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0 University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review of Current Research Trends in Design & Development Mariam Tariq AI Group February 2002

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Page 1: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

0

��

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 �

Page 2: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 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).�

Page 3: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 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�

Page 4: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 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�

Page 5: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 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.��

Page 6: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 5

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�

Page 7: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 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�

Page 8: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

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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�

Page 9: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 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.�

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� 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).�

Page 11: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

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Page 12: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 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.��

Page 13: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

�12

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Page 14: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 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�

Page 15: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 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�

Page 16: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 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�

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� 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.

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� 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/�

Page 19: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

�18

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Page 20: University of Surrey · University of Surrey School of Electronics, Computing & Mathematics Department of Computing Centre for Knowledge Management Technical Report Ontology: A Review

� 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�

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� 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�

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� 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�

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� 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.” ��

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� 23

Figure 6 Extension�of�WordNet�by�a�Visual�Hierarchy�(Srihari,1995b,�p417).�

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�

TREE�

TRUNK�CROWN� BRANCH�STUMP�

FOLIAGE�

ELONGATED�SHAPE�

FRACTAL�BOUNDARY�

SHAPE�

SHAPE�

IS_A�

IS_A�

IS_A�

PART�PART� PART�

PART�

� WORDNET�CONCEPT�

TREE-TOP�

VISUAL�CONCEPT�

WORDNET�LINK�

VISUAL�LINK�

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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.�

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� 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�

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� 26

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