Music domain ontology applications for intelligent web searching
CHAPTER 4 ONTOLOGY FOR SPORTS DOMAIN -...
Transcript of CHAPTER 4 ONTOLOGY FOR SPORTS DOMAIN -...
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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
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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
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quic
k m
igra
tion
path
for
the
exi
stin
g th
esau
ri an
d ot
her
conc
eptu
ally
sim
ple
hier
arch
ies. O
WL
Lite
sup
ports
tho
se u
sers
prim
arily
nee
ding
a c
lass
ifica
tion
hier
arch
y an
d si
mpl
e co
nstra
int
feat
ures
. Fo
r ex
ampl
e, w
hile
OW
L Li
te
supp
orts
car
dina
lity
cons
train
ts, i
t onl
y pe
rmits
the
card
inal
ity v
alue
s of
0 o
r 1.
It s
houl
d be
sim
pler
to
prov
ide
tool
sup
port
for
OW
L Li
te th
an i
ts m
ore
expr
essi
ve re
lativ
es, a
nd p
rovi
de a
qui
ck m
igra
tion
path
for t
hesa
uri a
nd o
ther
ta
xono
mie
s.
4.2.
2 O
ntol
ogie
s for
Spo
rts
Ont
olog
ies
for
spor
ts c
onsi
st o
f In
divi
dual
s, cl
asse
s an
d pr
oper
ties
of s
ports
dom
ain.
Indi
vidu
al re
pres
ents
the
spor
ts d
omai
n ob
ject
s. Th
is th
esis
de
als
with
cre
atio
n of
ont
olog
y co
ncep
ts fo
r spo
rts d
omai
n fo
r the
pur
pose
of
train
ing
thro
ugh
e-le
arni
ng. H
ere
the
form
al S
ports
kno
wle
dge,
its
conc
epts
an
d its
rela
tions
hips
are
repr
esen
ted.
For
exam
ple
Spor
ts i
s th
e m
ain
clas
s. Ph
ysio
logi
cal
varia
ble,
ph
ysic
al a
ctiv
ity, a
war
ds, m
ind
spor
ts a
nd
mis
cella
nea
are
clas
ses
and
ther
e ar
e su
bcla
ss fo
r eve
ry c
lass
for e
xam
ple
In c
ase
of p
hysi
olog
ical
var
iabl
e th
e su
bcla
sses
are
hea
rt ra
te, b
lood
pre
ssur
e, R
estin
g pu
lse
rate
, res
pira
tory
rat
e,
brea
dth
hold
ing
rate
, vita
l cap
acity
and
end
uran
ce.
OW
L on
tolo
gies
hav
e si
mila
r co
mpo
nent
s to
Pro
tege
fra
me
base
d on
tolo
gies
. How
ever
, the
term
inol
ogy
used
to d
escr
ibe
thes
e co
mpo
nent
s is
sl
ight
ly d
iffer
ent
from
that
use
d in
the
Prot
égé
3.4.
2 O
WL
onto
logy
, whi
ch
cons
ists
of
indi
vidu
als,
prop
ertie
s, an
d cl
asse
s, w
hich
rou
ghly
cor
resp
ond
to
Prot
égé
inst
ance
s, sl
ots a
nd c
lass
es.
4.2.
3 C
reat
ion
of C
lass
and
Sub
clas
s
Cla
sses
rep
rese
nt c
once
pts
in t
he d
omai
n. H
ere,
the
top
dow
n ap
proa
ch is
use
d. T
he d
evel
opm
ent
proc
ess
star
ts w
ith t
he d
efin
ition
of
the
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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
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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
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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
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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).
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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
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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.
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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.
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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
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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.
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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
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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
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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).
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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.
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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
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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.
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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%
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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
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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%
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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
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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.