1. e-Learning Technologies on the Semantic Web
31-Mar-17
[Title of the course]
e-Learning Technologies on the Semantic Web
Rachid Benlamri
Chair, Dept. of Software Engineering
Lakehead University
Canada
For Contact
King Abdulaziz University - Joint Supervision Program – Oct , Jeddah – KSA
Copyright © NameOfTheOrganization. All rights reserved.

2. 2. What does Semantic Web bring 3. How it all fits together?
31-Mar-17
[Title of the course]
Outline
1. Motivation
Historical view, Problems, Goals & Vision
2. What does Semantic Web bring
to e-learning?
Semantic Markup: LOM, RDF, Ontology Rule-Markup: Rule-ML, SWRL,
Web Services, Web Agents
3. How it all fits together?
Case Study: Context-Aware M-Learning
Use the Course summary slide to recap the most important points of the course. Keep the points short and easy to remember.
Summarize the content in short sentences or phrases. If a summary item is more than a few lines long, left align its text.
Compare your Course summary slide to your Course objectives slide. The summary should not just restate the objectives, but should contain the key points necessary to accomplish those objectives.
If you have more than five or six important points, consider breaking the course into multiple courses or reducing the coverage of the course.
4. Conclusions
Demos
Copyright © NameOfTheOrganization. All rights reserved.

3. Objectives This presentation is intended to provide you with
research motivations in Semantic Web-based
technologies
an understanding of the basic requirements of service-oriented learning systems
an understanding of what Semantic Web can bring to e-learning
methods for context modeling and management for e-learning
a system prototype for context-aware mobile-learning
a system demo for collaborative/social learning (Unite Software) 3 3

4. Part 1
Motivation
Historical view
Problems
Goals & Vision 4 4

5. e-Learning History
Traditional Learning
Multimedia-Based
Learning

6. Early Web-Based Learning
e-Learning History (Cont’d)
Early web: Simple web pages, Word, Excel, pdf files managed by simple databases of learning content (markup)
Markup
WWW
Early Web-Based Learning
Multimedia-Based
Learning

7. Early Web-Based Learning
31-Mar-17
[Title of the course]
e-Learning History (Cont’d)
Learning Management Systems: WebCT, Blackboard, Domino, etc. (markup and XML)
Educator-driven
Linear access
Time- and location-dependent
Learning aimed at mass participation
Mark-up + XML
WWW
Early Web-Based Learning
Web-based
e-Learning Systems
Copyright © NameOfTheOrganization. All rights reserved.

8. Learning Object based Systems
e-Learning History (Cont’d)
Learning Object Systems: SCORM, ADL, IMI, OCPI, etc.,
(XML-based Metadata + Web Services + Standards )
Can access any LO
Build learning paths out of LOs
LO-based metadata for
indexing and retrieval
Share
Discover
XML – LOM -SWDL- SOAP
Web Services
Web-based
e-Learning Systems
Interoperability
Reuse
Learning Object based Systems

9. Learning Objects based Systems
Vision: e-Learning on the Semantic-Web
Confluence of enabling technologies: Web Agents, Ubiquitous Computing, Ontologies, Web Services, and Open Standards
Scalable Service Oriented Systems
Share
Reasoning
Discover
WSDL-SOAP
Web Services
Agents …
Ontologies
OWL-SWRL
Semantic Web
Internet 2
Interoperability
Reuse
Learning Objects based Systems
Adapt to Context
Semantic Web-based
e/m/u-Learning Systems

10. Confluence of Enabling Technologies
Ontologies
E-learining on
Semantic Web
Agents
Web Services
Shared conceptualizations
Explicite Knowledge spec.
Semantic Annotation
Contextualization
Community creation
Proactivity
Service composition
Semantically well-defined
context-aware dynamic services
Interoperability
Service discovery
Service invocation
Knowledge Management
Reasoning Tools

11. Information-Transfer
based Learning
Information transfer
Online repositories
Teachers produce and learners consume
Passive learners
Learner cannot impact the learning process
No Personalization
Uniform Learning
All learners learn same way
No contextualization
System is unaware of learner situation
Knowledge-Transfer
based Learning
Knowledge transfer
Knowledge-driven framework
Semantically annotated content
Sharing, reusing, and reasoning
Active learners
Learner has impact on the learning process
Personalized Learning
Tailored to the learner’s needs, background, and learning style
Contextualized Learning
Adapted to learner’s situation (current activity, task at hand, and surronding environment)

12. Learning Models: How we Conceive the Learning
Contextualised learning
Understanding of concepts through direct experience of their manifestation in realistic contexts (e.g. providing access to real world data)
Social learning
User’s mental processes are influenced by social and cultural contexts
Collaborative learning
More than a simple information exchange – peers interactions, conversation tracks, knowledge reconstruction
Personalised learning
Guarantee the learner to reach a cognitive excellence through different learning path tailored to learner’s needs and preferences
These are enabling approaches to learning
[Ritrova 2005]

13. Requirements of future e-Learning Systems
Emerging web technologies provide new exciting horizons for building smart distributed e-learning environments
Semantic markup and reasoning
Learning resources from different sources can be linked to commonly agreed ontologies
Powerful semantic querying to build personalized learning paths
Open standards for resource sharing and reuse
Service orientation
Learning is conceived as support services
The learning process is enabled by composing services
Context-awareness
Ability to recognize learner’s current context (activity, place, device)

14. Requirements of future e-Learning Systems(2)
Learner-centric
Dynamically optimized learning, on demand, and on a learner's schedule and context.
Appropriate semantic annotation will still define pedagogical sequences in the learning hierarchy (core, prerequisites, electives, etc...)
Community and collaboration based
Is central for all kinds of formal and informal learning
Autonomous and dynamic creation of communities
Dynamicity
the learner can influence the process
the social and context aspects influence the learner

15. Requirements of future e-Learning Systems(3)
High demand of interoperability
access to resources on heterogeneous environments
Security and trust
Confidentiality, privacy, copyright issue, etc...
Simplify
Learning content authoring
Application Development
Metadata exchange

16. What Semantic Web Brings to e-Learning
Part 2
What does Semantic Web bring
to e-Learning?
Learning Object Metadata (LOM)
Semantic Markup (RDF, RDF-S, OWL, OWL-S)
Rule Markup Languages (Rule-ML and SWRL)
Web Services & Web Agents
What Semantic Web Brings to e-Learning

17. Semantic Web - Definition
The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in co-operation [Berners-Lee et al., 2001]

18. Semantic Web Layers (T. Berners-Lee et al.)

19. First Step Towards SW Learning: Augmenting Learning Objects with Metadata
Learning Object : A re-usable chunk of information used as a modular building block for e-Learning content.
Metadata (Data about data)
Structural data about Learning Objects (LOs), to augment them with a meaningful classification so that they can be easily reused, transformed, accessed, etc., in order to gain more information on demand.
IEEE LTSC LOM (Learning Object Metadata)
XML-based Metadata

20. Role of Metadata learning content on the Web
SW-techniques allow you to add metadata to distributed resources just like html allows you to link to such resources.
Metadata allows Learning Management Systems (LMS) to:
Annotate
Find
Select
Retrieve
combine
use/re-use, and
share
learning content on the Web
Metadata is not bound to a fixed schema. You may invent a description format of your own and add personal annotation

21. Sample of Metadata The display type of a device
The topic of a of a lecture
The size of a learning resource
The author of a learning resource
The operating system to execute a program

22. Metadata Standards Often, standards go through three stages:
Technical Specifications such as AICC, IMS, ARIADNE
Implementation Reference Models such as ADL SCORM (sharable content object reference model)
Accredited Standards such as IEEE LOM (Learning Object Metadata)

23. LO-based Learning Systems The SCORM Model
Source from ADL’s SCORM

24. Is Metadata Enough for Sharing Resources on the SW?
No metadata standard covers every aspect of life
They just provide information about the packaged learning content
Different communities develop different vocabularies (ontologies), but they should be able to use each others
XML is not good at describing vocabulary
There is a need for a language with semantic markup to describe shared vocabularies

25. Why not use XML to represent ontologies?
XML documents are labeled trees
Storage is done just like an n-ary tree (DOM)
Tree element = label + Attribute/Value + content
Document Type definition (DTD): Simple grammar (regular expressions) to describe legal trees (XML-Schema )
It says what elements and attributes are required or optional.
course
<course Name=“...”> <Lectures>...</Lectures> <Exams> <MidTerm>...</MidTerm>
<Final>...</Final>
</Exams>
<Projects>...</Projects> </course>
Projects
Lectures
Exams
This material is not covered in the book.
MidTerm
Final
Why not use XML to represent ontologies?

26. XML: Limitations for Semantic Markup
XML is a universal metalanguage for defining markup
It provides a uniform framework for interchange of data and metadata between applications
However, XML is unspecific:
- No predetermined vocabulary
- No semantics for relationships
XML does not provide explicit semantics (meaning) of data
E.g., there is no intended meaning associated with the nesting of tags
It is up to each application to interpret the nesting.
XML is not for sharable Web-resources on a broad scale
So, there is a need for other form of semantic markup

27. Resource Description Framework (RDF) for Semantic Markup
RDF provides metadata about Web resources
Basic building block:
Subject -> Predicate -> Object triples
subject is the focus of the statement
predicate describes a property of the subject
property value is the object.
So, RDF keeps meta-data external to objects
It has an XML syntax
Chained triples form a graph (semantic net)
site-owner
Benlamri
author

28. RDF’s Resources
Every resource has a URI, a Universal Resource Identifier
A URI can be
a URL or
unique identifier
We can think of a resource as an object, a “thing”. So, RDF URI’s can refer to anything and not just digital resources (e.g. lecturer, author, student, device, etc.)
So, RDF, is extendable and doesn’t require rigid meta-data structures or proprietary standards or fixed vocabularies

29. Properties Properties describe relations between resources
e.g. “author”, “prerequisite”, etc.
Properties are also identified by URIs (kind of resources)
Advantages of using URIs:
Α worldwide, unique naming scheme
Reduces the problem of distributed data representation

30. Statements Statements assert the properties of resources
A statement is an subject-predicate-object
It consists of a resource, a property, and a value
Values can be resources or literals
Literals are atomic values (strings)
The triple (x,P,y) can be considered as a logical formula P(x,y)
Binary predicate P relates object X to object Y
RDF offers only binary predicates (properties)

31. What does RDF Schema add?
Defines vocabulary for RDF
Organizes this vocabulary in a typed hierarchy
Class, subClassOf, type
Property, subPropertyOf
domain, range
Person
PhDStud
Professor
subClassOf
type
hasSuperVisor
domain
range
hasSuperVisor
York
Rudi
[Steffen Staab 2006]

32. Basic Ideas of RDF Schema
RDF is a universal language that lets users describe resources in their own vocabularies
RDF does not assume, nor does it define semantics of any particular application domain
The user can do so in RDF Schema using:
Classes and Properties
Class Hierarchies and Inheritance
Property Hierarchies

33. RDF(S) Contributions to Semantic Markup
RDF provides a foundation for representing and processing metadata
RDF has an XML-based syntax to support syntactic interoperability.
Next step up from plain XML:
(small) ontological commitment to modeling primitives
possible to define vocabulary
So, RDF & RDFS allow incremental building of knowledge, and its sharing and reuse
However, many desirable modelling primitives are missing
no precisely described meaning
no inference model
RDFS provides some KR techniques, but is still not sufficient for applying real KR to semi-structured data on the web.
how to apply KR - ontologies - to semi-structured data on the internet?
only natural language spec.
Only subsumption inference possible, no: transitivity, inverse, etc.

34. Limitations of the Expressive Power of RDF-S
31-Mar-17
Limitations of the Expressive Power of RDF-S
[Title of the course]
Example: Boolean combinations of classes
Sometimes we wish to build new classes by combining other classes using union, intersection, and complement ( e.g. person is the disjoint union of the classes male and female)
Therefore we need an ontology layer on top of RDF and RDF Schema to offer:
explicit, formal conceptualizations of domain models
efficient reasoning support
a formal semantics
sufficient expressive power
[G. Antoniou & F.Harmelen]
Copyright © NameOfTheOrganization. All rights reserved.

35. Ontology Ontology in Philosophy Eventually, what is being?
Ontology is a branch of philosophy that deals with the nature and the organization of reality
Ontology deals with questions such as:
What characterizes being?
Eventually, what is being?
“ People can‘t share knowledge if they do not speak a common language.“
[Davenport & Prusak, 1998]
Ontologies enable a better communication between Humans/Machines
Ontologies standardize and formalize the meaning of words through concepts

36. Ontology Formal Specification Conceptualization Domain of Interest
Concepts
Ontology …
Reasoning +
Processable
Formal Specification
Conceptualization of
shared of
People
Domain of
Interest
Group of
Web agents
Applications
Ontology is a formal Specification of a shared conceptualization of a domain of interest [Gruber 93]
Services
cooperation

37. Ontologies for e-Learning
In e-learning we distinguish between two types of knowledge (ontologies):
Operational Knowledge
Learner ontology
Activity ontology
Environment ontology( device + environment)
Domain Knowledge
Content
Pedagogy
Structure (navigation)

38. Example of Learner Ontology
Standards: RDF(S); OWL (Web Ontology Language)

39. Reasoning about Knowledge in Ontology
Class membership
If x is an instance of a class A, and A is a subclass of B, then we can infer that x is an instance of B
Equivalence of classes
If class A is equivalent to class B, and class B is equivalent to class C, then A is equivalent to C, too
Consistency
X instance of classes A and B, but A and B are disjoint
This is an indication of an error in the ontology

40. Reasoning about Knowledge in Ontology (2)
Reasoning support is important for
checking the consistency of the ontology and the knowledge
checking for unintended relationships between classes
automatically classifying instances in classes
Checks like the preceding ones are valuable for
designing large ontologies, where multiple authors are involved
integrating and sharing ontologies from various sources
Dealing with operational knowledge and domain knowledge in an efficient way (i.e. context-aware personalized learning)
[G. Antoniou & F.Harmelen]

41. Web Ontology Language (OWL)
OWL is a knowledge representation language to model ontologies and reason about their embedded knowledge
OWL is based on formal semantics
Semantics is a prerequisite for reasoning support
Semantics and reasoning support are usually provided by
mapping an ontology language to a known logical formalism
using automated reasoners that already exist for those formalisms
OWL is (partially) mapped on a description logic, and makes use of reasoners
Description logics are a subset of predicate logic for which efficient reasoning support is possible
[G. Antoniou & F.Harmelen]

42. OWL Standard
Three OWL versions with different expressive power and designed for different requirements:
OWL FULL (fully expressive, not fully decidable)
OWL DL (less expressive, Description logic )
OWL Lite (restricted power of expressiveness)
OWL is a knowledge representation language with rich modeling primitives:
Classes with data & object properties
Inverse and equivalence properties
Property and cardinality restrictions
Boolean combinations
Enumerations, etc…

43. SW and Rules Markup Languages Rule-ML & SWRL
The Semantic Web approach is to express the knowledge in a machine-accessible way using one of the Web languages (RDF/S, OWL, etc.)
Then, extend these knowledge representation languages with rule markup languages, also expressed in XML-like syntax
Basic Idea: shared reasoning is enabled through exchange of rules across different applications

44. Web Services – Contribution of Semantic Web Technology
Web Service: service based, aiming to provide interoperability among distributed loosely coupled components
Use machine-interpretable descriptions of services to automate:
discovery, invocation, composition and monitoring of Web services
Share web services across applications (e.g. use of Web Service Description Language - WSDL)
Web agents can compose simple web services into complex web services

45. Web Services and OWL-S
Applications should be able to employ a set of basic classes and properties by declaring and describing services: ontology of services
OWL-Schema can be used for developing an ontology language for Web services
It can be viewed as a layer on top of OWL
[G. Antoniou & F.Harmelen]

46. Three Basic Kinds of Knowledge Associated with a Service
Service profile
Description of the offerings and requirements of a service
Important for service discovery
Service model
Description of how a service works
Service grounding
communication protocol and port numbers to be used in contacting the service

47. Web Agents – Contribution of Semantic Web Technology
Software agents may represent dynamic entities in a Learning Management System
Shared ontology(es) are used for mutual understanding of terms and embedded knowledge
LMS Facts w.r.t. learners, learning process, environment, etc., and subsequent LMS decisions are represented by statements

48. Web Agents – Contribution of Semantic Web Technology (2)
Information is exchanged between Agents in a Markup language
Agent negotiation strategies are described in a logical language
Agents decide about next course of action through inference, based on negotiation strategy and current facts

49. How it all fits together?
Part 3
How it all fits together?
Case Study
Context-Aware M-Learning
On the Semantic Web
Web 49

50. Yesterday: Gadget Rules
Too bad they can’t talk to each other…
Cool toys…
[Harry Chen]

51. Today: Communication Rules
Configuration?
Too much work…
Sync. Download. Done.
[Harry Chen]

52. Tomorrow: Services Will Rule
Thank God! Pervasive Computing is here.
[Harry Chen]

53. Context-Aware Systems
“Systems that can anticipate the needs of users and act in advance by “understanding” their context.” [Chen 2003]
“Systems that are able to adapt their operations to the current context without explicit user intervention. Context –awareness here aims at increasing usability and effectiveness by taking environmental context into account.”
[Dustdar 2006]

54. Context - Definition [Dey and Abowd’s ]
“Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves.”
[Dey and Abowd’s ] 54

55. Context-Awareness in e-Learning Research Challenges
Context Modeling & Reasoning for
e- learning
How to build representations of context that can be processed, and reasoned about by the used devices
(2) Knowledge Maintenance & Sharing
How to maintain consistent knowledge about the context and share that information with other learning services
Possible Solution
Use SW ingredients to solve (1) & (2)

56. Ontology–based Context Models
Need for knowledge sharing
Knowledge sharing => Global Ontology spaces to enhance cross-domain understanding
Use OWL as a meta-language to define other languages that are used in context-aware systems
Policy languages for privacy and security
Content languages for agent communications
Use the ontology semantics of OWL+ SWRL (SW Rule Language) to reason about context
Deduce context knowledge that can’t be directly acquired from the sensors
Detect and resolve inconsistent knowledge that results from imperfect sensing
Infer new knowledge for service adaptation

57. Context Awareness Pyramid
Context Understanding & Usage
Semantics/Understanding/Insight
Expressiveness
Context Information
Context Perception
Context Acquisition (World)
Sensory Data 57

58. Context Awareness Pyramid Conceptual Model
Raw-Context Data
Context Sources
Atomic Context Sensor
Context Sensing Layer
Context Reasoning and Service Adaptation
Context Inference
Context Learning
Context Perception Layer
Context Understanding and Usage Layer
Context Computation 58

59. What is Atomic Context ? External (Sensed)
Context that can be measured by hardware sensors
location, light, sound, temperature, air pressure, blood pressure, heart rate, etc.
Internal (Retrieved from UI / Profiled )
Mostly specified by the user or captured monitoring the user’s interaction
the user’s goal, tasks, work context, business processes, etc.
Retrieved from profiles of users, tasks, schedule, etc.

60. Sensing Atomic Context
Sensors
Physical sensors
sensor, camera, microphone, accelerometer, GPS, thermometer, biosensors
Virtual sensors
From software: browsing an electronic calendar, a travel booking system, s, mouse movements, keyboard input
Logical sensors
Combination of physical and virtual sensors with additional information from databases: analyzing logins, mapping fixed devices to location information, etc.
Sensory Data Capture
Drivers and APIs
Query functionality (ex: getPosition())
Etc.
[Dustdar 2006]

61. Modeling Atomic Context: Context Atom Attributes
Context type (Nature of context)
Context value (Quantized / non quantized( boolean, literal) )
Description (Symbolic description for high level reasoning)
Time stamp (at acquisition time)
Source (Sensor ID)
Confidence (Truth probability)

62. Context Awareness Pyramid
Data Pyramid
Context Awareness Pyramid
Context Understanding & Usage
Semantics/Understanding/Insight
Expressiveness
Context Information
Context Perception
Context Acquisition (World)
Sensory Data 62

63. Context Perception Involves monitoring and simple recognition
Links sensors to network and network-centric services.
Enabled through:
Reasoning and interpreting
Computation
Extraction and quantization operations
Aggregation or compositing of atomic context elements
Learning techniques
Statistical methods where training phase is required
To deal with ambiguous context

64. Context Awareness Pyramid
Data Pyramid
Context Awareness Pyramid
Service layer
Semantics/Understanding/Insight
Context Understanding & Usage
Expressiveness
Context Information
Context Perception
Context Acquisition (World)
Sensory Data 64

65. Context Understanding and Usage
Semantic Analysis of Context Involves:
Reasoning: support of axiomatic rules (e.g. expressed in SWRL)
Interpretation
Recognition, and
Evaluation
Goal:
Awareness of the likely evolution of a situation,
Identify its possible/probable future states and events
Service adaptation to deal with the new situation

66. Context Awareness in M-learning66 66

67. Challenges & Contributions
Challenges : Today’s Learning Technology is characterised by:
Passive components rather than active components
Separate use of contextual information on users, environment, and services
Lack of techniques for modeling and reasoning with the global context
Contributions
Proactivity: context sensing, prediction, & management
Knowledge Representation: Ontological approach for context integration and aggregation
Knowledge Management: Hybrid approach that may combine probabilistic, logic, and rule based reasoning 67 67

68. System Architecture 68 68

69. Context Aggregation 69 69

70. ConductedLearningActivity
Global Ontology Space
Learner Ontology
Domain Ontology
Device Ontology
Activity Ontology
Environment Ontology
Concept
Learner
HasCovered
Query
HasKeyword
Environment
HasSurrounding
Learning Resource
Consumed
Learning
Resource
Learning Activity
ConductedLearningActivity
Device
UsedDevice
IsMappedTo
MakeQuery
HasLearning
Goal
LocatedAt
Location
HasLocatiom 70 70

71. Domain Ontology 71 71

72. Learner Ontology 72 72

73. HasWirelessNetworkType
Environment Ontology R
Data
Property D
Object
HasLocation
HasBandwith
HasWirelessNetwork
Exte
rnal
XSD:
Float
External
XSD: Date_Time
Class
WirelessNetwork
Learner
Environment
Location
Prope
rty
HasSur
rround
ingEnvironment
SensedAt XS
D: Boolean
WirelessNetworkT y pe
IsSecured
HasWirelessNetworkType
LocatedAt 73 73

74. Device Ontology 74 74

75. Activity Ontology 75 75

76. Subject Domain Ontology:
C++ Programming 76

77. Ontology Reasoning 77

78. System-Centric Adaptations
<<New Query>>
User
Web Borrower
HTML
WAP Borrower
WML
Sensing Type of Network Connection & Maximum Connection Speed
Sense & Update Actual Network Bandwidth
Compute Maximum Resource Size
Adapt Retrieved Resources to Device Features
Infer Current Bandwidth
Infer Media
Type
Global Ontology Space
Device Repository
Environment Repository
System Centric Adaptation
<<Login>> 78 78

79. Inferring Current Bandwidth
Infer Current Bandwidth based on
Previously sensed bandwidth
Fuzzy Membership Function
SWRL Rules to Compute Truth Values 79 79

80. Resource-Size & Media-Type Selection
Resource Size & Media Type Rules
Resource Size Computation
Knowledge base for the fuzzy logic system:
if bandwidth is Low then set file size to Small.
if bandwidth is Medium then set file size to Medium
if bandwidth is High then no file size restriction 80

81. SWRL Rules for Fuzzy Logic Reasoning
MediaTypeSelectionRule-1
ActivityID(?a)∧UsedDevice(?a,?y)∧NetworkBandwidth(?y,"Low") → HasMediaType(?y, Text)
MediaTypeSelectionRule-2
ActivityID(?a)∧UsedDevice(?a, ?y)∧NetworkBandwidth(?y, "Medium")→HasMediaType(?y, Text)∧
HasMediaType(?y, Image)
MediaTypeSelectionRule-3
ActivityID(?a)∧UsedDevice(?a, ?y)∧NetworkBandwidth(?y, "High") →HasMediaType(?y,Text)∧ HasMediaType(?y,Image)∧ HasMediaType(?y,Video)
ResourceSizeRule-1
ActivityID(?a)∧UsedDevice(?a, ?y)∧ProbLow(?y, ?Tl)∧ProbMedium(?y, ?Tm)∧ProbHigh(?y, ?Th)∧
MaxSize(?y, ?Maxsize) ∧swrlb:multiply(?Lowsize, 0.25, ?Maxsize)∧swrlb:multiply(?Mediumsize, 0.5, ?Maxsize)∧
swrlb:multiply(?Largesize, 0.75, ?Maxsize)∧swrlb:multiply(?l, ?Lowsize, ?Tl)∧
swrlb:multiply(?m, ?Mediumsize, ?Tm)∧swrlb:multiply(?h, ?Largesize,?Th)∧ swrlb:add(?z1,?l,?m, ?h)∧
swrlb:add(?z2, ?Tl, ?Tm, ?Th)∧swrlb:equal(?z2, 1)∧swrlb:divide(?z, ?z1, ?z2) → FileSize(?y, ?z)
AllowedResourceSizeRule-1
ActivityID(?a)∧UsedDevice(?a, ?y)∧FileSize(?y, ?Size)∧ AvailableMemory(?y,?MemorySize)∧ swrlb:lessThan(?MemorySize,?Size) →AllowedSize(?y,?MemorySize)
AllowedResourceSizeRule-2
ActivityID(?a)∧UsedDevice(?a,?y)∧FileSize(?y,?Size)∧AvailableMemory(?y, ?MemorySize)∧ swrlb:greaterThanOrEqual(?MemorySize, ?Size)→AllowedSize(?y,?Size) 81

82. Other System-Centric Adaptations
Selecting Resources based on Device Features
Language Rule
O/S-Compatibility Rule
LanguageRule-1
ActivityID(?a)∧UsedDevice(?a,?y)∧PreferredLanguage(?a, ?z)∧
HasSupportLanguage(?y,?z) → SearchLanguages(?a, ?z)
SystemCentricRule-1
SearchLanguages(?y,?z)∧ExpressedIn(?LR,?z)∧ UsedDevice(?y, ?D)∧ HasMediaType(?D,?b) ∧HapType(?LR,?b)∧HasOS(?D,?c)∧ RunsOn(?LR,?c) → SystemCentric(?y, ?LR) 82

83. Learner-Centric Adaptations
Objective: Generate a learning path tailored to the learner’s
needs, background, and task at hand.
Type of Adaptation
Adaptation Process
1. Current task
1 - Get similar knowledge (Isa)
2 – Check for prerequisite knowledge (HasPrerequisite)
2. Learner needs
1- Get core knowledge (HasNecessaryPartOf)
2- If time permits then get related knowledge (HasPartOf)
3. Learning history
Infer covered resources
1 - Infer covered concepts (HasCovered)
2 – Infer consumed learning resources (HasConsumed) 83 83

84. Current Task Adaptation
Inference of Similar Learning Knowledge
Inference of Prerequisite Learning Knowledge
SimilarLearningResourceRule-1:
ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C)
IsMappedTo(?C,?LR) → SimilarLR(?a,?LR)
SimilarLearningResourceRule-2:
ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C)Has(?C,?Ci) ¬Covered(?L,?Ci) IsMappedTo(?Ci,?LRi)¬Consumed(?L,?LRi) → SimilarLR(?a,?LRi)
SimilarLearningResourceRule-3:
ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C) Isa(?C,?Ci)
¬ Covered(?L,?Ci)IsMappedTo(?Ci,?LRi) ¬Consumed(?L,?LRi) → SimilarLR(?a,?LRi)
PrerequisiteLearningResourceRule-1:
ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C) HasPrerequisite(?Q,?Ci) ¬covered(?L,?Ci) IsMappedTo(?Ci,?LRi)  ¬Consumed(?L,?LRi) → PrerequisiteLR(?a,?LRi) 84

85. Learner Needs Adaptation
Inference of Core Learning Knowledge
Inference of Related (non-core) Learning Knowledge
CoreLearningResourceRule-1 :
ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q)  HasKeyword(?Q,?C)  HasNecessaryPart(?Q,?Ci)  ¬Covered(?L,?Ci)  IsMappedTo(?Ci,?LRi)  ¬ Consumed(?L,?LRi) → CoreLR(?a,?LRi)
CoreLearningResourceRule-2 :
ConductedLearningActivity(?L, ?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C)IsNecessaryPartOf(?Q,?Ci) ¬Covered(?L,?Ci)IsMappedTo(?Ci,?LRi)  ¬Consumed(?L, ?LRi) → CoreLR(?a,?LRi)
NonCoreRelatedLearningResourceRule-1: ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C) HasPart(?Q,?Ci)  ¬Covered(?L,?Ci)  IsMappedTo(?Ci,?LRi)  ¬Consumed(?L,?LRi) → NonCoreRelatedLR(?a,?LRi)
NonCoreRelatedLearningResourceRule-2:
ConductedLearningActivity(?L,?a) MakeQuery(?a,?Q) HasKeyword(?Q,?C) IsPartOf(?Q,?Ci)  ¬Covered(?L,?Ci)  IsMappedTo(?Ci,?LRi)  ¬Consumed(?L,?LRi) → NonCoreRelatedLR(?a,?LRi) 85

86. Example of Learner-Centric Adaptations
Assumptions:
- Irene’s query: C++ Loops
- Covered Concept by Irene: (Do-While)
Recommended Learning Sequence:
- Step 1: Learning Path (Ignoring Learning History) (C++ Loops) → (While) → (Do-While) → (For) → (Looping)
- Step 2: Final Learning Path (C++ Loops) → (While) → (For) → (Looping) 86

87. System Implementation87 87

88. System-Centric Adaptations88

89. Learner-Centric Adaptations89

90. Learner-Centric Adaptations90

91. Conclusions
Ontologies provide a shared understanding of a domain, hence allowing semantic interoperability
Ontologies are useful for improving the accuracy of searches for learning resources that are tailored to the learner’s needs, background, and environment
search engines can look for resources that refer to a precise learner’s context
SW provides a learning infrastructure where knowledge, organized in conceptual spaces (based on its meaning) can be semantically queried, discovered, and shared across applications.

92. Conclusions (2)
SW allows learning applications to share services without much overhead. Learning services across applications can be integrated by resolving differences in terminology through mappings between ontologies
SW allows expressing knowledge in well-understood formal semantics
Automated reasoners can deduce (infer) conclusions from the given knowledge
Logic can be used to uncover ontological knowledge that is implicitly given
It can also help uncover unexpected relationships and inconsistencies
Logic can also be used by intelligent agents for making decisions and selecting courses of learning action

93. Conclusions (3) SW provides Web agents with:
Agent communication languages
Formal representation of intentions (negotiation strategies)
Logic to reason based on current facts and negotiation strategies
Thus, e-learning systems will use Semantic Web-based Knowledge Systems as key parts of everyday learning cycles

94. Thank you Questions?

95. Acknowledgements & References
RDF Primer ,
OWL,
Web Services,
G. Antoniou & F. Harmelen, A Semantic Web Primer, 2nd Edition (Cooperative Information Systems) , MIT Press, 2008.
Terence Love et al., The Future of e-Learning: Inclusive learning objects using RDF.
Andreas Hotho & York Sure, A Short Semantic Web Tutorial, Knowledge Management Group Institute AIFB, University of Karlsruhe
Steffen Staab, Querying OWL Ontologies, 2007,
Steffen Staab, Ontologies and the Semantic Web, 2006,
Harry Chen, Semantic Web in a Pervasive Context-Aware Architecture,