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Learning Object Mediation System Based on an Ontology Model

Suphakit NIWATTANAKUL1, Philippe MARTIN2, Michel EBOUEYA1, Kanit KHAIMOOK3

1Informatics, Image and Interaction Research Laboratory, University of La Rochelle, France 2School of Information and Communication Technology, Griffith University, Australia

3School of Information Technology, Suranaree University of Technology, Thailandsuphakit.niwattanakul@lifc.univ-lr.fr phmartin@phmartin.info michel.eboueya@univ-lr.fr kkanit@sut.ac.th

The Fourth International Conference on eLearning for Knowledge-Based Society18-19 November 2007, Bangkok, Thailand

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

Outline

1. Introduction

2. Methodology1. Ontology model2. Learning Object Repositories3. SKOS (Simple Knowledge Organisation System)4. SPARQL Query Language5. Fuzzy Logic

3. Learning Object Mediation System

4. Classification of Learning Object Similarity5. Conclusion

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

1. Introduction – Using of digital resources

�The use of digital resources is increasingly in education focused on course development and content management

�The need of using digital learning resources is to support learning anywhere at any time

�The purpose of the reuse of digital resources is to combine of units of content – learning objects – for a new course

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

1. Introduction – What are Learning Objects?

�A Learning Object is “any digital resource that can be used and reused for support learning” (IEEE LCST, 2002; Wiley, 2002)

�Learning Objects (LOs) may be text, presentation, quizzes, video clips, tutorials, animations, photographs, map and so on.

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

1. Introduction – Searching for LOs

�Searching for LOs from the vast array available is time-consuming and a huge amount of searching results

�Propose Learning Object Repositories (LORs) for:�Collect LOs on the Internet and/or their

metadata �Provide functions to search for LOs in their

repositories

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

1. Introduction – Searching by keywords

�Searching for LOs in LORs based on keywords may lead to the problem of retrieving for LOs which are in the same particular domain but they are described in different keywords

K1

K2

LO1

LO2

Suppose: Keywords “K1” and “K2” are in the same particular domain

Search by “K1” � LO1

Search by “K2” � LO2

How search by “K1” or “K2” � LO1 and LO2

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

1. Introduction – Proposed system

�Propose a Learning Object (LO) Mediation system: �Using an existing LOR tool to store LOs and/or

their metadata and extracting some information of LOs into a mediator based on an ontology model

�Provide functions to search LOs by keywords and concepts

�Provide functions to classify similarity of LOs by Fuzzy Logic

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

1. Introduction – Propose to search by concepts

� In our system, a concept is �a set of words or phrases which are the same

meaning �a set of keywords which are in the same

particular domain

�Search LOs by concepts

K1

K2

LO1

LO2

Suppose: Keywords “K1” and “K2” are in the same particular domain or concept

Search by “K1” � LO1, LO2

Search by “K2” � LO1, LO2

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

Outline

1. Introduction

2. Methodology1. Learning Object Repositories2. Ontology model

3. SKOS (Simple Knowledge Organisation System)4. SPARQL Query Language5. Fuzzy Logic

3. Learning Object Mediation System4. Classification of Learning Object Similarity

5. Conclusion

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

2. Methodology

Store learning objects and/or their metadata

Learning Object Repository

Classify similarity of learning objects

Fuzzy Logic

Query language for an ontology model

SPARQL Query Language for RDF

Concept organisation (classification, indexation, annotation)

SKOS (Simple Knowledge Organisation System)

Define a structure for collecting learning objects

Ontology model

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

2.1 Learning Object Repositories (LORs)

�A Learning Object Repositories (LOR) is a systems that stores LOs on the Web and/or their metadata�For the serving of searching and retrieving LOs

on the Internet�For the structure of metadata based on an

ontology model

�An example of LOR tool: PALOMA

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

2.2 Introduction – An ontology model

�The use of ontology is for machine readable, explicitly defined and sharable.

�An ontology model consists of�Concept or class, property, relation, individual

or instances

Country

name

City

located_in

capital

Geographical Location

name

Land Boundary

neighbor_countrypart_of

has_boundary Classes/Concepts

Properties

Relationships

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

2.3 SKOS (Simple Knowledge Organisation System)

�SKOS stands for Simple Knowledge Organisation System

�Provides a model for expressing the basic structure and content of concept schemes such as subject-heading list, taxonomies

http://www.w3.org/TR/2005/WD-swbp-skos-core-guide-20051102/

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

2.3 SKOS (Simple Knowledge Organisation System)

Source: http://www.w3.org/TR/2005/WD-swbp-skos-core-guide-20051102/

An example of concept description by SKOS (Simple Knowledge Organisation System)

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

2.4 SPARQL Query Language for RDF

�An RDF query language �Allow for a query on triple patterns :

Subject, Predicate and Object�Example of SPARQL language

http://www.w3.org/TR/rdf-sparql-query/

PREFIX skos: <http://www.w3.org/2004/02/skos/core#>

SELECT ?concept ?label

WHERE

{ ?concept skos:prefLabel ?label }

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

2.5 Fuzzy Logic

�Fuzzy Logic (FL) is a multivalued logic that allows intermediate values to be defined between conventional evaluations.

�For example: the member of climate consists of “Very cold”, “Cold”, “Warm”, “Hot” and “Very hot”

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

Outline

1. Introduction

2. Methodology1. Learning Object Repositories2. Ontology model3. SKOS (Simple Knowledge Organisation System)4. SPARQL Query Language5. Fuzzy Logic

3. Learning Object Mediation System

4. Classification of Learning Object Similarity5. Conclusion

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

3. LO Mediation System – Level 1

Concept repository

Classification Indexation Annotation

Learning Object Mediator

LOM SCORM �

Level 3

Level 2

Level 1

Learning Objects

Classifying learning objects by keywords and concepts

Extracting keywords and conceptsLearning Object Repository

�Level 1: Learning Object Repository �Collect LOs and/or their metadata based on

IEEE LOM. �Use an existing LOR tool such as PALOMA

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

3. LO Mediation System – Level 2

Concept repository

Classification Indexation Annotation

Learning Object Mediator

LOM SCORM �

Level 3

Level 2

Level 1

Learning Objects

Classifying learning objects by keywords and concepts

Extracting keywords and conceptsLearning Object Repository

�Level 2: Learning Object Mediator�Describe LOs by keywords and concepts

based on an ontology model

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

3. LO Mediation System – Level 3

Concept repository

Classification Indexation Annotation

Learning Object Mediator

LOM SCORM �

Level 3

Level 2

Level 1

Learning Objects

Classifying learning objects by keywords and concepts

Extracting keywords and conceptsLearning Object Repository

�Level 3: Concept repository �Define concepts by SKOS and WordNet �Annotate, index and classify their concepts

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

3. LO Mediation System – An ontology structure

�Use skos:Concept class and their properties from SKOS model for classify concepts

skos:Concept

Keywords LearningObjects

prefKeyword

isDomainConceptOf

domainConcept

desKeyword

isDesKeywordOf

skos:subject

skos:isSubjectOf

Dublin Core MetadataisWordListOf

wordList

prefIndWord

altIndWord

IndexWords

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

3. LO Mediation System – An ontology structure

�Add Keywords class and their properties for collect keywords

skos:Concept

Keywords LearningObjects

prefKeyword

isDomainConceptOf

domainConcept

desKeyword

isDesKeywordOf

skos:subject

skos:isSubjectOf

Dublin Core MetadataisWordListOf

wordList

prefIndWord

altIndWord

IndexWords

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

3. LO Mediation System – An ontology structure

�Add IndexWords class and their properties for collect index words

skos:Concept

Keywords LearningObjects

prefKeyword

isDomainConceptOf

domainConcept

desKeyword

isDesKeywordOf

skos:subject

skos:isSubjectOf

Dublin Core MetadataisWordListOf

wordList

prefIndWord

altIndWord

IndexWords

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

3. LO Mediation System – An ontology structure

�Add LearningObjects class and their properties and use Dublin Core Metadata for describe learning objects

skos:Concept

Keywords LearningObjects

prefKeyword

isDomainConceptOf

domainConcept

desKeyword

isDesKeywordOf

skos:subject

skos:isSubjectOf

Dublin Core MetadataisWordListOf

wordList

prefIndWord

altIndWord

IndexWords

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

3. LO Mediation System – Define Concept

�Use words or phrases and annotation from WordNet dictionary to define concepts

Source: http://wordnet.princeton.edu/

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

3. LO Mediation System – Define concepts by SKOS

computer_science

applied_science

computer science

the branch of engineering science that studies (with the aid of computers) computable processes and structures

computing

skos:Concept

skos:altLable

skos:prefLable

skos:definition

skos:narrowerskos:broader

rdf:type

prefix skos: <http://www.w3.org/2004/02/skos/core#>

An example of the annotation and classification of

“computer_science” concept by using annotations from

WordNet

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

Outline

1. Introduction

2. Methodology1. Ontology model2. Learning Object Repositories3. SKOS (Simple Knowledge Organisation System)4. SPARQL Query Language5. Fuzzy Logic

3. Learning Object Mediation System

4. Classification of Learning Object Similarity5. Conclusion

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

4. Classification of Learning Object Similarity

�Propose a method for searching LOs and Classifying their similarity by Fuzzy Logic

�This method consist of three steps:�Searching for index words�Computing probability values of LO similarity�Classifying similarity of LOs with Fuzzy Logic

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

4. Classification of Learning Object Similarity

�Searching for index words�Use index words instead of input words from user

�Ex: time or timed or times � time

�Use the Jaccard’s coefficient for defining similarity between index words and input words

rqp

p

B) ,AP( )BP(A, B)P(A,

B)P(A,

B) P(A

B) P(A B)sim(A,-Jaccard

++

=

++

=

∪

∩=

p q r

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

4. Classification of Learning Object Similarity

�Computing probability values of LO similarity� Searching in three categories

�Keywords by index words�Concepts by keywords�Concepts based on WordNet dictionary by index

words

�Searching for LOs in each category �Computing probability values for each LO

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

4. Classification of Learning Object Similarity

�Classifying similarity of LOs with Fuzzy Logic�Use the probability values of each LO in three

categories from previous step�The fuzzy set of each categories contains

“Less”, “Same” and “More”�Define rules for the classification of LO

similarity into 7 classes

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

Outline

1. Introduction

2. Methodology1. Ontology model2. Learning Object Repositories3. SKOS (Simple Knowledge Organisation System)4. SPARQL Query Language5. Fuzzy Logic

3. Learning Object Mediation System

4. Classification of Learning Object Similarity5. Conclusion

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

5. Conclusion

�Learning Object (LO) Mediation system consists of�Learning Object Repository (LOR)�Learning Object (LO) Mediator�Concept Repository

�LOR is based on an existing LOR tool in order to collect LOs and their metadata

�LO Mediator describes and classifies LOs by keywords and concepts

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

5. Conclusion (cont.)

�Searching LOs is based on keywords and concepts

�LO Mediator provides function to classify LO similarity�Compute probability values of LOs �Classify similarity of LOs by Fuzzy Logic

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion

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� Kong, C. Y., Wang, C.L. and Lau, F. C. M. (2004) Ontology Mapping in Pervasive Computing Environment International Conference on Embedded and Ubiquitous Computing (EUC-04). pp. 1014-1023. Aizu, Japan, 26-28 August 2004.

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� Niwattanakul, S., Martin, Ph., Eboueya, M. and Khaimook, K. (2007) Ontology Mapping based on Similarity Measure and Fuzzy Logic . In Proc. of the World Conference on E-Learning in Corporate, Government, Healthcare & Higher Education (E-Learn 2007). 15-19 October 2007. Quebec City, Canada.

� Paquette, G., Marino, O., Lundgren-Cayrol, K., Léonard, M. and Teja, I. de la. (2006) Learning Design Repositories – Structure Ontology and Processes . In Proc. of International Workshop of Learning Networks for Lifelong Competence Development. pp. 18-22. March 30-31, 2006. Sofia, Bulgaria.

� Rodriguez, W. (2000). Fuzzy Logic Based Voice Processing . In Proc. of The International conference on: Modeling, Simulation and Neural Network (MSNN-2000). October 22-24, 2000. Mérida, Venezuela, 2000. Available on: http://iies.faces.ula. ve/Amse2000/papers/fuzzy/MSNN2000Wladimir.pdf

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Introduction Methodology LO Mediation System Classification of LO Similarity Conclusion