37

CHAPTER 4

ONTOLOGY FOR SPORTS DOMAIN

4.1 INTRODUCTION TO ONTOLOGIES

The term ontology refers a data model that represents a set of

concepts within a domain and the relationships between those concepts. It is

used to reason about the objects within that domain. Ontologies are used in

artificial intelligence, the semantic web, software engineering, biomedical

informatics, library science, and information architecture as a form of

knowledge representation about the world or some part of it. Ontology is a

formal description of concepts and the relationships between them.

Definitions associate the names of entities in the ontology with a human-

readable text that describes what the names mean. The Ontology can also

contain rules that constrain the interpretation and use of these terms.

4.2 DEVELOPMENT OF THE SPORTS DOMAIN ONTOLOGY

CONCEPT

Ontology is the structural framework for organizing information. It

formally represents knowledge as a set of concepts within that domain, and

the relationships between those concepts. It can be used to reason about the

entities within that domain and may be used to describe it.

Ontology is the specification of concepts. Conceptualization is a simplified view that represents the purpose. Every ontology includes a

38

dictionary with an explanation of the terms and indications, and shows their relationships. Ontology represents the conceptual description of the specific content, to identify the appropriate terms and relationship in a given knowledge domain. Ontologies show a hierarchical dependence of the terms together with descriptions, explanations and definitions. New users will be able to understand their use and incorporate the concepts in a knowledge domain. Ontology gives a graphical representation by ontoviz. Ontologies provide a mechanism to capture knowledge about the problem domain. The ontology document is present in the RDF and OWL Languages. Using RDF ontology, every provider is free to add or subtract concepts from the initial version without the risk of becoming incompatible.

This thesis deals with the creation of ontology for the sports domain. In this a query template has been developed for storage and retrieval of sports information. For this purpose, the ontology concepts are implemented using OWL lite.

The content for this implementation is taken from the dataset of BBC (2012 Olympics). It has a basic concept of sports ontology by adding physiological variable and physical activity to it the data set becomes complete. Physiological variable is very important data which is measured prior to the event as well as post event. The dataset of BBC 2012 contains the information about the events, venue, schedule and the performance of the athlete and the same has also been quoted by Nwe Ni Aung and Thinn Thu Naing (2011). The Performance of the e-learner is a physical activity and is very important measure is physiological variable hence the physiological variable is added to the basic data set to make this complete.

4.2.1 OWL-Lite

OWL-Lite is the syntactically the simplest sub-language. It is intended to be used in situations where only a simple class hierarchy and constraints are needed. Thus it is envisaged, that OWL-Lite will provide a

39

quic

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ri an

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sers

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ties

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Indi

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the

spor

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is th

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ere

the

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ports

kno

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are

repr

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For

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ple

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ts i

s th

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ain

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, res

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40

most general concepts in the domain, and the subsequent specialization of the concepts. The classes of ontology may be extensional or intentional in nature. A class can subsume or be subsumed by other classes. A class in protege can be concrete meaning and it can have direct instances or an abstract, which means that while it appears in the class hierarchy it has no direct instances. When the class is created, by default it is concrete.

In Figure 4.1 the entire class hierarchy is represented. The parent

class and the child classes are clearly visible. OWL classes are interpreted as

sets that contain individuals. These are described using formal descriptions

that state precisely the requirements for the membership of the class.

Figure 4.1 Class structure for sports

41

4.2.4 Relationships for Ontology

Has-ainstance of

part of

part of part of part of

mts of

finals of

finals of finals of

mts ofmts of

mts of

track of

track of

Track of

long distance of

Has-aHas-a

has

Has-ais a

is a

is a is a

is a

Middle Learner

Basic Learner

Learner

Instructor Basic Instructor

B. Ed Well Known Instructor

Particular EventInstructor

8 Tracks semi & finals

Sports

GroundDetails

100mts

Field Events

Sprint

Athletics

Sports College

End of the Stage (learner)

Long Distance

M. Ed Ph. D

Track Events

Private Institution

400mts 200mts 3000mts 1500mts

800mts

Final Results

is a

track of

sprint of

Figure 4.2 Relationship for sports ontology

In Figure 4.2 shows the Relationship for sports activity.

For example Is-a-Relationships ( )

Physiological variables

BH

ED VC

RRPart of

Part of

42

1. Basic E-learner(Beginner) Middle e-learner End of the stage(Expert)

2. Basic Instructor particular event instructor Well known instructor

Has-a-Relationships ( )

1. Sports college M.Ed Well known instructor instructor

2. Private Institution Ph.D Well known instructorinstructor

Part-of-Relationships ( )

1. 3000mts->long distance Track events Grounddetails Athletics

2. 800mts long distance Track events Grounddetails Athletics

3. 1500mts long distance Track events Grounddetails Athletics

4. Ground details Athletics sports

5. Sports Athletics physiological variable BHR

Track-of-Relationships ( )

1. Tracks semi final final track of 400mts sprint

2. Tracks ->semi final final track of 100mts sprint

3. Tracks-> semi final final track of 200mts sprint

Instance-of-Relationships

Athletics Sports

43

4.2.5 Range and Domain

The range and domain are the characteristics of an object of the class. The domain is represented on the left side of relation” (Destination), and Range is represented on the right side (Accommodation). The OWL restrictions in the ontology enable the inference. The domain and range information for object the property is provided. Based on this property the class is inferred.

In sports, the domain is the destination point which is “Athletic” and the range is the accommodation point; here it is 100mts and 200mts.

Domain

The domain is the Destination point which has accommodation inside. A domain can contain multiple classes, and can have an undefined property which can be used everywhere

4.2.6 Axioms

The OWL allows general expressions to be used in axioms. Like domain and range constraints, axioms are global and do not necessarily appear near the classes. The notion of an axiom is defined as follows; “100mts is a subclass of Athletic” means “100mts implies Athletic”, emphasizes the meaning of subsumption. On the other hand, it seems an odd way to express implication, if that is really what is intended. Hence, care is required with the paraphrase and improved user interfaces for the axioms.

The development of the sports domain ontology represented in Figure 4.3, defines the sports information. It is designed for the identification of sports, their named entities and their relations. The hierarchical taxonomy is identified according to the respective sports activities. Hierarchical taxonomy can be concepts, properties and attributes (instances).

44

Figure 4.3 Visual representation of the sports domain ontology

4.3 PROPERTIES OF SPORTS DOMAIN ONTOLOGY

Domain concepts can be physical or abstract. Physical concepts

include material or equipment objects. Abstract concepts are places of

competitions or tournaments, Names of the competitions, Time of the

45

tournaments/athletics, state/district/divisional names, physical activities,

physiological variables etc. The relationship involves the sports domain.

Concepts involve models of the activity relations. The attribute is the property

of the concept (class). It plays a role in the modification of words or phrases

with concepts and the relation between concepts. The State/district/division is

an environmental place that includes many nations. Time denotes sports

competition or tournament time where sports competitions time is specific.

Person is a player who competes in the tournament and some players

represent the national team. In physical activity is comprised of strength,

power and physiological variable of sports person. The body temperature of

the sports person is taken as the physiological variable during the training

activity.

The domain ontology contains the sports related objects such as

‘take off’ performer, ‘has break out’ starter, ‘has consider’ player, ‘go ahead’

stand for player, ‘go out’ strength performance, ‘hold on’ performance, ‘look

after’ performer, ‘wind up’ performance, ‘pick out’ game, hasComposedof,

hasDone, hasPerformed, hasPlayed, hasCompetition etc. Each property

defines a class for its role described in OWL. The OWL Properties represent

relationships between two individuals. There are two types of properties in

OWL classes: Data type properties, relations between an individual to an RDF

literal value. Object properties, relations between an individual to an

individual are defined.

4.3.1 OWL Properties

OWL properties represent relationships. There are two main types

of properties, Object properties and Data type properties. Refer Table 4.1 for

object properties and Data types.

46

4.3.1.1 Object properties

Object properties are the relationships between two individuals.

These link an individual to an individual. Note that the name object property

is not intended to reflect a connection with the RDF Object properties, which

is created using the 'Object Properties' tab. Use the 'Add Object Property’

button to create a new Object property and name the property using the

'Property Name Dialog'.

Object properties are ways to relate two Objects, and are also

named as predicates. If object properties use the syntax, object1 object

Property object2, for example Sachin hasNationality Indian.

Here hasNationality is an object property. Does asserting "Sachin

hasNationality Indian" automatically make Sachin a member of the class

parent and also Indian a member of the subclass of Sachin.

4.3.1.2 Data type properties

Data properties are just like object properties except for their

domains, and are typed literals. This property relates persons to strings, the

string being that person's full name. Data properties are a subset of the things

along with the object properties. Refer table 4.1, object and data type

properties.

The relationships between an individual and data values are

described. These can be created using add Data type Property button of the

Data type Properties tab. Data type properties include the relations between

instances of classes and RDF literals. Refer table 4.2, metrics for object and

data type properties.

47

Table 4.1 Object and Data type properties

Properties OWL Property Data Type

aLotOf owl:ObjectProperty Undefined

according to owl:ObjectProperty Undefined

aheadOf owl:ObjectProperty Undefined

apartFrom owl:ObjectProperty Undefined

arrangeFor owl:ObjectProperty Undefined

asideForm owl:ObjectProperty Undefined

askFor owl:ObjectProperty Undefined

backOut owl:ObjectProperty Undefined

because of owl:ObjectProperty Undefined

bringDown owl:ObjectProperty Undefined

bringOut owl:ObjectProperty Undefined

Callback owl:ObjectProperty Undefined

carryAway owl:ObjectProperty Undefined

Carryout owl:ObjectProperty Undefined

clearOff owl:ObjectProperty Undefined

clearDown owl:ObjectProperty Undefined

getAlong owl:ObjectProperty Undefined

Fallback owl:ObjectProperty Undefined

fallFor owl:ObjectProperty Undefined

farFrom owl:ObjectProperty Undefined

getOn owl:ObjectProperty Undefined

goAhead owl:ObjectProperty Undefined

goForward owl:ObjectProperty Undefined

Goon owl:ObjectProperty Undefined

Gout owl:ObjectProperty Undefined

48

Table 4.1 (Continued)

Properties OWL Property Data Type

goesTo owl:ObjectProperty Undefined

Handover owl:ObjectProperty Undefined

hangUp owl:ObjectProperty Undefined

hasPlayingtime owl:DatatypeProperty Time

hasPlayername owl:DatatypeProperty String

hasNationality owl:DatatypeProperty String

hasNationName owl:DatatypeProperty String

hasBat owl:DatatypeProperty String

hasBall owl:DatatypeProperty String

hasStump owl:DatatypeProperty String

hasRacket owl:DatatypeProperty String

hasNet owl:DatatypeProperty String

hasPlayinEndTime owl:DatatypeProperty Time

4.3.1.3 Individuals

Individuals are also known as instances. Individuals can be referred

to as ‘instances of classes’. Individuals provide a view of their properties in

Protege-OWL. In particular, it supports creating individuals that are members

of anonymous classes and creating relationships to anonymous individuals.

Figure 4.4 shows the types and relationships of the individual

"SportsOntology". This object has "india" relationship to an object called

"Cricket". It also has relationship to the object "india" to some anonymous

individual that is a member of "Hockey" etc.

49

Figure 4.4 Object properties and individual

Table 4.2 Metrics for object and data type properties in sports ontology

Type of Metrics Metrics Sports Ontology

Class Metrics

Class count 531Object property count 114Data property count 8Individual count 94DL expressivity ALCH(D)

Class axiomsSub Class axioms count 5367Disjoint Classes axioms count 34

Object property axioms

Sub object property axioms count 45

Data property axioms Sub data property axioms count 6Individual axioms Class assertion axioms count 142Annotation axioms Entity annotation axioms count 3825

50

4.4 QUERY TEMPLATE

The query template is a mechanism, by which the mapping of the

query is done easily on the ontology, to one or more query statements. The

query statements are kept separate, and changes to queries do not require

recoding. The query template stored with an index mapped to the ontology

enables the retrieval of the results from the CSP. For example, when an

e-learner inputs a keyword as “BPhigh”, the keyword is matched with the

ontology structure, and the result from the ontology would be “Physiological

variable” as the parent class, and “BPhigh” as the subclass. The query

template mechanism generates the query, by mapping this ontology and

retrieves the relevant data which satisfies the constraints from the CSP as

shown in Figure 4.5. The sports ontology provides the e-learners with

keyword relevant constraints for sports training activity. The e-learners

retrieve the sports training activity course content. The e-learner’s

physiological variable for sports training activity course contents is stored in

the query template. So the e-learners access or retrieve the relevant

information from the sports ontology query template in Figure 4.6.

Figure 4.5 Architecture of the query template

Query Template

CSP

Sports Ontology Input (physiological variable keyword)

Instructor

E-learner

Matches

Not found

Query

Constraints relevant to physiological variable

Mapping in Ontolog

Constraints

e-learner keyword relevant constraints for sports training

activity

Provides

Relevant answers’

Rules

Sports training activity course content

Retrieves

51

Figure 4.6 Query template user interface

4.5 SPORTS TRAINING ACTIVITY COURSE CONTENT

In this work, the main focus is on the ontology based e-learning

system, which is designed along with the course content for teaching online

athlete (sprint e-learner (100mts running, 200mts running, 400mts running),

jumper). The sample course content is given below.

For the sprinter’s Warm up session

1. 3 Rounds warm up -> exercise-.>sliding (slow running) for

100mts. After slow running for 100mts. 100mts, 200mts,

400mts, jump e-learner starts learning activity for the sprint

e-learner (workouts).

Learning activity for the sprint e-learner

Day1: 120mts running (5 times)-> slow walk -> 140mts running

(2 times) ->slow walk-> 180mts running (3 times).

52

Day 2: hurdles 5 times jumping , leg put in the hurdles exercise, slow

running 50 mts.

Day 3: 150mts running (6 times), 300mts running (5 times)

Day 4: Starting position learning (100mts), 50mts running from starting

position.

Day 5: Jumping events (Leg jump, Hand height jump, tree touch jump, net

jump)

Day 6: long running (10 kilometers)

Day 7: Game events (volley ball, throw ball, foot ball, hand ball)

E-learner learns the training activity from day 1 to day 7; the

sprint learner achieves the levels (National meet and

international meet).

During the training activity the e-learner checks the

physiological variable and physical activity.

E-learner consults the trainer or system, as to what type of

food he can eat, and the water intake levels. So if the e-learner

follows he achieves his goal.

4.6 PERFORMANCE ANALYSIS IN E-LEARNING SYSTEM

AND INSTRUCTOR SYSTEM

The e-learning system with keyword search based on ontology

concepts retrieves more number of documents than the traditional system.

Also, the relevance of the document retrieved is higher, with respect to the

e-learner query which is shown in Figure 4.7.

53

Table 4.3 Performance improvement in E-learning system based on ontology

Keyword Search

User Query No. of Keywords

Total No.of Documents in

Database

Total No. of Retrieved

Documents

Total No. of Relevant Documents in

Retrieved Document

Precision(%)

Recall

(%)

E-learning system

(ontology based)

Word (Correct keyword VChigh)

3 256

(constraintssatisfied in the

Query Template)

251 250 98.0 99.6

Instructor system or traditionalsystem

Word or Sentence(incorrect keyword Heart)

3 256 (traditionalSystem database)

240 210 93.7 87.5

54

0

50

100

150

200

250

300

E-Learning system Traditional system

No of DocumentsNo of retrived documentsNo of relevant documentsPrecisionRecall

Figure 4.7 Comparison of e-learning system with ontology vs

traditional system

As illustrated in Figure 4.7, the proposed sports e-learning system

for the sports domain based on ontology, gives higher precision and recall

compared with the traditional approach (without using ontology). The

experimental results are given in Table 4.3.

4.7 EVALUATION OF THE E-LEARNING SYSTEM AND

INSTRUCTOR SYSTEM

The evaluation of the e-learning system and instructor system is

done based on 70 e-learners, out of which 25 are beginners, 20 middle

e-learners, and 25 expert e-learners. The availability of course content, user

friendliness, response time, interactivity and easy to use, sufficient sports

content, relevant sports content, up-to-date content, and learning activity

assessment of the system as shown in tables 4.4 and 4.5 are used to measure

the performance of the e-learning system, by three categories of e-learners,

viz, beginner, Middle e-learner, and Expert e-learner.

55

Table 4.4 Measurement of various dimensions of e-learning system

e-learner Questions

Ontology based Access Information Through

E-learning System

Beginners

(25nos)

Learning%

Middle E-learners

(20nos) Learning %

Expert

E-learners

(25nos) Learning %

Q1 e-learning system provides High availability of course content(sports)

40% 60% 92%

Q2 e-learning system provides Sufficient content

64% 75% 92%

Q3 e-learning system provides relevant sports content

64% 80% 100%

Q4 e-learning system provides up-to-date

content40% 60% 96%

Q5 e-learning system provides user-friendliness

60% 80% 94%

Q6 e-learning system provides high response time

72% 85% 92%

Q7 e-learning system provides learning activity performance assessment

72% 70% 92%

56

Number of e-learners’ ontology based access information through

e-learning system

e-learner Questions

Ontology Based Access Information Through

E-learning System

Beginners

(25nos)

Middle

E-learners

(20nos)

Expert

E-learners

(25nos)

Q1 e-learning system provides High availability of course content(sports)

10 12 23

Q2 e-learning system provides Sufficient content

16 15 23

Q3 e-learning system provides relevant sports content

16 16 25

Q4 e-learning system provides up-to-date

content10 12 24

Q5 e-learning system provides user-friendliness

15 16 24

Q6 e-learning system provides high response time

18 17 23

Q7 e-learning system provides learning activity performance assessment

18 14 23

57

Table 4.5 Measurement of e-learner satisfaction through the instructor

system

E-learnersQuestions

Without Ontology Based Access Information Through E-learning

System (Instructor System)

Beginners

(25nos)

Middle E-learners

(20nos)

Expert

E-learners

(25nos)

Q1 Instructor system provides High availability of course content(sports)

48% 55% 52%

Q2 Instructor system provides Sufficient content

60% 45% 64%

Q3 Instructor system provides relevant sports content

64% 85% 72%

Q4 Instructor system provides up-to-date

content36% 40% 52%

Q5 Instructor system provides user-friendliness

44% 65% 40%

Q6 Instructor system provides high response time

24% 55% 72%

Q7 Instructor system provides learning activity performance assessment

48% 60% 56%

58

Number of e-learners without Ontology based access information

through e-learning system (instructor system).

E-learnersQuestion

Ontology based access information through

E-learning system

Beginners

(25nos)

Middle E-learners

(20nos)

Expert

E-learners

(25nos)

Q1 E-learning system provides high availability of course content(sports)

12 11 13

Q2 E-learning system provides sufficient content

15 9 16

Q3 E-learning system provides relevant sports content

16 17 18

Q4 E-learning system provides up-to-date

content9 8 13

Q5 E-learning system provides user-friendliness

11 13 10

Q6 E-learning system provides high response time

6 11 12

Q7 E-learning system provides learning activity performance assessment

18 12 14

59

4.8 SUMMARY

This chapter describes the role of ontology in developing the

e-learning system for the sports domain. The Ontology is built using the

protégé tool and the query is generated using the query template, based on the

keyword from the e-learner. The query generated using the query template, is

used in the Constraint Satisfaction problem (CSP), for retrieving the data

which satisfies the constraints, as will be discussed in the following chapter.