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University of Surrey School of Electronics, Computing & Mathematics
�
Depar tment of Computing Centre�for�Knowledge�Management
�
Technical Repor t ���
Ontology: A Review of Current Research Trends in Design & Development
Mariam�Tariq�AI Group �
February 2002 �
�
� 1
Ontology: A Review of Current Research Trends “ To be is to be the value of a quantified variable” Quine���
1�� ����Introduction.....................................................................................................................1�
2� Ontology�in�Artificial�Intelligence...................................................................................2�3� Ontology�Design�and�Development .................................................................................4�
3.1� Conceptual�Modelling�and�Methodologies ..................................................................7�3.2� Ontology�Specification�Languages�and�Formalisms ....................................................9�3.3� Ontological�Engineering�Tools..................................................................................11�3.4� Automatic�Ontology�Acquisition�and�Learning .........................................................13�3.5� Ontology�Applications�and�Uses...............................................................................17�
4�����������Database�support�for�Hierarchical�Data.........................................................................19�5� Ontology�in�Image�Retrieval .........................................................................................22�6� Discussion ....................................................................................................................24�
6.1� A�Proposed�Conceptual�Structure�for�the�Ontology ...................................................25�References………………………………………………………………………………………...28�
1 Introduction
�The�term�ontology bears� its�roots� in�philosophy�though� it�has�been�widely�adopted�in�other�fields�
such� as� psychology,� linguistics� and� artificial� intelligence� with� relatively� different� nuances� to� the�
meaning.� In� traditional� philosophy� it� is� the� study� of� "what� there� is"� (Rosen,� 1998)� or� the�
“knowledge� of� being” � (Greek:� ontos-� ‘being’ � and� logos-� ‘knowledge’)� (Reese,� 1980).� The� term�
goes� as� far� back� as� Aristotle’s� time� when� he� established� the� ten� basic� categories� to� classify�
anything� that� could� be� said� about� anything� else.� In� psychology,� ontology� can� be� taken� as� "An�
aspect� of� metaphysical� inquiry� concerned� with� the� question� of� existence� apart� from� specific�
objects� and� events"� (Reber,� 1985).� This� could� cover� cases� such� as� the� ontological� argument1�
concerning� the�underlying�conceptual�systems�of� theories�of� the�mind.� In� linguistics� it� is� taken�as�
“ the� nature� of� existence”� –the� question� whether� and� in� what� sense� a� language� system� or� its�
elements� exist� is� therefore� an� ontological� question,� or� concerns� the� “ontology� of� language”�
(Matthews,� 1997).� In� artificial� intelligence,� varying� definitions� of� ontologies� that� could�
encompass�all�of�the�above�have�been�used�depending�on�the�application�domain�in�question.��
�
������������������������������������������������1�“An�argument,�which�infers�that�something�exists�because�certain�concepts�are�related�in�certain�ways.” �
(Lacery,�1996).�
� 2
The�next�sections�deal�with� the� role�of�ontology� in�artificial� intelligence�for�modelling�knowledge�
and� discuss� the� various� related� issues.� The� various� areas� involved� in� ontology� research� are�
identified� such� as� ontological� engineering� tools,� ontology� specification� languages,� methodologies�
used�for�design�and�development,�ontology� learning�and�finally�the�different�application�areas�and�
uses�of�the�ontology.�A�simple�framework�is�provided�for�comparing�the�current�research�work;�In�
each�area� the�most�popular�examples�are�discussed�and�compared�with�references�being�provided�
for� further�study.� It�would�be� interesting� to�see�how�these�different�areas�relate� to�each�other�and�
hopefully�provide�some� insight� into�making�an� informed�decision�on�using�an�ontology�for� image�
retrieval�purposes.�
2 Ontology in Ar tificial Intelligence
�Ontologies�have�been�used� in�a�number�of�different� fields�and�applications�for�different�purposes.�
Recently� some� of� the� popular� research� fields� have� been� knowledge� representation,� knowledge�
management,� information� retrieval� and� extraction,� database� design� and� knowledge� sharing� and�
reuse.� An� ontology� is� one� of� the� key� components� of� a� knowledge-based� system� that� determines�
the� categories� of� things� that� exist� in� a� certain� application� domain.� Ontologies� have� been� used� in�
application�areas�as�versatile�as�medicine,�machine�translation,�geographic�data�set�integration�and�
bioinformatics.� Ontologies� are� also� widely� being� used� to� aid� information� retrieval� and� searching�
on�the�Web.��
�
Many�definitions�of�ontologies�have�been�put� forward� to�suit� the�respective�purposes�of�different�
application�areas�or�research�fields.�For�example�for�the�purposes�of�natural�language�processing,�
an�ontology�could�be�defined�as� "A�computational�entity,�a� resource�containing�knowledge�about�
what� concepts� exist� in� the� world� and� how� they� relate� to� one� another.� A� concept� is� a� primitive�
symbol� for� meaning representation� with� well-defined� attributes� and� relationships� with� other�
concepts.�An�ontology�is�a�network�of�such�concepts�forming�a�symbol�system�where�there�are�no�
un-interpreted�symbols"�(Mahesh�1996).�Mahesh�emphasizes�that�meaning�representation�must�be�
grounded�in�a�language-independent�ontology�such�that�lexicons�for�different�languages�as�well�as�
language� analysers� and� generators� can� share knowledge.� He� also� points� out� that� ontologies� in�
computational� applications� are� artificially constructed� entities� -they� are� created� and� not�
discovered�as�in�philosophy.�
�
Ontologies� are� becoming� increasingly� popular� in� sharing�and� reusing�knowledge�between�diverse�
sources�of� information�from�databases�or�knowledge�bases.�A�more�recent�definition�proposed�by�
� 3
Tom�Gruber� to�the�SRKB�mailing�list�reported�in�Guarino�(1997a)�is�“Ontologies�are�agreements�
about� shared� conceptualisations.� Shared� conceptualisations� include� conceptual� frameworks� for�
modelling� domain� knowledge;� content� specific� protocols� for� communication� among� inter-
operating� agents;� and� agreements� about� the� representation� of� particular� domain� theories.� In� the�
knowledge� sharing� context,� ontologies� are� specified� in� the� form�of�definitions�of� representational�
vocabulary� [.....].” �Most�applications� tend� to�be�based�on� limited�ontologies�of�highly�specialized�
domains,� which� create� a� major� difficulty� when� it� comes� to� sharing,� integrating� or� reusing�
applications.� Even� though� every� field� of� science,� business,� engineering� and� the� arts� has� its� own�
specialized� terminology� and� standards,� they� often� need� to� exchange� information.� Therefore,� in�
order� to� share� knowledge� with� other� applications,� an� ontology� must� be� part� of� a� more� general�
framework.� According� to� Sowa� (2000� p.53),� philosophy� could� provide� that� framework:� “ Its�
guidelines�and�top-level�categories�form�the�superstructure�that�can�relate�the�details�of�the�lower-
level� applications.” � Several� issues� need� to� be� considered� for� ontology� sharing� such� as� differing�
terminologies� for� the� same� concepts� and� incorrect� mapping� of� related� concepts� in� ontologies�
based�on�different�languages�(Sowa�2000).�
�
A� popular� definition� for� ontologies� that� has� been� used� in� a� number� of� AI� applications� is� "an�
ontology� is� an� explicit� specification� of� a� conceptualisation"� (Gruber,� 1993).� This� brings� up� the�
question�of�what�exactly�is�a�concept.�Guarino�&�Giaretta�(1995)�have�analysed�this�definition�as�
well� as� a� number� of� others� in� order� to� clarify� certain� terminologies� such� as� ontology,�
conceptualisation� and� ontological� commitment.� They� have� refined� Gruber’s� definition� to� “An�
ontology� is�an�explicit,�partial�account�of�a�conceptualisation.” �According�to�Guarino�(1998)�“ In�
its� most� prevalent� use� in� AI,� an� ontology� refers� to� an� engineering artefact,� constituted� by� a�
specific�vocabulary�used�to�describe�a�certain�reality,�plus�a�set�of�explicit�assumptions�regarding�
the� intended meaning�of� the�vocabulary�words.” �Guarino�has�classified�ontologies�as� four�kinds:�
top-level ontologies� which� describe� general� concepts� independent� of� any� domain� or� problem;�
domain ontologies� and� task ontologies� which� specialize� the� vocabulary� defined� in� top-level�
ontologies�by�describing�vocabularies� related�to�generic�domains�or� tasks;�application ontologies�
describe�concepts�relating�to�a�certain�domain�or�task�and�which�are�usually�specializations�of�the�
two�related�ontologies.�
�
Ontologies� can� be� used� for� modelling� or� building� hierarchical� structures� for� classifying� different�
terms,�entities,�objects,�concepts�or�categories.�The�structure�of�an�ontology�may�vary�depending�
on� the� requirements� of� the� application� domain.� Categories� may� be� organized� as� taxonomies� –
characterized�by�the�subtype�relationship;�as�subsumption hierarchies�–characterized�by�the�subset�
relationship;� or� as� compositional hierarchies� –characterized� by� the� subpart� relationship.� These�
structures� could� be� in� the� form� of� trees,� which� may� or� may� not� support� multiple� inheritance or�
� 4
lattices,� which� support� cross-links� for� showing� multiple� associations.� A� combination� of� the�
different� structures� may� be� ideal� for� certain� applications.� Ontologies� may� provide� various�
mechanisms� for� allowing� the� creation� and� description� of� new� categories� as� well� as� defining�
necessary�constraints�and� inter-relationships�amongst� them.�Ontologies� that�specifically�model�the�
vocabulary or terminology used� to� describe� domains� without� the� need� to� fully� specify� the�
categories� by� axioms� and� definitions� are� also� known� as� terminological ontologies� (Sowa� 2000;�
Norvig� & � Russell� 1995).� Chapter� 4� presents� current� research� work� on� ontology� within� an� AI�
context.�
3 Ontology Design and Development
�The�main�aim�of� this�section� is� to�provide�a�simple�framework�for�comparing�current�research�on�
ontology� design� and� development.� A� number� of� review� papers� have� been� written� that� discuss�
specific� areas� of� research� in� detail� but� none� that� provide� an� all-encompassing� overview.� This�
section� attempts� to� identify� the� various� areas� involved� in� ontology� research� such� as� ontological�
engineering� tools,� ontology� specification� languages,� methods� used� for� design� and� development,�
ontology�learning�and�finally�the�different�application�areas�and�uses�of�the�ontology.�In�each�area�
the�most�popular�examples�are�discussed�and�compared�with�references�being�provided�for�further�
study.� It� would� be� interesting� to� see�how� these�different�areas� relate� to�each�other�and�hopefully�
provide� some� insight� into� the� various� issues� that� need� to� be� considered� when� embarking� on� an�
ontology-related�project.�
�
Most�ontologies�have�been�created� in�an�adhoc�fashion�to�suit� the�domain�as�well�as� the�skills�of�
the� developers.� This� has� resulted� in� a� great� diversity� in� the� way�ontologies�are�created�and�used�
which� is�a�problem�when� it�comes�to�sharing,� re-using�or�integrating�existing�ontologies.�There�is�
a� wide� range� of� tools� and� languages� with� varying� complexities� and� expressive� powers� available�
for� the�development�and�formalization�of�an�ontology.�Since�there�has�been�such�an�extensive�use�
of� ontologies� in� recent� applications� there� is� a� need� to� have� common� standards,� robust� and�
accessible�tools�and�expressive�languages.��
�
One�main�issue�in�the�development�of�an�ontology�is�whether�to�use�an�existing�top-level�ontology�
or� create� one� specific� to� the� domain.� A� number� of� researchers� have� developed� a� top-level�
structure,� which� might� be� based� on� a� philosophical� background.� The� diagrams� below� show�
examples�of�four�top-level�ontology�structures�that�have�been�created�for�general�use.��
�
� 5
�
�
�
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�
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�
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�
�
�
�
�
�
�
Figure 1 Top-level�hierarchy�of�CYC�
�
�
�
�
�
�
�
�
�
�
�
�
�
Figure 2 Top-level�hierarchy�of�WordNet�
�
(THING,�ENTITY)�
(LIVING�THING,�ORGANISM)� (NON-LIVING�THING,�OBJECT)�
(PLANT,�FLORA)� (ANIMAL,�FAUNA)� (NATURAL�OBJECT)� (SUBSTANCE)�
(PERSON,�HUMAN�BEING)� (ARTEFACT)� (FOOD)�
THING�
INTANGIBLE� REPRESENTED�THING�
INDIVIDUAL�OBJECT�
INTANGIBLE�OBJECT�
EVENT� STUFF� COLLECTION�
RELATIONSHIP�
SLOT�
ATTRIBUTE�
PROCESS�INTANGIBLE�
STUFF�
INTERNAL�MACHINE�THING�
ATTRIBUTE�VALUE�SOMETHING�
EXISTING�
INTELLIGENCE�
TANGIBLE�OBJECT�
TANGIBLE�STUFF�COMPOSITE�TANGIBLE��INTANGIBLE�OBJECT�
� 6
�
�
�
�
�
�
�
�
�
�
Figure 3 Top-level�hierarchy�of�Sowa’s�ontology.�
�
�
�
�
�
�
�
�
�
�
�
�
�
�
�
�
�
�
Figure 4 Top-level�hierarchy�of�Norvig�& �Russell’s�ontology.�
�
Philosophers�tend�to�build�their�ontologies�in�a�top-down�fashion�by�trying�to�conceptualise�all�the�
basic� “ things” � that� exist� in� the� world.� The� idea� that� perception� is� enabled� by� contrasts� between�
objects�leads�to�observations�of�distinctions�between�them�so�that�they�can�be�placed�in�categories.�
(Sowa�2000)�Application�builders�use�a�variety�of�approaches�but�their�ontologies�model�concepts�
that� are� usually� limited� to� their� respective� domains,� which� Sowa� calls� “microworlds.” �All�of� the�
T�
OBJECT� PROCESS�
CONCRETE�
PHYSICAL�PROCESS� INFORMATION�OBJECT�
PHYSICAL�OBJECT� INFORMATION�PROCESS�
ABSTRACT�
ANYTHING�
ABSTRACT�OBJECTS� EVENTS�
�SETS� NUMBERS� REPRESENTATIONAL�
OBJECTS�INTERVALS�
PLACES�
PHYSICAL�OBJECTS� PROCESSES�
CATEGORIES�
SENTENCES� MEASUREMENTS� MOMENTS� THINGS� STUFF�
TIMES� WEIGHTS� ANIMALS� AGENTS� SOLID� LIQUID� GAS�
HUMANS�
� 7
generic� top-level� ontology� structures� have� an� “entity” � or� “ thing” � as� the� most� generic� concept.�
CYC’s� hierarchy� is� quite� tangled� while� the� WordNet� hierarchy� is� based� solely� on� a� taxonomic�
relationship� (i.e.� an� is_a� hierarchy).� It� is� interesting� to� note� that� certain� concepts� appear� to� be�
common� such� as� events,�abstract�versus�concrete,� living�versus�non-living,� though� they�might�be�
expressed� in�different�ways� for�example�an�abstract�concept�might�be�equivalent� to�an� intangible�
object.� IEEE�has�recently�started�a�Standard�Upper�Ontology�(SUO)�working�group.�Their�aim�is�
to�provide�a�standard�for�specifying�the�semantics�of�a�general-purpose�upper�level�ontology.�This�
will�provide�definitions� for�general-purpose�terms�and�a�structure�and�foundation�for�larger�lower�
level� domain-specific� ontologies.� It� is� estimated� to� contain� between� 1000� and� 2500� terms� plus�
roughly�ten�definitional�statements�for�each�term.�
�
3.1 Conceptual Modelling and Methodologies
�A� number� of� individual� software� methods� have� been� developed� to� aid� in� building� ontologies.�
Most� of� these� methods� have� been� geared� towards� a� particular� domain� or� application� area.� For�
example� TOVE2� was� developed� for� enterprise� modelling� and� Mikrokosmos� (Mahesh� 1996)� for�
NLP�and�machine� translation.�Some�of� them� like�Methontology� (Gomez-Perez,�1997)� though�are�
aimed�to�be�generic.�A�number�of�researchers�have�also�outlined�the�design�criteria�that�should�be�
considered�when�developing�ontologies� (Mahesh�1996;�Gruber�1993;�Guarino�1997b).�There� is�a�
need� though� for� formal� principles� to� be� defined� for� ontological� engineering� as� common� to�
software� engineering� (Gruber� 1993).� Aussenac-Gilles� et� al� (2000)� have� proposed� a� method� to�
build�an�ontology�given�a�text�corpus,�which�has�been�discussed�in�section�3.4.��
�
In� this� section� we� will� take� three� different� methodologies� intended� for� different� application�
domains� and� compare� them� to� extract� the� features� they� have� in� common.� This� will� help� in�
determining�whether� it� is�possible� to�have�one�definitive�design�methodology�or�whether�different�
applications�will�need�their�own�specific�methodologies.�Table�1�shows�how�the�different�steps�for�
the� methodologies� map� to� each� other.� Methontology� has� proposed� a� 7-step� methodology� and�
highlights� the� similarity� between� ontology� development� life� cycles� and� classical� software� life�
cycles.� For� example� the� ontology� is� developed� as� an� evolving� prototype,� which� can� be� modified�
anytime�during� its� life�cycle,�and� the�need� for�an�ontology� requirement�specification�document� is�
introduced.� Norvig� and� Russell� (1995)� have� outlined� a� 5-step� methodology� to� aid� in� the�
development� of� a�knowledge�base.�Mikrokosmos�have�not�specified�a�step-wise�methodology�but�
������������������������������������������������2�http://www.eil.utoronto.ca/tove/toveont.html�
� 8
some� design� “desiderata” � to� guide� their� ontology� development.� Hence� any� step� that� is� identified�
has�been�shown�but�not�numbered�in�the�table.��
��
NORVIG & RUSSELL �
METHONTOLOGY �
MIKROKOSMOS �1.����Decide�what�to�talk�about�
�1.����Knowledge�Acquisition�
�Ontology�Acquisition��(Situated�Development)�
� �2.�� Production�of�ontology�
specification�document�
�_�
2.�� Decide�on�a�vocabulary�of��predicates,�functions�&�constants�
�3.�����Ontology�Conceptualisation�
�
�����������������������_� �4.�����Ontology�Integration�
�Ontology�Integration�
3.�� Encode�general�knowledge�about�the�domain.�
4.�� Encode�a�description�of�the�specific�problem�instance�
��5.�����Ontology�Implementation�
��Ontology�Implementation�
_� �6.�����Evaluation��
�
_��
�7.�����Documentation�
�
5.�� Pose�queries�to�the�inference�procedure�and�get�answers.�
_��
�Inference�procedures�
�Table 1:�Comparison�Of�Different�Methodologies�For�Ontology�Development.��
The�different�phases�of�Methontology�are�briefly�described�below:�
�
1.� Acquire the knowledge.� This� is� an� independent� activity� using� the� standard� knowledge�
elicitation�techniques.�Most�of�the�acquisition�is�done�simultaneously�with�phase�2.�
2.� Produce an ontology specification document.� This� document� is� prepared� in� natural�
language� and� should� include� the� following� information:� the� purpose� of� the� ontology,�
defining� its� intended�uses,�scenarios,�etc;� the� level�of� formality;� the�scope�of� the�ontology�
which� should� include� its� characteristics� and� granularity� as� well� as� its� set� of� represented�
terms.�
3.� Conceptualize the ontology.� In� this�phase�a�conceptual�model� is�produced�based�on�a�set�
of� intermediate representations.� This� includes� various� activities� such� as� identifying�
concepts,�their�instances,�attributes�and�values�in�a�Data Dictionary�as�well�as�classifying�
groups�of�concepts�in�Concept Classification Trees�etc.�
� 9
4.� Integrate your ontology with others if helpful.� If� there� are� libraries� of� ontologies� that�
provide� definitions� of� terms� whose� semantics� and� implementation� methods� are� coherent�
with�your�conceptualization�than�their�definitions�may�be�reused.�
5.� Implement the ontology.� The� result� of� this� phase� is� the� ontology� defined� in� a� formal�
language�such�as�CLASSIC,�LOOM,�Prolog�etc.�
6.� Evaluation.� Here� the� ontology� is� verified� and� validated� (to� look� for� incompleteness,�
inconsistencies� and� redundancies)� with� respect� to� a� frame� of� reference� such� as� the�
requirement�specification�document.�
7.� Documentation of the ontology.��This�takes�place�during�the�whole�ontology�development�
cycle.�A�respective�document�is�produced�for�each�of�the�phases�above.�
3.2 Ontology Specification Languages and Formalisms
�The�choice�of� representation�and�structuring�of�knowledge� in� the�ontology� is�essential� to� the� type�
of� reasoning,�querying�and�support�for�evolution�and�integration�required�for�a�certain�application�
domain.� One� of� the� key� research� areas� in� ontological� engineering� is� the� development� of� suitable�
languages� for� the� specification� of� ontologies.� These� languages� are� based� on� varying� knowledge�
representation� paradigms.� There� are� frame-based� languages� such� as� Ontolingua,� those� based� on�
description� logics�such�as�LOOM,�those�that�combine�frames�with� first�or�second�order�predicate�
calculus�such�as�FLOGIC,�and�those�based�on�web�standards�(XML�and�RDF)�such�as�OIL,�XOL�
and�SHOE.�
�
When� choosing� a� language� for� a� particular� application� it� is� important� to� consider� its�
expressiveness�as�well�as�its�inferencing�capabilities�(Corcho�&�Gomez-Perez�2000).�As�discussed�
in� section� 2,� an� ontology� can� be� formally� represented� by� concepts,� attributes,� relations,� axioms�
and� instances.�The�expressiveness�criterion�compares�the�ontology�specification�languages’ �ability�
to� represent�concepts,� their�attributes�and� relations�between�concepts� if�present�etc.�Secondly� the�
inferencing�mechanisms�provided�by� the�various� languages�are�compared�e.g.�support�of�multiple�
inheritance,� automatic� classification� etc.� Table� 2� uses� the� criteria� mentioned� above� to� provide� a�
comparative� summary� of� the� most� prominent� ontology� specification� languages� being� used�
currently.� The� table� shows� a� comparison� of� the� main� features� –for� a� more� detailed� evaluation�
please�see�(Corcho�&�Gomez-Perez�2000).�
�
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� 11
3.3 Ontological Engineer ing Tools
�
Ontological� engineering� tools� could� vary� from� those� that� help� in� creating� and� maintaining�
ontologies�to�those�that�help�in�ontology�integration�or�reusability.�This�section�will�briefly�outline�
the�main�features�of�the�various�tools�available�commercially�for�building,�editing,�integrating�and�
maintaining�ontologies.�Our�main�reason�for�doing�the�survey�was�to�discover�whether:��
�
1)� There� is�any�support�provided�for�modelling�visual�primitives�and�linking�them�with�language�
and��
2)� If�there�is�any�support�for�automatic�ontology�generation.��
�
At� the� time� of� writing� this� report� there� was� no� support� provided� for� either.� A� few� researchers�
(Maedche�&�Staab,�2000,�and�Mikheev�&�Finch,�1995)�are�developing�integrated�environments�to�
aid� in� the�ontology�acquisition�process.�Their�work�has�been�discussed�in�the�next�section�(4.2.4).�
Table� 4-3� on� the� next� page� summarizes� the� main� features� of� some� of� the� most� popular� tools.�
Duineveld� et� al.� have� carried� out� an� extensive� survey� of� the� most� prominent� tools� based� on� a�
number� of� criteria� they� specified� as� important:� General features,� such� as� the� design� of� the�
interface�and�installation�procedures;�Ontology,�where�they�focus�on�issue�such�as�the�provision�of�
high� level� primitives,� example� ontologies� as� well� as� the� modelling� power� of� the� tool� such� as�
support� for� multiple� inheritance;� Cooperation,� where� the� different� systems� are� tested� to� see�
whether� they� provide� features� that� support� cooperative� building� of� an� ontology� such� as�
synchronous�editing.��
�
�12
�O
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NT
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K
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ms�
Lab
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ory,
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nfor
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The
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ity�
of�
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olog
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Inte
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GE
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The
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and�
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.�A�g
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ting
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onto
logy
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tool
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�re
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�sy
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App
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C�
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reat
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Ont
olog
ies�
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Bro
wsi
ng�O
ntol
ogie
s�
� �
� �
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USAGE
Edi
ting
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ies�
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ONTOLOGY
Lib
rary
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ntol
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s�
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atio
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sing
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olog
ies�
� �
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ntol
ogy�
Sha
ring
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Ont
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port
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SUPPORT
Ont
olog
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xpor
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� �
Tab
le 3
�Ont
olog
ical
�Eng
inee
ring
�Too
ls�
� 13
3.4 Automatic Ontology Acquisition and Learning
�Building� an� ontology� by� hand� is� a� difficult,� error-prone� and� time-consuming� task� as� well� as�
having� a� significant� level� of� subjectivity.� Subjectivity� could� be� related� to� the� identification� of�
different�concepts:�What�are� the�distinctions�between�categories;�should�something�be�a�category�
or� an� instance;� how� are� various� relationships� defined� and� so� on.� The� idea� of� generating� an�
ontology� automatically� would� be� an� ideal� situation� that� needs� to� be� explored� further.� Another�
related� issue� to� be� considered� is� whether� an� existing� ontology� can� learn� new� concepts�
automatically� so� that� the� ontology� is� systematically� updated.� There� has� been� some� extensive�
research� on� creating� general� semantic� lexicons� like� WordNet� or� Sensus� as� well� as� knowledge�
bases� that� claim� to� model� world� knowledge� like� CYC.� Though� very� useful� for� generic�
applications� they� are� inadequate� to� use� for� specialised� domains� such� as� medicine� or� forensic�
science.�The�role�of�specialist languages�or�domains�in�ontology�acquisition�has�not�yet�been�fully�
explored� and� it� would� be� interesting� to� study� the� possibilities� of� automatically� deriving� an�
ontology� given� a� structured� text� in� a� specialist� field.� Recently� there� have� been� two� trends� of�
research� into� the� automatic� acquisition� of� an� ontology:� One� approach� is� based� on� deriving� an�
ontology� directly� from� relevant� texts� without� the� need� for� any� background� knowledge� while� the�
other�approach�uses�learning�techniques�that�require�some�background�knowledge.��
Riloff� & � Shepherd� (1997)� have� proposed� a� method� to� automatically� build� a� semantic� lexicon�
given� a� text� corpus� by� providing� a� few� sample� categories� and� without� using� any� other� semantic�
knowledge.� They� have� used� the� fact� that� quite� often� category� members� tend� to� appear� in� close�
proximity� to� each� other,� for� example� you� can� have� lists� (guns,� knifes,� axes� …...),� conjunctions�
(knifes� and� guns� and� axes� …….),� appositives� (the� gun,� a� magnum)� and� nominal compounds�
(bowie� knife;� crime� scene� photographer).� They� have� tested� whether� using� a� text� corpus� it� is�
possible�to�collect�the�surrounding�words�given�some�seed�words�to�start�with.�Statistical�methods�
were� used� to� identify� and� rank� words� that� may� be� related� to� the� specified� categories.� Riloff� & �
Shepherd� have� used� a� 5-step� algorithm:� Sentences� that�contain� the�seed�words�are� identified�and�
then� parsed;� A� 2-word� context window� is� defined� around� the� seed� word� wherever� it�occurs�as�a�
head� noun� in� the� corpus;� A� category score� for� each� word� is� created� based� on� the� ratio� of� the�
frequency� of� the� word� in� the� category’s� context� window,� to� the� frequency� of� the� word� in� the�
corpus;�Numbers,�stopwords�and�words�with�a�frequency�below�5�are�then�removed;�Finally�the�5�
nouns� with� the� highest� score� are� added� to� the� category� list� and� the� whole� procedure� is� then�
repeated.� Users� have� to� validate� the� list� of� ranked� words� that� would� then� be� used� to� create� the�
lexicon.� They� tested� their� method� on� a� number� of� different� categories� and� seed� words� and� their�
� 14
results� showed� that� a� basic� semantic� lexicon� could� be� built� using� 5� seed� words� and� with� 15-20�
minutes�of�human�intervention.��
�
Hearst� (1992)� has� identified� a� set� of� commonly� occurring� “ lexico-syntactic� patterns” � which� he�
uses�to�automatically�derive�hypernymic�relationships� from�unrestricted�text.�The�example�Hearst�
(1999� p.1)� has� cited� using� the� such as� cue� is� “The� bow� lute,� such as� the� Bambara� ndang,� is�
plucked� and� has� an� individual� …..” .� He� notes� that� most� fluent� English� readers�will�guess� that�a�
Bambara� ndang� is�a�kind�of�bow� lute�even� though� they�have�never�come�across�Bambara�ndang�
before�and�only�have�a�rough�idea�what�a�bow�lute�is.�Hearst�relates�the�hypernym�relationship�to�
the�cue�kind of�i.e.�X�is�a�hypernym�of�Y�if�Y�is�a�kind�of�X.�Some�other�example�cues�he�has�used�
are� or other, and other, including,� and� especially.� Hearst� has� also� outlined� a� procedure� for� the�
automatic� discovery� of� patterns� and� uses� WordNet� to� verify� all� the� relations� that� were� derived�
from� the� acquisition� method.� According� to� Hearst� the� advantage� of� using� these� patterns� is� that�
they�are�usually�indicative�of�the�required�relation�and�can�be�applied�to�a�large�variety�of�texts�as�
well� as� there� being� no� need� for� any� pre-encoded� knowledge� to� identify� the� patterns.� Some�
problems� he� encountered� were� the� occurrence� of� metonymy� (‘king,’ � ‘ institution’ ),� the� under-
specification�of�certain� facts� for�example�knowing� that� literary�devices�are�being�discussed�when�
the�hyponym�(‘device,’ � ‘plot’ )� is�acquired�and� the�contextual�dependence�of�certain� relations� like�
(‘aircraft,’ �‘ target’ ).��
�
Sanderson� &� Croft� (1999)� use� statistical� techniques� based� on� subsumption,� a� type� of� co-
occurrence� relationship,� to� organize� words� extracted� from� a� set� of� documents� in� a� hierarchical�
manner.�Subsumption�is�defined�for�two�terms�x�and�y�as�“x�subsumes�y�if�the�documents�which�y�
occurs� in�are�a�subset�of� the�documents�which�x�occurs�in.” �Initially�a�set�of�terms�for�the�query,�
which� is� consequently� expanded� using� local� contextual� analysis,� is� provided� to� retrieve� the�
relevant�documents.�Terms�are�elicited� from�the�highest�ranked�documents�and�each�selected�term�
is�compared� to� the�rest� to�check�for�subsumption.�Through�a�user�evaluation� it�was�seen�that� the�
derived�hierarchies�had�a�number�of�properties�(67%)�similar�to�the�ones�constructed�manually.��
�
Caraballo�(1999)�has�attempted�to�automatically�build�a�hypernym-labelled�noun�hierarchy�from�a�
text�corpus�without�using�any�other� lexical� resources.�She�uses�a�method�for�clustering�nouns�by�
creating� a� vector� for� each� noun� including� the� frequency� of� occurrence� of� all� other� nouns� in�
conjunctions� or� appositives� with� it.� The� cosine� of� the� angle� between� any� two� vectors� is� used� to�
measure� the� similarity� between� two� nouns.� Similar� nouns� are� combined� and� placed� under� a�
common�parent�and�this�process� is�repeated�to� form�a�hierarchy.�Light�(1996)�proposes�a�method�
� 15
for� the�acquisition�of� lexical�semantic� information�through�the�study�of�the�derivational�affixes�of�
words� to� elicit� the� semantics.� For� example� the� un-� and� de-� affixes� (e.g.� unfasten,� unwind,�
decompose,�defocus)�when�added�to� the�base�word� indicate�a�negation�of� the�original�state�of� the�
base�word.�Heyer�et�al.�(2001)�use�corpora-based�statistically�generated�collocation�sets�to�retrieve�
certain� semantic� relations.� They� use� a� collocation� measure� to� assign� a� value� based� on� the�
significance�of�each�word� in� the�collocation�set.�Two�types�of� relations�were�effectively�retrieved�
when�the�collocates�of�king�were�studied:�co-hyponymy�(shah,�queen,�rook)�and�instance of�(Fahd,�
Husein).�
�
Aussenac-Gilles� et� al� (2000)� have� proposed� an� approach� for� modelling� knowledge� by� using�
linguistic� techniques� on� texts� for� the� purpose� of� knowledge� elicitation.� They� consider� texts� as�
major� knowledge� repositories� that� can� be� explored� using� approaches� in� linguistics� and�
terminology.�They�claim�that�ontologies�and�texts�may�be�connected�in�2�ways:�concepts�could�be�
semantic� tags� attached� to� texts� while� texts� in� turn� could� be� connected� to� certain� concepts� in� the�
ontology.�Their�modelling�process� is� language� independent�and�consists�of�4-steps:�Setting up the
corpus,� where� relevant� texts� are� selected� based� on� the� requirements� for� the� model;� Linguistic
analysis,� where� the� terms� and� lexical� relationships� are�elicited�using�appropriate� linguistic� tools;�
Normalization,� where� the� previous� results� are� refined� in� that� the� user� chooses� the� terms� and�
relations� to� be� modelled� and� then� some� semantic� analysis� is� done� to� develop� the� conceptual�
model;� Formalization,� where� the� ontology� is� built� and� then� validated.� Aussenac-Gilles� et� al�
(2000)� have� tested� their� approach� by� building� an� ontology� of�knowledge�elicitation� tools�but� the�
method� will� have� to�be� tested�a�number�of� times�and� re-evaluated�before�a�definite�claim�can�be�
made�to�its�effectiveness.��
�
Maedche� &� Staab� (2000)� present� a� general� system� architecture� called� Text-To-Onto� for� the�
engineering� of� an� ontology.� They� propose� a� new� approach� for� the� semi-automatic� acquisition� of�
non-taxonomic� relationships� using� generalised� association� rules,� which� extends� current�
approaches� used� just� for� taxonomy� building.� Their� system,� Text-To-Onto,� has� various�
components� that�work� together� to�produce�an�environment� for�ontology� learning.�Text�processing�
techniques� such� as� tokenization,� lexical� processing� and� chunk� parsing� can� be� performed� to�
produce�a�mixture�of�syntactic�and�semantic� information.�For�example�a� lexical�database� is�used�
to� perform� POS� tagging,� some� morphological� analysis� such� as� identification� of� the� stems� of�
related�words,�analysis�of�compound�words�as�well�as�named-entity�recognition,�which�leads�to�an�
output�of�dependency� relations.�Associations�between�terms�at�a�particular� level�of� the� taxonomy�
are� discovered� using� a� generalized� association� rule� algorithm.� They� have� given� an� example� of�
� 16
using� the�appropriate� level�of�abstraction� for�a�purchasing�scenario,� for�example,�saying�“snacks�
are� purchased� together� with� drinks” � instead� of� “ chips� are� purchased� with� beer” � or� “peanuts� are�
purchased�with�soda.” �
�
Mikheev� &� Finch� (1995)� combine� various� methods� from� knowledge� engineering,� information�
retrieval� and� computational� linguistics� to� work� collectively� as� a� Knowledge� Acquisition�
WorkBench�(KAWB)�under�development�at�LTG,�Edinburgh.�Different�modules�work�together�to�
support� the� process:� A� data� extraction� module� includes:� a� word� class� identifier� that� attempts� to�
identify�semantic�categories�based�on�statistical�word�clustering�and�making�use�of�annotated�texts�
and�external�linguistic�and�semantic�resources;�a�lexical�pattern�finder�that�uses�parsed�text�to�look�
for� word� collocations� that� are� automatically� reviewed� to� deduce� regularities� which� are� presented�
to� the� user� as� potential� candidates� for� conceptual� characterization;� Finally� an� analysis� and�
refinement�module�aids� the�user� in�an� iterative�process�to�refine�his/her�hypothesis�by� testing�and�
generalizing�patterns.��
�
A� number� of� researchers� have� worked� on� automatically� learning� word� meanings� from� context�
using�a�knowledge� intensive�approach.�Hahn�(1998)�has�proposed�a�method�based�on�natural�text�
understanding� for� the�automatic�maintenance�of�domain-specific�ontologies.�A�given�taxonomy� is�
systematically� incremented� by� acquiring� concept� instances� and� classes,� taking� into� account� the�
background� knowledge� of� the� domain� as� well� as� the� linguistic� patterns� in� which� unknown�
lexicons� occur.� No�specialized� learning�algorithm� is�used�since� learning� is�a�meta-reasoning� task�
carried�out�by�the�classifier�of�a�terminological�reasoning�system.�This�qualitative�reasoning�copes�
with� several� competing� hypothesis� judging� on� the� quality� of� arguments� as� some� form� of� a�
hypothesis� evaluation.� The� example� he� cites� is� from� the� domain� of� information� technology.� He�
also� draws� a� distinction� between� his� methodology� and� that� of� information� extraction� in� which� a�
pre-fixed�set�of�templates�are�used�to�fill�with�the�required�knowledge.
�
�
�
�
�
�
�
�
� 17
3.5 Ontology Applications and Uses
�There� are� a� number� of� major� ontology� projects� that� are� being� developed� at� the� moment.� These�
ontologies� may� consist� of� simple� taxonomies� such� as� WordNet3� or� could� be� based� on� deep�
formalisms�such�as�Mikrokosmos.�Some�of� them�are�intended�to�be�generic�such�as�CYC4,�which�
aims�to�model�world�knowledge�e.g.�CYC�can�find�the�match�between�a�user's�query�for�"pictures�
of�strong,�adventurous�people"�and�an�image�whose�caption�reads�simply�"a�man�climbing�a�cliff."�
Others� are� more� domain-specific� such� as� GENSIM� a� genetic� simulation� system,� PLINIUS� an�
ontology�that� represents�mechanical�properties�of�ceramics.�Ontologies�are�being�used�for�various�
purposes� such� as� knowledge� sharing,� interoperability� amongst� agents,� and� information� retrieval.�
Table�5�provides�a�comparison�of�some�of� the�more�prominent�ontology�projects.� It� is� interesting�
to�note� that�since�most�of� these�projects�have�been� in�development� for�a�number�of�years�none�of�
them� use� any� of� the� tools� discussed� in� the� previous� section� and� they� have� all� been� constructed�
manually�with�no�use�of�automation�in�any�aspect.�
�
�
������������������������������������������������3�http://www.cogsci.princeton.edu/~wn/�4�http://www.cyc.com/�
�18
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� 19
4 Database suppor t for Hierarchical Data
Hierarchies�are�very�common�and�useful�in�a�number�of�domains�from�biological�classifications�to�
management� structures� and� organization� charts.� An� example� could� be� a� hierarchy� of� different�
types�of�weapons�as�shown�below.�
�
� Figure 5�An�example�hierarchy�of�weapons��
�
The� Node� DataBlade� extends� the� Informix� database� system� (Brown,� 2000)� by� providing� a� new�
‘node’ � data� type,� which� stores� the� node� identifiers� in� a� tree� hierarchy� using� the� basic� Dewey�
Decimal�Scheme.� It�enables� the�storage�of�hierarchical�information�such�that�the�hierarchy�can�be�
searched�and�queried�using�standard�SQL.�Traditionally� in�relational�databases,�hierarchies�could�
be� modelled� using� primary� keys� as� links�and�searched�by�performing� repeated� joins�which�could�
be�very�inefficient�if�the�hierarchy�was�deep�and�could�also�be�liable�to�circular�references�leading�
to� integrity�problems.�If�node�identifiers�were�stored�as�ordinary�strings�there�would�be�a�problem�
when�they�were�sorted�e.g.�sorted�in�hierarchical�order�1.1,�1.2,�1.8,�1.11,�1.12�and�sorted�in�string�
order� 1.1,� 1.11,� 1.12,� 1.2,� 1.8.� Hence� the� client� side� would� have� to� deal� with� processing� the�
hierarchy2.�
�
Various� types� of� queries� can� be� performed� on� the� hierarchy� based� on� the� standard� hierarchical�
relationships�such�as�ancestor,�parent,�sibling,�child,�descendant�and�so�on.�The�whole�tree�can�be�
������������������������������������������������2�How to Manage Hierarchical Data with the Node DataBlade Module. ���http://examples.informix.com/doc/case_studies/datablade/node/nodeAll.html�
� 20
searched�in�a�breadth�first�or�depth�first�manner.�Queries�can�be�varied�to�retrieve�for�example:�all�
the� descendants� or� just� the� immediate� descendants� of� a� given� node;� similarly� the� parent,� all� the�
ancestors�or� the� top-most�ancestor�can�be�retrieved.�The�query�below�is�used�to�search�the�tree�in�
breadth�first�order�and�display�the�results�
�SELECT�node_id::LVARCHAR,�name,����length(�node_id�)�����FROM�weapons����ORDER�BY�depth,�node_id;���Node_id name depth 1.0� � � Weapon� � � 1� �1.1� � � Firearm� � � 2� �1.2� � � Knife� � � � 2� �1.3� � � Blunt�Instrument� � 2� �1.1.1� � � Gun� � � � 3� �1.1.2� � � Pistol� � � � 3� �1.1.3� � � Revolver� � � 3� �1.1.4� � � Rifle� � � � 3� �1.1.1.1�� � Machine�Gun� � � 4� �1.1.1.2�� � Shotgun� � � 4� �1.1.2.1�� � Browning� � � 4� �1.1.2.2�� � Colt-45� � � 4� �1.1.2.3�� � Magnum� � � 4� �1.1.2.4�� � Diana�Air�Pistol� � 4� �1.1.3.1�� � Smith�and�Weston� � 4� �1.1.4.1�� � Hanibal� � � 4� �1.1.4.2�� � AK-47�Assault�Rifle� � 4� �1.1.1.1.1� � Sub-machine�Gun� � 5� �1.1.1.2.1� � Gentry�� � � 5� �1.1.1.1.1.1� � UZI� � � � 6� � Table 5 Result�from�a�breadth�first�search�of�the�weapons�table�
� 21
�
QUERY RESULT
Select the parent of AK-47(node 1.1.4.2) SELECT�node_id,�name�FROM�weapons�WHERE�node_id�==�GetParent(�'1.1.4.2'�);��
�node_id Name 1.1.4� Rifle��
Select the highest ancestor of AK-47(node 1.1.4.2) �SELECT�node_id::LVARCHAR,�name�FROM�weapons�WHERE�node_id�==�(�� SELECT�MIN�(node_id)�� FROM�weapons�� WHERE�IsAncestor�(node_id,�'1.1.4.2'));��
�node_id Name 1.0� Weapon��
Select all immediate children of gun (1.1) �SELECT�node_id,�name�FROM�weapons�WHERE�GetParent(�node_id�)�==�'1.1'��ORDER�BY�node_id;��
�node_id Name 1.1.2.1� Browning�1.1.2.2� Colt-45�1.1.2.3� Magnum�1.1.2.4� Diana�Air�Pistol��
Select all siblings of Diana Air Pistol(1.1.2.4) �SELECT�name,�node_id�FROM�weapons�WHERE�GetParent(�node_id�)�==�GetParent(�'1.1.1'�)�ORDER�BY�node_id;��
�node_id Name 1.1.1� Gun�1.1.2� Pistol�1.1.3� Revolver�1.1.4� Rifle��
Select all descendants of gun(1.1.1) �SELECT�node_id,��name��FROM�weapons�WHERE�'1.1.1'�<�node_id�AND�node_id�<�Increment(�'1.1.1')�ORDER�BY�node_id;��
�node_id Name 1.1.1.1� Machine�Gun�1.1.1.1.1� Sub-machine�Gun�1.1.1.1.1.1� UZI�1.1.1.2� Shotgun�1.1.1.2.1� Gentry��
Find number of entities at each level of the hierarchy �SELECT�length(�node_id�)�depth,��count(*)�number_of_items�FROM�weapons�GROUP�BY�1�ORDER�BY�1;���
�depth Number _of_items
1� 1�2� ����������������3�3� 4�4� 9�5� 2�6� 1��
Table 6 Some�sample�queries�and�results�using�the�Node�DataBlade�
� 22
The� hierarchical� DataBlade� is� limited� in� that� it� can� model� just� one� hierarchy� or� taxonomy� as�
conceptualised�by� the�person,� it�cannot�represent�a�combination�of�relationships�between�different�
concepts,� as� is� the� case� in� most� domains� i.e.� it� does� not� support� a� “ tangled� hierarchy.” � For�
example�in�the�scene�of�crime�domain�you�will�need�is_a�relationships�as�illustrated�in�the�weapon�
example�in�the�previous�section�but�you�would�also�need�part_of,�(a�blade�and�handle�are�part�of�a�
knife)� function_of,� (the� function� of� a� knife� is� to�cut�or�stab),� there�could�be�other�miscellaneous�
relationships� such� as� blade� stained_with� blood,� etc.� Hence� if� a� DataBlade� package� provided� an�
ontology� to� help� in� search� and� retrieval� it� might� address� the� limitations� mentioned� above.� The�
package�should�provide� the�necessary� routines� to�create�an�ontology�and� ideally� to�automatically�
update�it�with�new�information.�
�
5 Ontology in Image Retr ieval
�There� has� been� some� research� going� on� to� develop� visual� ontologies� though� most� of� it� has�been�
specific� to� a� specialized� domain� like� medical� imaging.� For� example� Aubry� et� al.� (1999)� have�
proposed� an� ontology� structure� to� aid� in� the� management� of� medical� images.� The� ontology� is�
supposed� to� provide� a� semantic� frame� of� reference� that� various� tools� can� benefit� from� by�
addressing� the� shortcomings� of� existing� data� formats� such� as� DICOM.� Bittner�& �Winter� (1999)�
attempt� to�use�an�ontological�basis� to� investigate� the�relationship�between�fiat�objects�(created�by�
the� spatial� analysis� of� remotely� sensed� images)�and� their�corresponding�objects� in� the�world� that�
pre-empt�their�existence.��
�
Srihari� (1995a)� suggests� that� a� visual� ontology� should� permit� entities� sharing� several� visual�
properties� to� be� grouped� together� and� new� entities� to� be� classified� accordingly.� According� to�
Srihari� visual�descriptions�can�be�organised� in�a�hierarchy�similar� to� textual�descriptions�and�she�
has� extended� WordNet� by� superimposing� a� visual hierarchy� with� links� such� as� visual� is_a� to�
represent�hyponymy�and�visual�part_of�for�meronymy.�These�visual�hierarchies�consider�contrasts�
such�as�shape,� texture�and�boundaries�between�objects.�A�main� issue� is� the� interpretation�of�a�3-
dimensional� object� represented� in� a� 2-dimensional� form.� It� would� be� interesting� to� study� the�
Family Tree of� images� illustrated�by�Mitchell� (1987�p.10)�as�a�basis� for�developing�a� top-down�
ontology� for� images.� It� would� be� a� difficult� issue� though� when� it� came� to� the� classification� of�
images�that�fall�into�the�category�of�either�perceptual�or�mental.�As�Srihari�said�(1995a),�"It�is�not�
sufficient� to� develop� independent� ontologies� corresponding� to� vision� and� language"� –we� need� to�
find�a�way�to�integrate�them.” ��
� 23
�
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Figure 6 Extension�of�WordNet�by�a�Visual�Hierarchy�(Srihari,1995b,�p417).�
�
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�
�
�
�
�
�
Figure 7 Family�tree�of�images�(Mitchell�1987,�p.10).�
�
Image Likeness�� Resemblance�� Similitude�
Graphic ��Pictures���Statues���Designs�
Optical ��mirrors���projections��
Perceptual ��sense�data���“ species” ���appearances�
Mental ��dreams���memories���ideas���fantasmata�
Verbal ��metaphors���descriptions�
LIFEFORM�
PLANT�
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TRUNK�CROWN� BRANCH�STUMP�
FOLIAGE�
ELONGATED�SHAPE�
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IS_A�
PART�PART� PART�
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� WORDNET�CONCEPT�
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WORDNET�LINK�
VISUAL�LINK�
� 24
6 Discussion
�An� ontology� underpins� the� conceptual� structure� of� a� domain� and� has� to� be� captured� and�
formalized� in� order� to� be� used� as� a� knowledge� source� by� intelligent� systems.� To� support� this�
process� there� are� a� number� of� methods� and� tools� available.� Until� recently,� knowledge� engineers�
have� manually� constructed� ontologies� by� using� knowledge� elicited� from� domain� experts� or� by�
studying� formal� texts.� Since� this� is� a� time-consuming� and� error-prone� process� the� ideal� solution�
would� be� to� automatically� or� semi-automatically� construct� an� ontology� of� a� domain.� This� has�
shown� to�have�promise� through� the�use�of� lexical� resources�such�as� texts�and� terminologies�as�a�
source�of�knowledge� together�with�established�methods� for�processing� them.�This� is�still�an�open�
research� question� and� some� issues� that� need� to� be� considered� for� generating� an� ontology�
automatically�are�summarised�below:�
• Establishing� the� type� of� knowledge� source� and� the� classification� of� texts/data� e.g.� free� texts�
such�as�natural�language,�semi-structured�data�such�as�HTML/�XML,�or�structured�data�such�
as�database�schemata.�
• Using� background� knowledge� in� the� form� of� existing� linguistic/� lexical/� terminological/�
ontological�resources�such�as�machine�readable�dictionaries�(MRDs),�Wordnet�or�CYC.�
• Defining�a�standard�set�of�conceptual�relations�such�as�hyponymy�and�meronymy�that�need�to�
be�discovered.�There�may�be�some�relationships� that�are�specific�to�a�domain�that�might�need�
to�be�elicited.�
• The� type� of� reasoning/learning� methods� that� would� be� most� suitable� –depending� on� the�
conceptual�structures�required�e.g.�relational�learning/analogical�reasoning.�
• There� are� well-established� text-analysis,� NLP� and� IE� tools� available;� how� can� they� best� be�
exploited�for�use�in�ontology�acquisition.��
• How�would�the�two�methods�mentioned�above�work�in�combination.�
• The� problem� of� how� to� bootstrap� an� ontology� -should� one� start� from� scratch� (bottom-up�
approach)� or� use� an� existing� top-level� ontology� as� a� basis� (top-down� approach)� or� a�
combination�of�both.�
When� considering� a� domain� in� which� images� play� a� key� role� such� as� fine� art� and� crime� scene�
investigation,� it� is� an� interesting� question� to� see� what� role� an� ontology� can� play� to� aid� in� image�
retrieval� either� just� by� capturing� linguistic� descriptions� of� objects� or� more� ambitiously� by�
incorporating�visual� information�within� the�ontology.�The�next�section�presents�some�preliminary�
thoughts�into�how�this�integrated�structure�might�be�conceptualised.�
� 25
6.1 A Proposed Knowledge Structure
�A�structure�was�proposed� for� the�ontology,�which�has� its� roots� in� the�meaning�triangle�presented�
by�Ogden�and�Richards�(Sowa�2000a/b).�There� is�a�concept,�a�symbol�and�an�object�at� the� three�
corners� of� the� triangle� (see� figure� 8).� The� concept� relates� the� symbol� to� the� object� where� the�
symbol� represents� something� according� to� some� convention� and� the� object� shows� the� form� of�
something.� As� Sowa� (2000b)� clarified,� “Ontologies� contain� categories,� lexicon’s� contain� word�
senses� and� terminologies�contain� terms.” �The�ontological�structure� is�being�developed�as� just� the�
preliminary� phase� of� an� investigation� whether� it� is� possible� to� have� a� terminology,� lexicon� and�
visual�features�as�part�of�the�ontology.�The�symbol�is�being�represented�by�a�‘ linguistic�entity’ �that�
has� the�different�words�senses�of� the� term�as�well�as� the�compounds�and�morphological�variants.�
The� object� is� the� ‘visual� entity’ � that� has� the� colour,� shape� and� texture� features� represented.�The�
next�step�will�be�to�see�if�the�ontology�can�be�represented�in�an�OSL.�
�
�
����
�
�����
�����������Figure 8�Proposed�knowledge�structure����
CONCEPT
OBJECT/ICON SYMBOL
VISUAL�ENTITY�
LINGUISTIC��ENTITY�
<COLOUR>�
<SHAPE>� <TEXTURE>�
<SYNSETS>�
<COMPOUNDS>� <MORPHOLOGICAL�VARIANTS>�
ONTOLOGICAL ENTITY�
� 26
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