricProperty defines a symmetric property,.

• owl:FunctionalProperty defines a property that has at most oneunique value for each object.

• owl:InverseFunctionalProperty defines a property for which twodifferent objects cannot have the same value.

Boolean combinations: OWL makes possibility to talk about Booleancombinations (union, intersection, complement) of classes (be it definedby owl:Class or by class expressions).

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• the intersection of two or more class descriptions is representedusing owl:intesectionOf

• the union of two or more class descriptions represented usingowl:unionOf

• the complement of a class description can be obtained by owl:com-plementOf Boolean combinations can be nested arbitrarily.

Enumerations: An enumeration is a owl:oneOf element, and is usedto define a class by listing all its elements.

Instances: There are three built-in OWL properties that allow for stat-ing facts about the identity of individuals:

• owl:sameAs indicates that a certain concept is equal to some otherconcept.

• owl:differentFrom indicates that two instances refer to differentsubjects.

• owl:AllDifferent states that all instances in a list refer to differ-ent subjects; this construct can be replaced through correspond-ing binary owl:differentFrom properties and is hence provided forconvenience only.

Versioning: OWL provides several built-in properties to enable ver-sioning of ontologies. The owl:versionInfo allows to attach an arbitrarystring to a concept which contains information about the current versionof that concept.

Three species of OWL: OWL provides three sublanguages. The lan-guages along with specification which features of the language can beused in which sublanguage are given below in decreasing order of theirexpressiveness:

OWL Full is meant for those who want maximum expressiveness andthe syntactic freedom of RDF with no computational guarantees. InOWL Full, all the language constructors can be used in any combinationas long as the result is legal RDF.

OWL DL supports those who want the maximum expressiveness andhave computational completeness (all conclusions are guaranteed tobe computable) and decidability (all computations will finish in finitetime). OWL DL includes all OWL language constructs, but they canbe used only under certain restrictions. OWL DL is so named due toits correspondence with description logics, a field of research that hasstudied the logics that form the formal foundation of OWL.

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The advantage of this is that it permits efficient reasoning support. Thedisadvantage is the lack of full compatibility with RDF.

OWL Lite supports those who primarily need a classification hierar-chy and simple constraints. An OWL ontology must be an OWL DLontology, and must further satisfy some constraints.

3.2.4 Comparing OWL and Topic Maps

There are many similarities between Topic Maps and RDF [34]. In thissection, some differences between Topic Maps and OWL are figuredout. The Topic Map standard does not require us to declare topics astypes before using them; actually the standard gives no provision of assuch. A topic is considered a type or class as soon as some other topic,association, role or occurrence, is declared to be an instance of it. There-fore, by parsing an Topic Map it’s quite easy to gather which topics areused as topic, association, role and occurrence types, and constitute theTopic Map ’implicit ontology’. But it is difficult this way to capturemore subtle ontological features, like patterns of implicit constraints,such as association templates linking association type, role types andclasses of role players, which are certainly present implicitly in the in-tention of the Topic Map author. But OWL provides means for declaringthat are source is a class before populating it with instances, and OWLDL demands that this declaration be explicit before declaring instances[15].

Topic Maps provide no provision for checking logical consistency atany level, not even simple taxonomy integrity, like forbidding class-subclass loops, class-instance mismatching and other structures gener-ally forbidden by any mainstream ontology schema or language. Infact, the Topic Maps specification is deliberately intended to supportany kind of inconsistency the authors would like to assert. This particu-larity has attracted serious critics both from the Formal Logic commu-nity for obvious theoretical reasons, but also from implementers, usedto provide in their system all sorts of safeguards against well-knowntendency of human users to be inconsistent, individually or as a group.Provision for consistency should be brought about by future Topic MapsConstraint Language.

Both Topic Maps specifications and literature have implicitly or explic-itly presented the standard as ’ontology-agnostic’, meaning they areable to support, represent and manage any kind of knowledge in anykind of ontological context, and even independently of the constraintsimposed by any ontology" [Vat]. For instance, the Topic Map standarddoes not define properties of associations such as transitivity; if oneneeds such a property, he has to define it (e.g. by creating a topic "tran-sitive association" and using it as type for other associations). The OWLstandard, on the other hand, defines several properties such as transitiv-

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ity or symmetry, but does not allow for creating arbitrary propertiesof relationships (and thus is said to have a fixed ontological vocabu-lary). But this makes Topic Maps complex as everything needed is tobe declared explicitly. And this high expressivity provided increases thecomplexity of reasoning the Topic Maps.

Figure 3.1: OntologySpectrum

As shown in the figure [26] above OWL has strong semantics comparedto Topic Maps.

Along with above mentioned reasons Topic Maps also have some limi-tations to be used for representation of ontologies for teaching materials.

Creating instances is very simple using OWL but when using TopicMaps we have to define many terms for every instance. There are noproper tools to create Topic Map authoring tools for teaching materialswhich makes it compulsory that author should have knowledge aboutTopic Maps [11]. But when OWL is used to create sharable ontologiesfor teaching materials the created authoring tools are very user friendlyand no such knowledge is necessary. Considering all the above reasonsOWL was chosen to represent the ontologies of teaching materials.

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4 Ontology for TeachingMaterial and Modelling with

Protégé

An ontology is a formal explicit description of concepts in a domain ofdiscourse, properties of each concept describing various features and at-tributes of the concept, and restrictions on these properties. Classes arethe focus of most ontologies, classes describe concepts in the domain.The attributes for these classes are described by properties. These prop-erties are also used to describe relations between classes. There maybe different relations between the classes like subclass, superclass, has-part etc. Also some restrictions may be given on properties; domain andrange for the properties and cardinality restrictions are some examplesof them. An ontology along with a set of individual instances of classesconstitutes a knowledge base. Hence developing an ontology includesdefining the classes in the ontology, arranging these classes hierarchi-cally if there are such relations like subclass and superclass, definingthe relations with other classes, defining the properties for the classesand describing allowed values for these properties.

The goal of the thesis is to conceptualize teaching material, i.e., to createan ontology for teaching material which can be used as shared vocabu-lary. Teaching materials considered here for classroom instruction, theyare the materials which are used by the teachers to help learners learn.There are several such teaching materials available and some them areshown in the figure (4.1). Books are text books, other documentationhere may be some reference manual or research papers. Web sites onparticular topics may also be used as teaching material. Demos hererefer to the multimedia demos which may be used for visual presen-tation of showing how something works. Programming languages andother software may be considered as tools. Tutorials are a small groupof teaching sessions for discussion and the provision of individual as-

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sistance. In this thesis, an ontology for lecture material and exercises isdeveloped, to be used as shared vocabulary. In the following sections,the ontology developed for lecture material and exercises is explained .The ontology developed for the lecture material is described in the firstand then the ontology of exercises is described. Lecture material andexercises of the course "Natural Language System 1" is used for devel-oping the ontology, all the examples in the following sections are fromthat lecture.

Figure 4.1: Learning ObjectsOverview

4.1 Model of Lecture Material

An ontology for the structure of the lecture material is developed. Lec-ture materials which are considered here are materials which are used toexplain about a topic in the class room, they are generally PowerPointslides. The slides used in the class room are also placed in WWW sothat students can access them when ever they need. Lecture materialsare developed for all the topics of the course. The structure of the lecturematerial remains the same even though they are designed for differenttopics. Here an ontology for the structure and metadata of the lecturematerial is developed and which can be used for any course. Hence thedeveloped ontology can be used for developing the lecture material forany course.

Figure 4.2: Example for aSlide

Important concepts in the domain are identified to be represented asclasses and an ontology for the lecture material is developed. Lecturematerial discussed here are the PowerPoint slides which are used forexplaining about a topic in the class room, so they are a group of slides.An example of a slide in the lecture material is shown in the figure 4.2.

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Figure 4.3: Annotation ofLectureMaterial

The classLectureMaterialis used to represent the lecture material de-signed for explaining particular topic in the class room. The classLec-tureMaterialand its relation with other classes are shown in figure 4.3.

Figure 4.2 shows the classes used in representing the lecture materialand how they are related. EachLectureMaterialis designed to covera particular topic and that is represented using the classLectureTopic.These two classes are represented using the relationforLectureTopic.For instance the slide shown in figure 4.3 is an introductory slide and itis designed for the topic "Grammars and Parsing".

The Lecture materials discussed here are a set of slides and so they arerepresented using the classSlides. There are more than oneSlidesineachLectureMaterialand this is represented using the * for the relationhasSlideswhich is used to relateLectureMaterialandSlides. Figure4.4 shows some of the slides designed for lecture topic "Grammars andParsing".

Figure 4.4: Slides about"Grammars and Parsing"

The classLanguageused to represent the language in which theLec-tureMaterial is available. This is particularly useful during searching.This is related to the classLectureMaterialusing relationinLanguage.For example in all the examples above all the lecture materials are avail-able in English.

Termis the class used to represent the term in which this lecture mate-rial is going to be used. Details like for which year and whether it iswinter semester or summer semester are represented. This is related totheLectureMaterialusing the relationinTerm. Often the same coursesare repeated in different semesters and so the sameLectureMaterialcan

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be used again and hence * is present for the relationinTerm. The slidesshown above are used in winter semester 2003/04. Using the alreadyexisting material saves a lot of time.

The classlm-authoris used to represent the author of theLectureMate-rial and these classes are represented using the relationhasAuthor.

The lecture materials discussed here are designed to be used in classrooms to help in explanation and hence they are very simple and brief.These lectures materials often provide the information regarding otherbooks or materials etc. so that they can be used to get more detailedexplanation. Such type of materials are represented using the classMa-terial and it is related to the classLectureMaterialusing the relationadditional-information. There can be more than such references andthis is represented using the * for the relation additional information.The slide in figure 4.5 shows such references.

Figure 4.5: Slide with aReference

The classContentused to represent the topics that are covered in thatparticular course. Lecture materials may cover some topics of the con-tent. These topics which are covered in that material or some times theimportant topics among the contents of the course which are covered inthat material are considered as keywords. These keywords are very use-ful for searches. This relation ofLectureMaterialwith the contents isrepresented using the relationhas-content-keywordand often there canbe more than one keyword and hence a * is present on this relation. Forexamplecontext free grammars, parsing algorithms, etc. are keywordsin topic "Grammars and Parsing".

4.2 Model of Exercises

Here an model for exercises is developed. Exercises are used to testskills of students. A group of exercises are given in form of an exercisesheet for the students. Hence exercise sheet is described first and thenexercises. Exercise sheet is a set of exercises and are intended to bedone by students in order test and increase skill.

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The Exercise sheet is designed for a particular topic i.e., the exercisesor tasks to be done are on a particular topic and is used to test knowl-edge in that topic. The exercise sheets discussed here are available onWWW, are used along with the courses, and are designed to be used inclass room. Ontology for the structure of the exercise sheet is devel-oped and it does not depend on the topic for which the exercise sheetis developed. The ontology developed can be used for creating exercisesheet for different topics. An example of an exercise sheet is shown inthe figure (4.6).

Figure 4.6: Examples forExercises Exercise sheet 1

Introduction

Ex. 1Explain in which areas natural language is beingused in human-computer interaction already today.Where would it be useful in the future?

Ex. 2The lecture introduced different levels oflinguistic description (morphology, syntax,semantics, pragmatics). The following utterancesare considered wrong or problematic. On whichlevel do these phenomena arise?

To design an ontology for the exercise sheet the concepts of the domainare identified and are represented as classes. AnExerciseSheetis a setof exercises. The exercises are represented using the classExercise.ExerciseSheethas aTitle which represents the topic in which the skillsof the students are tested i.e., the exercises to be solved are given inthat topic. Also aNameis given to theExerciseSheetfor identification.The ontology for exercise sheet defines classes for each of the conceptsitalicized above. The exercise sheet shown in the figure 4.7 has name"Exercise sheet 1" and title "Introduction".

Figure 4.7: SomeAnnotations forExerciseSheet

The propertieshasTitle, hasName, hasExerciseare used to describe therealtions betweenExerciseSheet, Title, NameandExercise. From thefigure shown above, we can say that eachExerciseSheethave aTitle,

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Nameand a set ofExercise‘s, hasExercise* indicates that anExercise-Sheetcan have zero or moreExercise‘s. As the exercise sheets dis-cussed here are available on WWW, they have a location, and as theseexercise sheets are designed to be used in class room they have the infor-mation about the date on which it is going to be discussed in class. TheclassExerciseSheethas the attributeshasFileandesdateto represent theabove discussed location, date respectively.hasFileis a URI datatype,andesdateis a date datatype. For example, the exercise sheet shown infigure 4.6 available is available online1. This can be expressed usingthe propertyhasFile.

The classExercisewhich is used to represent a single exercise or ques-tion or task to be solved in theExerciseSheetis an important class. Thisclass is used to represent different types of exercises. Many exercisesheets were analyzed manually and from that the exercises can be clas-sified into 3 different types depending on their structure. The three dif-ferent types of exercises are explained briefly along with examples.

The first type ofExerciseis represented using the classExType1. Thisis the simplest of all the exercises an example of an exercise of this typeis shown in figure 4.8.

Figure 4.8: Example forExType1

Ex. 1Explain in which areas natural language is beingused in human-computer interaction already today.Where would it be useful in the future?

The exercises of this type have a description of the task to be solved;this is represented using the classTaskDescriptionin the ontology ofExerciseSheet. The solution for this exercise is represented using theclassSolution.

Figure 4.9: Annotation forExType1

Figure 4.9 showsExType1 and its relationship with the classesTaskDe-scrition andSolution. hasTaskDescriptionandhasSolutionpropertiesare used to describe the relationship ofExType1with TaskDescription

1 http://paris.cs.uni-magdeburg.de/lehre/ws-03-04/natsys-uebung/index-eng/session1.pdf

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andSolutionrespectively.

The second type ofExerciseis represented using the classExType2. Anexample of this type of exercise is shown in figure 4.10

Figure 4.10: Example forExType2

Ex. 4Translate the following Latin sentences intoEnglish using the dictionary shown below.Observe yourself: Which are the smallestdistinguishable steps you are going through whiletranslating. For which types of translationproblems is this dictionary useless? Whichaspects of your language competence influencethe translation process? Which additionalinformation would be useful to have in thedictionary? (a) Omnis Gallia est subdivisa inpartes tres.(b) Ariovistus ad Caesarem legatos misit.(c) Epistulam a te scriptam non accepi.(d) Cuius regio, eius religio.(e) Consulem legatus primus vidit.Note: You can find a Latin-English dictionaryonline ato http://www.sunsite.ubc.ca/Latin/index.html oro http://www.nd.edu/ archives/latgramm.htm/

In this type of exercises, there is a single task description and some datais given on which this task is to be performed. A simple example of thistype of exercises is say we are given a task description as add the fol-lowing numbers and then data is given. Then the addition is performedon the data given and here it is possible to give several sets of data andaddition of each set of data is to be done. The example shown above isalso of that type, the task to be performed is described first and then setsof data are given on which the task is to be done. Here each data itemhave a different solution.

Figure 4.11: Annotation forExType2

ExType2also has propertyhasTaskDescription. As explained in the caseof ExType1the classTaskDescriptionis used to represent the descrip-tion of the task. The classData represents the data on which the task

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is to be performed.hasData*represents that there can be zero or moredata. There is aSolutionfor each data item. The classData and itsrelations with other classes are as shown in figure 4.12.

Figure 4.12: Annotation forData

The classData has an attributetext, which is of string datatype andit can be in any language. Suppose for example, we are given a taskof translating the sentences from one language to another, the propertyhasLanguageis used to describe this relation. Languages are repre-sented using the classLanguage. And as discussed already each dataitem have solution and this is represented using the classSolution, thisis the same class which we used inExType1.

Figure 4.13: Example forExType3

Ex. P2Python operations on lists, sequences anddictionaries: Reflect the differences betweenlists and sequences.(a) Which function transforms a sequence into alist?(b) How can the second character from a string beremoved?(c) Which operations are useful to modify a listfrom (3, None,’hallo’) to (1,2,3)?(d) Implement your own method m.haskey(k).(e) Which relations do exist betweenlen(m), len(m.keys()), len(m.values()) andlen(m.items())?(f) Use an alternative way to create thedictionary m= (’street’:’Grundigstr.30’,’room’:108)

In both the type of exercises discussed above there is only a single taskto be solved.ExType3represents the class of exercises of third typein which there are more tasks to be solved. In this type of exercises,there are more than one task descriptions or the tasks to be solved, itcan be said that these exercises have more than one subtasks. ExType3represents the exercises which have one or more subtasks. Figure 4.13shows an example ofExType3.

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The example shown in figure 4.13 is an exercise of typeExType3andit has 6 subtasks. These subtasks can again be classified into differenttypes. Here the subtasks are classified into 4 types.

Figure 4.14: Example forExType3 with Subtask ST1

Ex. 3The lecture discussed typical properties ofnatural languages. Discuss similarities anddifferences between formal languages (e.g.,programming languages) and natural languages.(a) Where are the differences with respect tovocabulary (lexicon), rules (syntax), meaning(semantics), and usage (pragmatics)?(b) There exist artificial languages for humans(auxiliary languages, e.g. Esperanto, seeo http://www.esperanto.net/info/indexen.html oro http://www.webcom.com/ donh/esperanto.html.In which ways are they similar to formallanguages, and in which to natural languages(regarding the categories listed above)?

Subtask of first type is represented using the classST1. This type ofsubtask is similar toExType1, i.e., this type of subtasks also have a sin-gleTaskDescriptionand aSolution. An example of this type of subtaskis shown in figure 4.14.

The exercise shown in figure 4.14 is an example ofExType3and it hastwo subtasks. Both the subtasks in the example are of typeST1.

The second type of subtaskST2is used to represent questions asked incomprehension type of exercises. Some data is given and the questionsare asked based on the data.ST2is used to represent this type of tasks.

In the example shown in figure 4.15 data is given. The sentences 1through 3 are the data given and a, b, c, d are the tasks to be performed.There may be a single solution for each task or more than one solutionfor each task. For example in the example shown above task (a) hasa solution for each of the sentences given. But it is not the case withcase with task (c), this has a single solution taking all the sentences intoconsideration and presenting the results obtained.ST2represents thistype of subtasks.

ST3 is used to represent subtasks dealing with multiple choice ques-tions. In this type of tasks a question is given and some solutions arealso given and the correct one among the given solution is to be chosen.

The example shown in figure 4.16 subtasks of such type. A questions

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Figure 4.15: Example forExType3 with Subtask ST2

Ex. 8Look at the following sentences:(1) Time flies like an arrow.(2) Mr. Spock was charged with illegal alienrecruitment.(3) Flying planes made her duck.Please answer the following questions:(a) Show that the sentences are ambiguous.(b) Describe informally the syntactic structureof the sentences to show which ambiguities arecaused by syntax. When you translate thesesentences to your native language are theyambiguous as well?(c) Which semantic ambiguities do you observe?Do they occur in your native language as well?(d) Which pragmatic ambiguities do you observe?Which ones can you resolve? What knowledge doyou need to resolve them?

and options to choose are given and a solution have to be chosen fromthe given options.ST3is used to represent subtasks of this type.The subtask of the fourth type is represented by the classST4. In sometasks data for the task may be given only for a subtask i.e., not fortheExerciseas whole but only for a subtask. These types of tasks arerepresented usingST4.

Figure 4.16: Example forExType3 with Multiple Choice

Subtasks

(f) In the English language inflectionalmorphemes can be:(1) Prefixes, Suffixes, Infixes(2) Prefixes, Suffixes(3) Suffixes only(4) Infixes only

(g) In the English language derivationalmorphemes can be:(1) Prefixes, Suffixes, Infixes(2) Prefixes, Suffixes(3) Suffixes only(4) Infixes only

For example in the exercise shown in figure 4.17 the task (a) does notneed any data it is of typeST1but task (b) needs the data from thecomic. Here the data is given only for task (b).ST4is used representthis type of subtasks.

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Figure 4.17: Example forExType3 with Subtask ST4

Ex. 11(a) Which word classes does English have? What

are the criteria for differentiation?(b) Determine the word classes of the words from

the comic below.

Till now, the exercises are classified based on their structure. These ex-ercises i.e.,ExType1, ExType2, ExType3can be classified as theoreticalor practical. Theoretical tasks are represented using classTheoretical-tasks. Practical tasks are represented using the classPractical-tasksandthis type of tasks needs some programming to be done.Practical-taskshave some additional properties like the programming language neededto complete the task and the deadline for the submission of the task.

Figure 4.18: Annotation forPractical Tasks

The Practical-taskshave propertiesusedPland hasDeadline. Theseproperties are used to describe the relationship with theProgramming-Languageused andDeadlinefor the submission of the task respectively.The classProgramming-Languageis an enumerated class and it is usedto represent programming languages, the classDeadlineis used to rep-resent the deadline for submission.

The exercise shown in figure 4.19 is an example of practical-tasks.These tasks need some programming and the language needed is se-lected form classProgramming-Language. As shown in the example,the practical tasks have deadline for submission of the task. The classesTheoretical-tasksandPractical-tasksare subclasses of the classExer-ciseand exercises can be of any type i.e., they can beExType1or Ex-Type2or ExType3.

There are many other properties for the classExercise; they are shownin the figure 4.20. Each row in the figure represents a property of theclassExercise. If there are three columns in row it indicates that these

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Figure 4.19: Example forPractical Task

Ex. P3The Regular Expressions are helpful for theprocessing of text streams. In this task youshould use regular expressions for the detectionof statements of times or dates (e.g. 10/29/2003or 1.15pm). The input is a text file with morethan one time and date statements. The detecteddata should be returned in a list.Hint for solution: Python modules re, string,and soHint for vote: The task is to be solved till11/11/03. If you want to ’vote’ this task byemail you must submit the solution of this taskbefore 11/11/03 to makunze@iws.cs.unimagdeburg.de

properties relate theExerciseclass to other classes. If the propertiesare used to linkExerciseclass and data items the there are only twocolumns in a row. But the data properties are not included int the figureshown. The first column in each row is the name of the property andlast column represent the class to whichExerciseclass is related or therange of the property. If there is more than one class in the last columnfor a particular column it indicates that any one class among them canbe used as range. Each exercise can be either theoretical or practical thisis defined using the propertyhasType. Each exercise can be any one ofthe three type of exercises this is represented using the propertyhas-TaskType. These properties and the classes which are ranges of theseproperties are already described above. If there is a * in the relationsshown then it indicates a one-to-many relation. The remaining proper-ties of the Exercise are described in figure 4.20.

Each Exercise have an identification which represented using the classId and the classes are related using the propertyhasId. For example theexercise shown in figure 4.21 has Ex.37 as id. Exercises may sometime hasIntroctionand this represented using the classIntroductionof-task. The example shown below has introduction. The introduction inthe example has some brief explanation about WordNet. Sometimes,data is given along with the exercise and questions or tasks to be an-swered are based on these data, for example comprehension type ofquestions which are explained while describingST2. SoExercisesometimeshasDataand the data is represented using the classData1. Thereis a * in the second column of the propertyhasDataindicating that therecan be more than one data item. The example shown in figure 4.21 hasData1the elements represented using 1,2,3 are data.

The same exercise may be used many times for example in summersemester and then again in winter semester. The details like this i.e.,

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Figure 4.20: Annotation forExercise

the previous terms in which the same exercise is represented using theclassHistory and this is related toExerciseusing the propertyhasHis-tory. The results from other exercises are needed for solving an exercisethese types of dependencies are represented using the classReference.Sometimes hints are provided to solve the exercise, these are also rep-resented using theReferenceclass and is related to Exercise using thepropertyhasReference. An example of exercise having a reference isshown in figure 4.22.

In the example shown in figure 4.22 the results from Ex. 17 are usedto solve the problem. Identifying the keywords in the exercise helps insearching the tasks on a particular topic. For example, in the exerciseshown in figure 4.22 noun phrases and prepositional phrases can beconsidered as keywords. The keywords are represented using the classKeywordsand is related to Exercise usinghasKeyproperty. Even thesubtasks ST1, ST2, ST3, ST4 have the propertieshasReference, hasId,hasHistory.

Blooms Taxonomy for learning levels which is introduced in the lastchapter is used to classify the exercises. Bloom identified six levelswithin the cognitive domain, from the simple recall or recognition offacts, as the lowest level, through increasingly more complex and ab-stract mental levels, to the highest order which is classified as evalua-tion. All the questions can be categorized using this taxonomy. Each

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Figure 4.21: Example forExercise with Introduction

Ex. 37WordNet is a lexical database providinginformation on English nouns, verbs,and adjectives, e.g., different senses,lexico-semantic relations (synonymy, meronymy,etc.). You can use two interfaces to WordNet inthe exercise acount: xwordnet and wn. WordNetand the conceptual dependency (CD) theory takedifferent approaches in the analysis of verbs:WordNet describes verbs using lexicla relations(e.g., synonymy, antonomy, etc.) while CD isbased on decomposition.(a) Draw the CD representations for the sentencesbelow.(b) Using WordNet, draw a diagram for everyverb (for the relevant meaning!) that shows thelexical relations to other verbs.(1) Yesterday, Sue gave a bar of chocolate toOliver.(2) Oliver takes the chocolate from Sue.(3) Oliver sells Tom the chocolate for 2 pounds.

Exercisecan be any one of the six categories depending on its complex-ity. An enumerated classbloom-categoryis created to represent BloomsTaxonomy, the instances of this class areknowledge, comprehension,application, analysis, synthesis, evaluation; categories given by Bloom.Each exercise is given a category depending on the complexity. LOMproperties that were found to be adequate to represent elements of theExercise sheet ontology, are integrated into the model.

Figure 4.22: Example forExercise with Reference

Ex. 22In Exercise 17 (on Exercise sheet 5) youidentified the noun phrases (NPs) andprepositional phrases (PPs) of a text.(a) Specify grammar fragments which describe thestructures of the NPs and PPs occurring in thistext.(b) Draw the constituent structure trees forthree NPs and three PPs

The properties from IEEE LOM, used in the ontologies of teaching ma-terial are given in the table shown in 4.23. The ontologies for teachingmaterial were developed and if the concepts were already covered inIEEE LOM, then those properties were used. Some general, techni-cal and educational metadata properties were used in the model. Someother metadata data might be added but adding too much metadata to

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these small learning objects is not very useful and more over the such adata will be redundant. Hence only minimum required properties wereadded.

One of the advantages of shared conceptual models is that they can bereused in various contexts, even some that have not been imagined yet.Ontologies should adequately represent a domain and allow some kindof formal reasoning. They should be both understandable by humansand processable by software agents. Furthermore, since ontologies willevolve over time, they need to be maintainable. This demands for on-tology modeling tools that provide a user-friendly view on the ontologyand support an iterative working style with rapid turn-around times.Tools should also provide intelligent services that reveal inconsisten-cies and hidden dependencies among definitions. OWL is chosen forrepresentation of ontologies. The ontology language employed is theWeb Ontology Language OWL [27]. To build the ontologies, we em-ployed the ontology editor Protégé [32] in combination with an plug-insupporting OWL [23].

Figure 4.23: List of LOMProperties Used

4.3 Modelling with Protégé

Protégé is an open-source ontology development environment with func-tionality for editing classes, slots (properties), and instances. At its coreis a framebased knowledge model with support for metaclasses. Otherlanguages such as OWL can be defined on top of this core frame model.Protégé makes it not only possible to extend the metamodel but alsoto customize the user interface freely. As illustrated in figure 4.24,Protégé’s user interface consists of several screens, called tabs, each ofwhich displays a different aspect of the ontology in a specialized view.Each of the tabs can include arbitrary Java components. Most of theexisting tabs provide an explorer-style view of the model, with a tree

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Figure 4.24: Screenshot ofProtégé

on the left hand side and details of the selected node on the right handside. The details of the selected object are typically displayed by meansof forms. The forms consist of configurable components, called wid-gets. Typically, each widget displays one property of the selected ob-ject. There are standard widgets for the most common property types,but ontology developers are free to replace the default widgets with spe-cialized components. Widgets, tabs, and back-ends are called plugins.

The OWL Plugin is a large Protégé plugin with support for OWL. It canbe used to load and save OWL files in various formats, to edit OWLontologies with custom-tailored graphical widgets, and to perform in-telligent reasoning based on description logics. As shown in 4.24, theOWL Plugin’s user interface provides various default tabs. The OWL-Classes tab displays the ontology’s class hierarchy, allows developersto create and edit classes, and displays the result of the classification.The "Properties" tab can be used to create and edit the properties in theontology. The "Individuals" tab can be used to create and edit individ-uals, and to acquire Semantic Web contents. The "Forms" tab allows tocustomize the forms used for editing classes, properties and individu-als. The "Metadata" tab displays ontology metadata such as namespaceprefixes. The OWL Plugin provides direct access to reasoners such asRacer2.

The ontologies for exercises and lecture material are developed usingProtégé OWL plugin. The ontologies are populated by creating in-stances. Exercises and lecture material of "Natural Language Systems1" is used to create instance. The hierarchies of the ontologies of Exer-cise sheet and Lecture material are shown in figure 4.25.

The model allows the user to search for specific material, for example

2 http://www.racer-systems.com/index.phtml?lang

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Figure 4.25: ClassHierarchies of Lecture

Material and Exercises

search for tasks with a specific semantic density, or programming tasksfor prolog etc.

Figure 4.26 shows the result for a query. In the graph (presentedby TGVizTab3 of Protégé), all learning objects are listed which arelinked with the content keyword "syntax". The system found three ex-ercises (ES1Ex1, ES9ExP13, ES9ExP14) and one slide material (nl-stopic2slides). The different colors of the graphs present different rela-tions. Green marks annotation by LOM metadata, red marks the cate-gory of the bloom classification, dark blue marks other relations of themodel, for instance, the usesPl relation. Light blue annotates relationsbetween the learning objects and the content keywords. Several suchsearches are possible using the model.

3 http://www.ecs.soton.ac.uk/ ha/TGVizTab/TGVizTab.htm

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Figure 4.26: Retrieval viaGraphical Presentation

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5 Conclusion

In this thesis, teaching material was conceptualized. The teaching ma-terials which are taken into consideration are lecture materials and exer-cises, which are designed for a class room use and are available online.Conceptualization of teaching material is to create a model for it so thatit can be shared between different applications.

Ontologies provide a shared and common understanding of a domainthat can be communicated between people and heterogeneous applica-tion systems. In consequence, they will play a major role in supportinginformation exchange processes in various areas. The formal nature ofontologies makes them amenable to machine-readability and providesa well-defined semantics for the defined terms. This allows computerprograms to manipulate, transform and draw inferences from informa-tion represented using the ontology. Here ontologies are developed forteaching material.

A model is developed for the content and structure of the teaching mate-rial. Humans and computers can communicate alike when the teachingmaterials are semantically marked up. In order to be able to interpretthe meaning of learning objects and services, several semantic model-ing and coding techniques are available, like RDF Schema, Topic Maps,OWL. Hence Semantic Web tools are chosen to represent the ontolo-gies. There are several such Semantic Web tools available for repre-senting the ontologies.

In this thesis Topic Maps, RDFS and OWL are taken into considerationand compared. OWL chosen for representing the ontologies, because itwas found to be suitable for representing teaching materials. The on-tologies are developed using Protégé-OWL.

For developing the model of teaching material the lecture materials and

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exercises of "Natural Language Systems 1" are used in this thesis. Forcreating the model it was necessary to analyze a lot of material partic-ularly for exercises, because there are various types of exercises. Thedeveloped ontologies are populated with that material. Since the ontol-ogy is developed for the content and metadata of the teaching materialthere are several uses. A model for annotation of lecture material and amodel for annotation of exercises is created. Metadata plays an essen-tial role, when the goal is to discover, exchange and reuse web-basedlearning material. Metadata is used in so many different ways and is soimportant for effectively searching out, organizing, and using learningresources that many different approaches have been developed. The useof standards for metadata representation for the educative domain to de-scribe the background and meaning of learning objects is necessary toachieve semantic interoperability on the Web. So the LOM metadataand Bloom’s taxonomy are integrated in our model. The model devel-oped can be used while developing exercises and materials for differentcourses. If the applications share the common ontology of teaching ma-terial then the teaching material of one application can be used by an-other, it also provides intelligent integration such as sharing, searchingand reusing information among applications. With the model developedits possible to access even small components of the learning object. Thisis particularly helpful when we often want to reuse an already existingmaterial. In such cases, we want only some parts of the already existingmaterial, this is possible with the ontology developed. For instance, wewant to use only one exercise from pool of exercises, then we can accessthat exercise and reuse it since the content and structure of the exercisesheet are semantically marked up. Since, the learning resources areannotated based on ontology the annotated parts can be used betweendifferent courses and also in the same course in different ways (e.g. astable, or as a bulleted sequence, etc). Obviously, it would be useful touse different presentations of the same learning object than number oflearning objects describing the same problem.

Semantically marking up the learning objects content improves betterlearning object findability. The Semantic Web search engine can en-able the creation of subject matter specific search agents capable ofcrawling the web for materials in multiple formats (ex. HTML, PDF,PowerPoint, Word, etc), curriculums, syllabi, and various other fields.There are several such searches possible, which are useful for both thecourse developers, tutors and students. As already mentioned materialscan be searched based on the type of resource like PDF or PowerPoint,etc. Materials can be searched on the keywords; this type of search isvery useful when material on particular topic being searched. Thesesearches are possible because the metadata is also included in the ontol-ogy. There are various other concepts involved in the ontology, whichmakes it possible to search the material in different ways. If the ontol-ogy developed is shared by different applications the reusability of theteaching material increases, which saves a lot of effort involved in cre-ating teaching materials.

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Future Work

In this section, we discuss some possibilities for future work that couldbe carried out.

In this thesis a model for teaching materials is created, but here a modelonly for lecture material and exercises is created. But there are severalother teaching materials available like websites, tutorials, etc. Ontolo-gies can be developed for other teaching materials also. Developingsuch ontologies have many benefits like increasing reusability of exist-ing material.

While developing model for teaching material, the material of the course"Natural Language Systems 1" is used. So the ontology developed maynot cover model of all the lecture materials and exercises. There areseveral other courses and may be designed by different universities, theymay be explored and a better model can created and cover wide range oflecture materials and exercises. This will be very useful because, modelcan be globally used by course the developers at different universitiesand for developing different courses. This increases the interoperabilityand reusability of the existing material.

Here the ontologies for teaching are developed using Protégé-OWL andare populated. User friendly interfaces can be developed to be usedby different people. Interfaces can be developed to be used by coursedevelopers and students for instance. Course developers can use the in-terface to find the details about the existing materials, while designingthe material. Every one has different access facilities like students arenot allowed to edit the material. Students should be able to search thematerial in different ways i.e., like using different topics for search orbased on metadata. This designed model can be used to search for thedata in the existing instances. This model can also be used to create in-stances for any lecture i.e., we can populate the model with any courseand can be used.

One of the most interesting application is to design the user interface. Itwould be useful if there is a tool support for developing teaching mate-rial contents of the web pages that can be presented, modified and inter-linked consistently. This is possible if the pages produced by authoringtools contain annotation with pointers to appropriate ontologies. Thiskind of authoring tool for teaching materials would be easy to createby an author who is not an expert in Web page development could markthe contents from the input with pointers to various ontologies related toteaching materials. Moreover, the resulting Web page itself would thenbe properly marked-up and thus made machine-interpretable. The basicidea here is that authors need not worry about the technical details ofthe markup moreover, they may not even need to know that ontologies

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exist, and will still do free markup when developing their applications.Once the initial network of ontologies for teaching materials is estab-lished, the tool developer may decide if he wants to use the ontology. Ifwe design such a tool, while the author is developing the material, hegets a complete description of the ontology, relations, the constraints,and possible links to other ontologies. Upon saving the material, thetool insets pointers to the ontologies being used into the web page ofthe application automatically. Now, the course material can be trulydistributed in many pages and on different servers, yet all of the pageswill be semantically interconnected through the network of ontologiesand the material developed for the application will be reusable.

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Bibliography

[1] XTM, 2001. http://www.topicmaps.org/xtm/1.0/xtm1-20010806.html.

[2] OWL, 2004. http://www.w3.org/TR/owl-features/.

[3] RDF, 2004. http://www.w3.org/RDF/.

[4] BARKER, P., “Developing Teaching Webs - Advantages, Prob-lems and Pitfalls,” (Montreal, Canada), Educational Multimedia,Hypermedia and Telecommunication (AACE), 2000.

[5] BERNERS-LEE, T., HENDLER, J., and LASSIL, O.,The SemanticWeb, ch. 5, pp. 34–43. Scientific American, May 2001.

[6] BLOOM, B. S.,Taxonomy of educational objectives: Handbook 1- cognitive domain. New York: Longman, 1956.

[7] BRASE, J. and NEJDL, W., Handbook on Ontologies, ch. Ontolo-gies and Metadata for eLearning. Springer-Verlag, 2005.

[8] CASEY, J. and MCALPINE, M., “Writing and usingreusable educational materials: a beginner’s guide.” CETISEducational Content Special Interest Group Publication.http://www.gla.ac.uk/rcc/staff/mhairi/index.html.

[9] DEVEDZIC, V., “What does current Web-based education lack,”(Innsbruck, Austria), Proceedings of the IASTED InternationalConference APPLIED INFORMATICS, Feb 2002.

[10] DEVEDZIC, V., “Key Issues in Next-Generation Web- Based Ed-ucation,” in IEEE Trans. on SMC - Part C: Appl. and Review,vol. 33, pp. 339–349, 2003.

[11] DICHEV, C., DICHEVA , D., and AROYO, L., “Using Topic Mapsfor E-Learning,” (Rhodes, Greece), pp. 26–31, Computers and Ad-vanced Technology in Education (CATE 2003), 2003.

65

[12] DOWNES, S., “Learning Objects: Resources For Distance Educa-tion Worldwide,” International Review of Research in Open andDistance Learning, 2001.

[13] FENSEL, D., Ontologies: A Silver Bullet for Knowledge Manage-ment and Electronic Commerce. Berlin: Springer-Verlag, 2 ed.,2004.

[14] GAEUEVIC, D., JOVANOVIC , J., and DEVEDIC, V., “EnhancingLearning Object Content on the Semantic Web,” (Joensuu, Fin-land), pp. 714–716, The 4th IEEE International Conference onAdvanced Learning Technologies, 2004.

[15] GARSHOL, L. M., “Topic maps, RDF,DAML, OIL: a Comparison in Ontopia,” 2002.http://www.ontopia.net/topicmaps/materials/tmrdfoildaml.html.

[16] GENESERETH, M. R. and NILSSON, N., Logical Foundations ofArtificial Intelligence. San Mateo, CA: Morgan Kaufmann Pub-lishers, 1987.

[17] GÓMEZ-PÉREZ, A., FERNÁNDEZ-LÓPEZ, M., and CORCHO,O., “Ontological Enginering,” (London), Springer-Verlag, 2003.

[18] GRUBER, T. R., “A Translation Approach to Portable OntologySpecifications,” 1993. Knowledge Acquisition.

[19] GRUBER, T. R., “Toward Principles for the Design of OntologiesUsed for Knowledge Sharing,”International Journal of Humanand Computer Studies, 1995.

[20] GUARINO, N., “Formal Ontology in Information Systems,” (Am-sterdam), IOS Press, 1998.

[21] HEIJST, G. V., SCHEREIBER, A. T., and WIELINGA , B. J., “Us-ing Explicit Ontologies in KBS Development,”International Jour-nal of Human and Computer Studies, 1996.

[22] IEEE P1484.12,Draft Standard for Learning Object Metadata.http://ltsc.ieee.org/wg12/.

[23] KNUBLAUCH , H., “Editing OWL Ontologies with Protégé,” 2004.http://protege.stanford.edu/plugins/owl/tutorial/index.html.

[24] LEE, T. B. and FISCHETTI, M., Weaving the Web: The OriginalDesign and Ultimate Destiny of the World Wide Web by its Inven-tor. San Francisco: HarperCollins Publishers, 1999.

[25] MANOLA , F. and MILLER , E., “RDF Primer: W3C ProposedRecommendation,” Dec 2003. http://www.w3.org/TR/2003/PR-rdfprimer- 20031215.

[26] MCGUINNESS, D., “Ontologies Come of Age.” MIT Press, 2002.In D. Fensel et al. (eds): The Semantic Web: Why, What, andHow.

66

[27] MCGUINNESS, D. L. and HARMELEN, F. V., “OWL WebOntology Language, W3C Candidate Recommendation,” 2004.http://www.w3.org/TR/2003/CR-owl-features-20030818/.

[28] M IZOGUCHI, R., IKEDA , M., and SINITSA , K., “Roles of SharedOntology in AI-ED Research –Intelligence, Conceptualization,Standardization, and Reusability,” inAIED-97, (Kobe, Japan),pp. 537–544, 1997.

[29] MOHAN, P. and BROOKS, C., “Learning Objects on the SemanticWeb,” in Adv. Learning Technologies, (Athens, Greece), In Proc.of the 3rd IEEE Int’l Conf, 2003.

[30] MOHAN, P. and GREER, J., “Reusable learning objects: cur-rent status and future directions,” (Honolulu, Hawaii), Ed-Media,2003.

[31] NILSSON, M., “The Semantic Web: How RDF WillChange Learning Technology Standards,” Sep 2001.http://www.cetis.ac.uk/content/20010927172953/viewArticle.

[32] NOY, N. F., SINTEK , M., DECKER, S., CRUBEZY, M., FERG-ERSON, R. W., and MUSEN, M. A., “Creating Semantic WebContents with Protégé-2000,”IEEE Intelligent Systems, vol. 16,pp. 60–70, 2001.

[33] PEPPER, S., “The TAO of Topic Maps - find-ing the way in the age of infoglut,” 2000.http://www.ontopia.net/topicmaps/materials/tao.html.

[34] PEPPER, S., “Ten Thesis on Topic Maps and RDF,” 2002.http://www.ontopia.net/topicmaps/materials/rdf.html.

[35] RATH , H. H., “The topic maps handbook,” tech. rep., Empolis,2003.

[36] STOJANOVIC, L., STAAB , S., and STUDER, R., “E-LearningBased on the Semantic Web,” inWorld Conference on the WWWand Internet, (Orlando, Florida, USA), WebNet200, 2001.

[37] STUDER, R., BENJAMINS, V., and FENSEL, D., “Knowledge en-gineering, principles and methods.” Data and Knowledge Engi-neering, 1998.

[38] VATANT, B., “Owl and Topic Map pudding,” 2003.

[39] VATANT, B., “Ontology-Driven Topic Maps,” XMLEurope 2004 - Conference Proceedings, 2004.http://idealliance.org/papers/dxxmle04/papers/03-03-03/03-03-03.html.

[40] WILEY, D. A., “Connecting learning objects to instructional de-sign theory: A definition, a metaphor, and a taxonomy,” 2000.

67

[41] WILEY, D. A., “The reusability Paradox, Reusabil-ity, Collaboration, and Learning Troupe Report,” 2001.http://rclt.usu.edu/whitepapers/paradox.html,2001.

[42] WILEY, D. A., “Learning objects: difficulties and opportunities,”2003.

[43] WRIGHTSON, A., “Topic Maps and Knowledge Representation,”2001.

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