Supporting Ontology Alignment Tasks By Organizing Ontology Mappings Using Edge Bundling

Support ing Onto logy A l ignment Tasks By Organ iz ing Onto logy Mapp ings Us ingEdge

Bund l ing

by

cMuhammadNas ir

Athes issubm ittedtothe Schoo lo fGraduateStud ies

inpart ia lfu lfi lmento fthe requ irementsforthedegreeo f

Mastero fSc ience

Departmento fComputerSc ience Memor ia lUn ivers ityo fNew found land

May2014

St .John ’s New found land

Abstract

Onto log iesarecommon lyusedforknow ledgerepresentat ionandtoexchangein-

format ionbetweenmu lt ip leapp l icat ions . Whens im i larin format ionisrepresentedby

d ifferentonto log ies ,in format ionshar ingrequ iresa mapp ingbetweencorrespond ing

pa irso fent it ies . Wh i leonto logya l ignmenta lgor ithmshavebeendeve lopedtosup-

portsuchtasks ,theygenera l lydonotofferent ire lycomp leteandprec ise mapp ings .

Consequent ly ,animportantinteract iveaspecto fthea l ignmentprocessistheva l i-

dat iono fautomat ica l lygenerated mapp ingsandtheadd it iono fnew mapp ings ,by

aknow ledge manager . Wh i lev isua linter facesex isttosupportthesetasks ,show ing

alargenumbero f mapp ingscanresu ltinas ign ificantamounto fv isua lc lutter . To

addressth isissue ,anedgebund l ingapproachhasbeenadaptedtotheconstra intso f

anex ist inga l ignmentinter face . Auserstudywasdes ignedandconductedtoeva lu-

atetheva lueo fedgebund l inginth iscontext ,w ithpos it iveresu ltsforbothmapp ing

va l idat ionandadd it iontasks .

Acknow ledgements

F irsto fa l l ,Iwou ldl iketos incere lythank mysuperv isors ,Dr .JoergEvermann andDr . Or landHoeber ,forthe ircont inuedgu idance ,andsupport . Theyprov ided the irins ight fu ladv ices ,showedhowtheacadem icresearchisconducted ,he lpedin improv ingmycr it ica lth ink ingsk i l ls ,andattent iontodeta i l . Mostimportant ly ,they constant lycha l lengemetodobetterinconduct ingh ighqua l ityresearchandlearn ing thesk i l lo fexp la in ingtheresearchbetter .Iamverygrate fu ltothemandw i l la lways apprec iatethe irinva luab legu idanceinimprov ing myovera l lsk i l lset .

Iwou ldl iketothankthe Departmento fComputerSc ienceand Memor ia lUn i- vers ityo fNew found landforthe irgenerousresourcesw ithoutwh ichth isthes iswou ld nothavebeenposs ib le . Myresearchisfinanc ia l lysupportedbymysuperv isor ’sgrant (NSERCD iscoveryGrants)andscho larsh ipfromtheUn ivers ity ’sSchoo lo fGraduate Stud iesandDepartmento fComputerSc ience .

Iwou lda lsol iketothankthegraduatestudentso ftheDepartmento fComputer Sc ienceforprov id ingthe irins ight fu lcommentsandsuggest ions . A l lo fmySt .John ’s fr iendsthat made mytrans it ionheresmooth , made myl i fefunanda lwaysprov ided mora lsupportthroughoutthecourseo f myresearch .

Lastbutdefin ite lynottheleast ,Iowemydeepestgrat itudetomylov ingparents , MuhammadYakoobandFar idaYakoob ,whoare myidea ls ,bestfr iends ,strongest supporters ,anda lwayspush metostr iveforexce l lenceandinbecom ingabetter personeachday .

Refereedpub l icat ionsfromth isthes is : Basedupontheresearchworkcon-

ductedinth isthes is ,apeer-rev iewedpub l icat ionhasbeen made[58 ] .

Contents

Abstract i i

Acknow ledgements i i i

L istof Tab les v i i i

L istofF igures x

1 Introduct ion 1

1 .1 Mot ivat ion... 1

1 .2 Approach ... 6

1 .3 ResearchQuest ionsand Methods... 7

1 .4 Organ izat iono fThes is... 8

2 Re lated Work 10 2 .1 Onto log ies... 10

2 .2 Onto logyA l ignment ... 14

2 .2 .1 Defin it ion... 15

2 .2 .2 A lgor ithmTypes... 15

2 .2 .3 HumanInvo lvement W ith intheOnto logyA l ignmentProcess. 19

2 .3 Onto logyA l ignmentInter faces... 22

2 .3 .1 In format ionV isua l izat ion ... 22

2 .3 .2 Representat iono fOnto log ies... 28

2 .3 .3 Representat iono f Mapp ings... 30

2 .4 Onto logy Mapp ingsRepresentat ionIssues ... 34

2 .5 Base l ineSystem... 37

2 .6 EdgeBund l ing ... 41

2 .6 .1 Theoret ica lFoundat ion... 42

2 .6 .1 .1 Gesta ltLaws... 43

2 .6 .1 .2 Categor izat ionTheory... 44

2 .6 .1 .3 Cogn it iveLoadTheory... 45

2 .6 .2 EdgeBund l ingA lgor ithm... 46

2 .6 .2 .1 Compat ib i l ity Measures... 48

2 .6 .3 BenefitsandDrawbackso fEdgeBund l ing... 50

3 Edge Bund l ing w ith inan Onto logy A l ignmentInterface 52 3 .1 Mot ivat ion... 52

3 .2 Approach ... 54

3 .2 .1 F rameworkandConstra intso f Mapp ingRepresentat ions... 54

3 .2 .2 EdgeBund l ingProcess... 56

3 .2 .3 Bund leAmb igu ityandD isamb iguat ion... 59

3 .2 .4 SupportforOnto logyA l ignmentTasks... 61

3 .2 .4 .1 Mapp ingVa l idat ion ... 61

3 .2 .4 .2 Supportfor Mapp ingAdd it ion ... 66

3 .3 Imp lementat ionDeta i ls... 69

3 .3 .1 P lat form... 69

3 .3 .2 SystemArch itecture ... 70

3 .3 .3 WorkF low... 71

3 .4 Examp le... 73

3 .5 D iscuss ion... 79

4 Eva luat ion 86 4 .1 Purpose... 86

4 .2 Hypotheses ... 88

4 .3 Methodo logy ... 97

4 .3 .1 Tasks... 97

4 .3 .2 Se lect iono fDatasets... 99

4 .3 .3 Procedures... 101

4 .3 .4 Ana lys is... 104

4 .4 Eva luat ionResu lts... 106

4 .4 .1 Part ic ipantDemograph ics... 106

4 .4 .2 T imetoTaskComp let ion ... 107

4 .4 .3 Accuracy ... 111

4 .4 .4 Perce ivedD ifficu ltyo fG ivenTasks... 112

4 .4 .5 Perce ivedConfidenceinResu lts... 116

4 .4 .6 Perce ivedUse fu lnessandEaseo fuse... 118

4 .4 .7 Pre ference... 122

4 .4 .8 Open-endedFeedback ... 122 4 .4 .9 Th ink-a loudProtoco lF ind ings ... 124 4 .5 D iscuss ion... 125

5 Conc lus ionsandFuture Work 133

5 .1 ResearchContr ibut ions... 134 5 .1 .1 Bund l ingA lgor ithm Mod ificat ionsandInteract ionSupport.. 134 5 .1 .2 UserStudyF ind ings... 137 5 .2 Future Work... 139 5 .2 .1 FurtherImprovemento ftheProposedSystem... 139 5 .2 .2 Studyo fD ifferentFactorsAffect ingtheBund l ingQua l ity... 141 5 .2 .3 FurtherEva luat ions... 142

B ib l iography 144

A UserStudy 156

B Test Onto logy Datasets 177

L istof Tab les

2 .1 L isto fpopu lara l ignmentsystemsa longw iththetypeso fa lgor ithms emp loyedw ith inthemtoca lcu late mapp ings . ... 20 3 .1 Summaryo fco lourencod ingsforthe mapp ingdepend inguponthe

stateandlocat ion . ... 68 4 .1 Summaryo fcharacter ist icso fthetestonto logydatasets ... 101 4 .2 Round-Rob inrotat iono ftasksandinter faces . LettheCogZsystem

beCandCogZ-easC e andcons idertheusedonto logydatasetsasO A

andO B ... 102 4 .3 Featureso fthepart ic ipantdemograph ics ... 106 4 .4 Stat ist ica lana lys iso fthed ifferencesinthet imetotaskcomp let iono f

theva l idat iontasks . ... 108 4 .5 Stat ist ica lana lys iso fthed ifferencesinthet imetotaskcomp let iono f

theadd it iontasks . ... 110 4 .6 Stat ist ica lana lys iso fthed ifferencesintheaccuracyo ftheadd it ion

tasks ... 112

4 .7 Stat ist ica lana lys iso ftheresponsesfortheperce ivedd ifficu ltyformap- p ingva l idat iontasks ... 114 4 .8 Stat ist ica lana lys iso ftheresponsesfortheperce ivedd ifficu ltyformap-

p ingadd it iontasks ... 115 4 .9 Stat ist ica lana lys iso ftheresponsesfortheperce ivedconfidenceon

correctnessfor mapp ingva l idat iontasks ... 117 4 .10Stat ist ica lana lys iso ftheresponsesfortheperce ivedconfidenceon

correctnessfor mapp ingadd it iontasks ... 118 4 .11Stat ist ica lana lys iso fthed ifferencesintheperce iveeaseo fuseand

use fu lnesso fthetwointer facesforthemapp ingva l idat ionandadd it ion tasks ... 120 4 .12Summaryo ftheva l idat ionresu ltso fthehypothesesforbothtypeo f

onto logya l ignmenttasks ... 126

L istofF igures

2 .1 Anexamp leonto logyo faun ivers itydoma in[29 ]generatedus ingOWLV iz [46 ]p lug ininProt´ eg´ e[60 ] ... 12 2 .2 Typ ica lflowo fanonto logya l ignmentprocess ... 16 2 .3 Screenshoto ftheinter faceo ftheAgreementMakersystem[15 ,16 ].. 33 2 .4 Screenshoto ftheinter faceo ftheCOMA++system[28 ] ... 33 2 .5 Asnapshoth igh l ight ingthec lutter ingprob lemin B izTa lk Mapper

system[67 ] ... 36 2 .6 V iewo fthe ma ininter faceo ftheCogZsystem[28 ] ... 38 2 .7 Thefish-eyezoomfeatureo ftheCogZsystem[28 ] . ... 40 2 .8 Resu ltso fpresent ingalargenumbero f mapp ingson CogZ ’sinter-

face[28 ] . Thel ightred-co lordottededgesrepresentthecand idate mapp ings ,edgesturndarkerwhentheknow ledgemanagerhoversover

themandthetoo lt ipisa lso madev is ib le ... 41 2 .9 Thesnapshoto fasystemfrom[45 ]present ingthe m igrat iondatabe-

tweend ifferentpartso fthewor ldus ing(a)w ithoutedgebund l ing(b)

w ithedgebund l ing ... 42

2 .10(a)Pr iortothebund l ingprocess(b)A fterthebund l ingprocess... 47

3 .1 The ma inv iewo ftheprototypew ithoutbund l ing ... 56 3 .2 V iewo fthebase l inesystemw ithnobund l ing . Thereddashedcurves

representthecand idate mapp ingsgeneratedbythesystem ... 60 3 .3 App l icat iono fedgebund l ingtothe mapp ingedges . Notethat map-

p ingswhosesourceanddest inat ionarenotv is ib lehavebeenfi ltered . 60 3 .4 Thetoo lt ipshow ingthein format ionaboutthe mapp ingsthatare

presentinthebund le ... 63 3 .5 Thepop-upw indowprov id ingthe mapp ingva l idat ionoperat ions ... 64 3 .6 Aspertheknow ledgemanager ’sse lect ion ,the“m in i-corne l l :Astronomy”

to“corne l l :Astronomy” mapp ing ’sco lourhasbeenchangedtob lack .. 65 3 .7 Thetoo lt ipshowstheconfirmed mapp ingsinredco lourw ithbo ldfont . 65 3 .8 Theconfirmedmapp ingsl istedinthepop-upw indowarea lsopresented

us ingredco lourw ithbo ldfontfo l low ingtheschemeadoptedinthe too lt ip ,andwhenaconfirmedmapp ingisse lectedtheco lourischanged togreenfromb lue ... 67 3 .9 Theco lourtrans it iono fthemapp ingedgedepend inguponthecurrent

state ... 67 3 .10System Arch itectureo ftheprototypeCogZ-e . Thein format ionflow

fromd ifferentmodu leshasbeenmarkedbyb lackarrows .Theexchange

o fin format ionisrepresentedus ingredarrows . Thenewmodu lesthat

havebeendeve lopedare markedin maroon ... 70

3 .11 Extendedtoo lt ipl ist inga l lthe mapp ingsconta inedinthebund le ... 74

3 .12 Pop-upw indowl ist ingthe mapp ingsthatwerev is ib leinthetoo lt ip .. 75

3 .13 Theconfirmed mapp ing“Organ isat ionto Organ isat ion”isshownin b lueco lourw iththetextinthetoo lt ipinredco lourw ithbo ldfont .. 76 3 .14 Theconfirmed mapp ingappear ingatthebottominredco lourw ith

bo ldtextinthepop-upw indowa longw iththerema in ingcand idate mapp ings ... 77 3 .15 Thesnapshoto ftheprototypea ftertheknow ledge managerhascon-

firmeda l lthe mapp ingso fthein it ia lbund le ... 78 3 .16 Thenew lycreatedmapp ingbetweenthesource“Presentat ion”concept

andthetarget“Ind iv idua lPresentat ion”conceptisshownbythetop b luel ineaswe l lasinthetoo lt ip ... 79 4 .1 D istr ibut iono ftheaggregatedresponsesaboutfam i l iar ity w iththe

doma ino fthetestonto logydatasets ... 107 4 .2 Averaget imetakentocomp letetheva l idat iontasks . Errorbarsare

reflect ingthestandarderroraboutthe mean . ... 108 4 .3 Averaget imetakentocomp letetheadd it iontasks . Standarderror

aboutthe meanisshownus ingtheerrorbars ... 110 4 .4 Averageaccuracyforadd it iontasksinpercentage .Standarderrorso f

meano fthedatasamp leisa lsoshownus ingtheerrorbars ... 112 4 .5 D istr ibut iono fresponsesforperce ivedd ifficu ltyforva l idat iontasks

(a)un ivers itydoma in(b)con ferencedoma in ... 113 4 .6 D istr ibut iono fresponsesforperce ivedd ifficu ltyforadd it iontasks(a)

un ivers itydoma in(b)con ferencedoma in ... 114

4 .7 D istr ibut iono fresponsesforperce ivedconfidenceforva l idat iontasks (a)un ivers itydoma in(b)con ferencedoma in ... 117 4 .8 D istr ibut iono fresponsesforperce ivedconfidenceforadd it iontasks

(a)un ivers itydoma in(b)con ferencedoma in ... 118 4 .9 Aggregatedresponsesforperce ivedeaseo fuseanduse fu lnessmeasures

forthe mapp ingva l idat iontasks . ... 120 4 .10 Aggregatedresponsesforperce ivedeaseo fuseanduse fu lnessmeasures

forthe mapp ingadd it iontasks ... 121

Chapter1

Introduct ion

1 .1 Mot ivat ion

Effect ivecommun icat ionandco l laborat ionamongpeop le ,organ izat ions ,andso ft- waresystemsisthekeytosuccessintoday ’scompet it ive wor ld . However ,such commun icat ionamongso ftwaresystemscanbecha l leng ingduetothe irindependent deve lopment .So ftwaresystemsarebu i ltbypeop le ;duetod isparatebackgroundsand cu ltures ,educat ion ,andlanguages ,so ftwaredeve lopersmayused ifferentterm ino logy toexp la inthesameconceptua lent ity[80 ] .Inadd it ion ,they mayproduced ifferent h ierarch ica ldecompos it ionso fd ifficu ltorcomp lexdoma insw ith intheso ftwarethey create . Th isleadstod ifferentcomputersystemsstor ingin format ionind ifferentfor- mats , mak ingitd ifficu ltforthemtoshareandinter-commun icate . There fore ,in orderforsuchsystemstosharein format ion ,aneffect ive mechan ismisrequ iredthat canprov idetrans lat ionsbetweend ifferentdataformats .

Onto log iesprov ideaconven ient mechan ismtorepresentin format ionaboutado-

ma inbyorgan iz ingthe ir majorconceptsandform ingh ierarch ica lre lat ionsh ipsbe- tweenent it ies . Theyhavebecomeanimportanttoo lforshar ingandre-us ingin for- mat ionamongacadem iaandindustry[29 ] ,andthe irusagetosupporttheshar ingo f in format ionbetweenmu lt ip leso ftwaresystemshasbecomeincreas ing lyimportantin recentyears[18 ,41 ,43 ] .

Wh i lecommun icat ionandshar ingisstra ight- forwardwhentwosystemsusethe sameonto logy ,cha l lengesex istwhenshar ingisrequ iredbetweensystemsthatuse d ifferentonto log iestorepresentthesamein format ion .Th ismayoccurwhensystems arebu i ltinpara l le l ,whenonesystemischangedorupdated ,orwhennewshar ing funct iona l ityisrequ ired .Inthesecases ,in format ionshar ingrequ iresonto logya l ign- ment( i .e . ,a mapp ingbetweentheent it iesinthepa iro fonto log ies)[42 ] . Sucha mapp ingcanthenbeusedtoautomat ica l lytrans latein format ionfromoneonto logy totheother . Forexamp le ,inthe Webserv icearea ,onto logya l ignmentsareused totrans lateone messageformato fane-commerceapp l icat ionintoanother ,com- p lete lyautomat ica l lyorsem i-automat ica l ly[43 ] . Th istrans lat ionisneededtocon- ductbus inesstransact ionsbetweend isparatedatabases ,inventorycontro lsystems , ande-commercesystems . Thus ,thecha l lengeistoobta inaccuratetrans lat ionsby reso lv ingheterogene ityissuesandd iscover ings im i lar it iesamongd ifferentonto log ies represent ingcomparab ledoma ins . Theso lut ionsresearchedanddeve lopedto meet th ischa l lengeareusua l lyre ferredtoas“onto logya l ignmentapproaches”inthel it- erature[30 ] .

Onto logya l ignmenthasbeenanact iveresearchareaforovertwodecades[66 ] .Itis

st i l loneo fthemostfundamenta l ,t ime -consum ing ,labor- intens ive ,andcruc ia ltasks

afewconceptstosevera lhundreds ,manua l lya l ign ingtwoonto log iesisnotpract ica l whenonto log iesarelarge . There fore ,automat iconto logya l ignmenta lgor ithmsare requ iredthatcanautomat ica l lymatchent it iesbetweenthetwoonto log ies . Numerous onto logya l ignmenta lgor ithmsex isttoper formth istask[66 ] ,w iththeoutputbe ing aseto fcand idate mapp ingsbetweenent it iesacrossthepa iro fonto log ies .

Fundamenta l ly ,onto logya l ignmentisad ifficu ltprob lemduetothecomp lex it ies o fhumanlanguage . Humanbe ingsperce ivethesameth ingsd ifferent lybynature[70 ] ; thuswhend ifferentdeve lopersdes ignonto log iesforthesamedoma in ,eachmaylabe l andstructures im i larconceptsandpropert iesd ifferent ly .Forexamp le ,oneonto logy deve lopermaylabe lthepr iceo fthet icketinanonto logyrepresent ingthedoma ino f atrave lagencymanagementsystemas“pr ice” ,butanotherdeve lopermaydefinethe labe las“cost”inad ifferentonto logyrepresent ingthesamedoma in . Furthermore , intheh ierarch ica ldecompos it iono ftheprob lemdoma in ,oneonto logy mayinc lude thet icketpr icew ith inalargert icketent ity ,whereasanotherm ightinc ludeitw ith in atrave lit inerary . Usageo fsuchd ifferentlabe l l ingsandh ierarch ica ldecompos it ions o fimportante lementso ftheconceptso fanonto logy mayresu ltinheterogene ity confl icts ,andconceptua ld iscrepanc ies[66 ] . Thea l ignmentprocessneedstoreso lve thesetofindthe matchesbetweentheg ivenonto log ies .

Eventhoughs ign ificantadvanceshavebeenmadeasaresu lto fnatura llanguage

process ing[36 ]andgraphmatch ingapproaches[62 ] ,theendresu ltso fa l ignmenta lgo-

r ithmsarese ldomcerta inandareo ftenincomp lete[29 ] . Asaresu lt ,fu l lyautomat ic

a l ignmentsystemsarenotfeas ib le .S incethehuman m indiswe l l-su itedto mak ing

senseo famb iguousandincomp leteknow ledgeaboutthe wor ld ,it maybebenefi-

c ia ltotakeadvantageo fsuchhumancapab i l it iesbyinc lud ingthemintheonto logy

a l ignmentprocess[2 ] .

Recent ly ,researchershavestartedtoana lyzeandg iveimportancetovar iousas- pectso fuserinvo lvementw ith inonto logya l ignmentprocesses[30 ] .In it ia l ly ,us ing a match inga lgor ithm ,suchsystemsproducecand idate mapp ingsbetweentheg iven onto log iesandpresentthemtoaknow ledgemanager . Acommonapproachtoaddress theuncerta intyaboutthesecand idate mapp ingsistoa l lowtheknow ledge manager tointeract ive lyva l idatethe mapp ings ,aswe l lasaddnew mapp ingsthatwerenot detectedbytheautomat ica l ignmenta lgor ithm . Wh i leana¨ ıveapproachm ightbeto s imp lyprov ideal isto fmapp ingsfromwh ichaknow ledgemanagercande leteoradd newmapp ings ,do ingsomakesitd ifficu lttocons idertheonto log ica lstructuredur ing th isprocess . Th isinturnm ightleadtoincorrect lyva l idatedorm issedmapp ings . A moreeffect iveapproachistopresenttheonto log iesandthe ir mapp ingsinav isua l mannerthatisspec ifica l lydes ignedtosupporttheva l idat ionandadd it iontasks ,and toprov ideinteract ivetoo lsthata idinthecomp let iono fthesetasks[28 ,30 ] .

Thev isua lcomponento fonto logya l ignmentsystemsnorma l lyrepresentsthetwo onto log iesastreestructuresone ithers ideo ftheinter face ,andgraph ica l lydep ictsthe mapp ingsasedgesbetweenthecorrespond ingent it ies .Thetreestructuresprov idean effic ientwayo frepresent ingtheh ierarch ica lnatureo ftheonto log ies ,anda l lowforthe nav igat ionamonglargeorcomp lexonto log ies . Therepresentat iono fthe mapp ings us ingedgesa l lowstheknow ledge managertov isua l lytracetheconnect ionbetween ent it iesintheonto log ies .

Onto log iesthatarebu i lttorepresentcomp lexdoma inknow ledgecanbevery

large . Whenasem i-automat ica l ignmentprocessiscarr iedoutonapa iro fsuch

cand idate mapp ingsthat mustbeva l idatedbytheknow ledge manager . Theva l i- dat ionprocessinc ludestrac ingind iv idua l mapp ings ,confirm ingthere lat ionsh ip ,as we l lasident i fy ingandadd ingnew mapp ings .Inordertocomp letetheva l idat ion task ,theknow ledge managerhastoprocessand man ipu lateeachandeverycand i- date mapp ing . Va l idat ingthese mapp ingsisthe mostfundamenta lbutprotracted taskinthea l ignmentprocess .

S incethev isua lspaceava i lab letoshowthe mapp ingedgesisl im ited ,prob lems mayoccur whenrepresent ingalargenumbero f mapp ings . Whenrepresent inga re lat ive lysma l lseto fmapp ings ,thespacecanbeusedeffect ive ly ,mak ingiteasyfor know ledgemanagerstoperce iveandunderstandthemapp ingsandcomparethepa ir o fonto log ies . However ,whenrea ll i feonto log iesarea l igned ,thenumbero fmapp ings producedisgreatandpresent ingthesemapp ingsw ith inthespaceava i lab lecreatesa v isua l mess .

These mapp ingsareconvent iona l lydrawnasstra ightl inesbetweenthesource andtargetonto log iestrees ,andina l lbutthes imp lestcasesw i l linc lude manyedge cross ings . Evenw ithasfewas20 mapp ings ,theusab i l ityo fsuchinter facesqu ick ly deter ioratesduetov isua lc luttercreatedbytheedgecross ingsw ith inthe mapp ing reg iono ftheinter face . Asaresu lt ,theknow ledgemanagermayexper ienced ifficu lty inobserv inggenera lpatternsortrendsw ith inthemapp ingset ,aswe l lasinchoos ing and man ipu lat ingapart icu lar mapp ing . Suchc lutter maya lsocauseprob lemsfor find ingaspec ific mapp ingorseto f mapp ings .

Wh i lesomestud ieshaveexp loredtheuseo fcurvedl inestorepresentthemapp ings

[31 ]orinteract iveh igh l ight ingtoa l lowtheknow ledgemanagertofocusonaspec ific

mapp ing[31 ,67 ] ,thefundamenta lprob lemo ftry ingto makesenseo fav isua l ly

c lutteredrepresentat ionrema ins . Thepotent ia lso lut ionforreduc ingtheamounto f v isua lc lutterandimprov ingthev isua lorgan izat iono f mapp ingsthatisproposedin th isresearchistouseedgebund l ing[45 ]ontherepresentat iono fthe mapp ings .

1 .2 Approach

Edgebund l ingistheprocesso fd istort ingtheshapeso ftheedgesinagraphto prov idepathsthatareeas ierforthehumaneyetofo l low[45 ] .Ithasbeenproposed forprov id inganorgan izedv iewo flargegraphs ,group ingthegraphedgesbybund l ing the ircommonpathwaystogether[45 ] .

Inthecontexto fonto log ies ,thebund l ingprocesscanbecons ideredasapotent ia l so lut ionforthev isua lc lutterprob lemdueto mapp ingedgescross ings .Bybund l ing theedgesw ith inanonto logy mapp inginter face , mucho fthev isua lc lutterw ith in themapp ingrepresentat ionsw i l lbee l im inatedduetothec luster ingo ftheedgesand thecross ingo fafewbund lesratherthan manyind iv idua ledges . Thegroup ingo f mapp ingedgeshe lpsinprov id inganoverv iewo fthe mapp ingsresu lts ,wh ich may he lpaknow ledge managertoper formthe mapp ingva l idat iontasksw itheaseand increasedper formance .Insteado fwork ingonind iv idua l mapp ings ,w ithbund l ing , theknow ledge managercanworkon mapp ingsets( i .e . ,thosethataregroupedinto bund les) .

Anotheradvantageprov idedbyedgebund l ingistheeasyident ificat iono fpoten-

t ia l lyinterest ingareasw ith inthesourceandtargetonto log iesfromwh ichalarge

numbero f mapp ingsareor ig inat ingorterm inat ing .Ident i fy ingtheseareascanhe lp

theknow ledgemanagerinfocus ingonimportantconceptsw ith inthesourceandtar-

getonto log ies . Duetothestructureo fonto log ies ,theseareas mayconta inpotent ia l cand idateconceptsforfind ingandadd ingnewre lat ionsh ipsthatwerem issedbythe automat ica l ignmenta lgor ithm .

Wh i leedgebund l ingaddsstructuretothev isua l izat iono fthe mapp ings ,itdoes soatthecosto fintroduc ingamb igu ity . Thedrawbacko fus ingbund l ingisthat the mapp ingedgesarenowgroupedtogether mak ingitd ifficu lttotraceas ing le mapp ingfromsourcetodest inat ion .S incetheknow ledgemanagerisrequ iredtov iew eachandevery mapp ingbe foreva l idat ingit ,thesameab i l ityisa lsorequ iredwh i le dea l ingw ithbund l ing . There fore ,inordertod isamb iguatethe mapp ingsconta ined w ith inabund le ,someinteract ion mechan ismisneeded . Assuch ,a long w iththe imp lementat iono fedgebund l ing ,aninteract ivefocus ingfeatureisa lsorequ ired , wh ichcana l lowtheknow ledge managertoeas i lyv iewandper formthere levant va l idat iontasksforeach mapp ingind iv idua l ly .

1 .3 Research Quest ionsand Methods

Thepr imarygoa lo fth isthes isistostudytheapp l icat iono fanedgebund l ingap- proachtoonto logymapp ingrepresentat ion . Thefo l low ingresearchquest ionsw i l lbe addressed :

1)Howcantheedgebund l ingtechn iquebeadoptedtothedoma ino fonto logy mapp ingrepresentat ion?

2) Whatarethebenefitsanddrawbackso fus ingedgebund l inginthecontexto f onto logy mapp ingva l idat ionandadd it iontasks?

Toanswerthesequest ions ,thefirststepwastoident i fyastate-o f-the-artonto logy

a l ignmentandv isua l izat ionsystem ,wh ichcou ldbeusedasabase l inesystem . The m in imumrequ irementso fth issystemweretohand letheinputo fsourceandtarget onto log ies ,per formtheautomat ica l ignmento ftheonto log ies ,andfina l lypresent thecand idate mapp ingsw ith inav isua linter face . Toaddressthefirstresearchques- t ion ,a ftertheident ificat iono fthebase l inesystem ,thenextstepwastostudythe constra intsandrequ irementso ftheonto logymapp ingrepresentat ionandmod i fythe genera lbund l ingprocesstocomp lyw iththeserestr ict ions .Inadd it ion ,tosupportthe a l ignmentre latedtasks( i .e . ,mapp ingver ificat ionandadd it ion) ,interact ionmethods weredeve lopedforbund led isamb iguat ion . Thefina lstepwastoconductanemp ir- ica leva luat iontostudythebenefitso fus ingedgebund l ingtosupportaknow ledge manager ’sonto logya l ignmenttasks .Theresu ltso fth iseva luat ionaddressthesecond researchquest ion .

1 .4 Organ izat ionof Thes is

Therema indero fthethes isisorgan izedasfo l lows . Thefo l low ingchapterout l ines

re latedworkperta in ingtoautomat iconto logya l ignment ,onto logya l ignmentinter-

faces ,andedgebund l ing ,a longw ithatheoret ica ld iscuss ionontheproposedbenefits

o fedgebund l ing .Chapter3exp la instheprocesso fper form ingedgebund l ingw ith in

thebase l ineonto logya l ignmentframework ,prov idesthedeta i lsregard ingtheimp le-

mentat ion ,andd iscussestheva lueo ftheapproachinthecontexto fthepr imarytasks

o f mapp ingva l idat ionand mapp ingadd it ion . Chapter4prov idesthe methodo logy ,

resu lts ,andd iscuss iono ftheemp ir ica leva luat ion , wh ichwasconductedtostudy ,

eva luate ,andcomparethebenefitsanddrawbackso ftheproposed methodincom-

par isontothestate -o f-the-artbase l inesystem .InChapter5 ,th isthes isconc ludes

w ithasummaryo fthepr imarycontr ibut ionso fth isworka longw ithanout l ineo f

futurework .

Chapter2

Re lated Work

Th ischapterbeg insbydescr ib ingtheconcepto fonto log iesindeta i l ,a fterwh ichsev- era lcommontypeso fa lgor ithmsthatareusedina l ignmentsystemsw i l lbed iscussed . Fo l low ingthat ,theimportanceo fknow ledge managersinthea l ignmentprocessw i l l bedescr ibed ,wh ichw i l lleadtothed iscuss ionabouttheinter faceso fthea l ignment systemstosupportknow ledge managers . Th issect ionw i l la lsoinc ludead iscuss ion abouttheva lueo fv isua l izat ionindes ign ingsuchinter faces ,aswe l lasthetheor iesand pr inc ip lesfromthefie ldo fin format ionv isua l izat ionthatapp lytotherepresentat ion o fonto logy mapp ings . Ad iscuss iononex ist ing mapp ingrepresentat ionapproaches andissuesw i l lconst itutethenextsect iono fthechapter . Deta i lsw i l la lsobeprov ided onthechosenbase l inesystemforth isresearch .

2 .1 Onto log ies

“Onto logy”isatermthator ig inatesfromthefie ldo fph i losophywhereitisused

andthenatureo fre lat ionsh ipsthatex istsbetweentheseth ings . Inthefie ldo f computersc ience ,th istermhasevo lvedtorepresentthefina lproductobta inedwhen anabstractands imp l ifieddescr ipt iono fadoma iniscreated[39 ] .Thekeyconceptso f thedoma inarerepresentedus ingc lasses ,andlog ica lre lat ionsh ipsarecreatedamong thembyknow ledgeexpertstoreflectacommonunderstand ingo ftheovera l ldoma in . Pr imar i ly ,onto log iesprov ideastandardvocabu larythatcanenab letheshar ing andre-useo fdoma inknow ledge[43 ] ,aswe l lasa l lowautomatedcomputerreason ing andana lys is[43 ] . Doma inonto log iesprov idestandarddefin it ionso fthedoma in conceptsandthelog ica lre lat ionsh ipsthatex istbetweenthem ,wh ichcanbeused bycomputerstoextractcommon mean ingautomat ica l lyfromtheg ivenin format ion [81 ] . Us ingthecapab i l it ieso fthedoma inonto logy ,largeamountso fdatacana lso becons istent lyc lass ifiedandaggregated[81 ] .

Atrad it iona ll ist-basedgraph ica lrepresentat iono fasma l lonto logyo faun ivers ity doma inisshowninF igure2 .1 . The ma inconceptsthatdefineth isdoma inare dep ictedasnodesandthere lat ionsh ipsthatex istbetweentheseconceptsareshown intheformo fl inesconnect ingthere levantconcepts .Th isisoneexamp leo fav isua l representat iono fanonto logy ;a morecomp leted iscuss ionononto logyv isua l izat ion ispresentedinSect ion2 .3 .2 .

Onto log ieshavebeenusedextens ive lyind ifferentapp l icat iondoma ins .Incom-

putersc ience ,oneo ftheimportantusageisintheareao ftheSemant ic Web[43 ] ,

inwh ichtheyfac i l itatethein format ionexchangebetweend ifferentsystems ,support

query-answerserv ices ,prov ideknow ledgebasesthatarere-usab le ,andenab lethe

interoperab i l ityamongvar iousheterogeneousin format ionsystems[39 ] . Theypr i-

mar i lyprov idesemant icsforthee lementso fawebdocument ,aswe l lasenab lethose

F igure2 .1 :Anexamp leonto logyo faun ivers itydoma in[29 ]generatedus ingOWLV iz [46 ]p lug ininProt´ eg´ e[60 ] .

semant icstobeusedbyinte l l igentin format ionretr ieva lsystemsandapp l icat ions [43 ] .

Thereare manytechno log iesandlanguagesthathavebeenintroducedtospec i fy onto log ies . XML(eXtens ib le MarkupLanguage)[6 ] ,isadata-descr ib inglanguage thatusestagstodescr ibeanddefinedataonthe Webinastructuredfash ion[18 ] . Itisthebas icunder ly ingtechno logythatcanbeusedtowr iteonto log ies ,wh icha lso prov idesthefoundat ionfor morespec ia l izedlanguagestoauthoronto log iessuchas RDF(ResourceDeve lopmentF ramework)[44 ]andOWL(Onto logy WebLanguage) [79 ] .

Theideabeh indtheselanguagesistoa l lowautomat icprocess ingo fthecontent

ratherthanshow ingpresentab lein format iontohumans[43 ] .RDF[44 ]wasdeve loped toincorporatemean ingto Webdatae lementsbyadd ingmach ine-readab lemetadata . Itwasthefirstlanguagethatwasdes ignedtosupporttheSemant ic Web[18 ] . RDF canbecons ideredasalayerontopo ftheex ist ingtechno log iesthatuseXML .RDF Schema(RDF-S)[7 ]extendsRDFandprov idesthebas icstructuresrequ iredtocreate thefundamenta lso fanonto logy ,suchasc lasses ,propert ies ,instances ,andre lat ion- sh ips(e .g . ,is-a ,ch i ld-o f)[18 ] .

Current ly ,the mostpopu laronto logydeve lopmentlanguageisOWL ,wh ichwas deve lopedbythe Web Onto logywork inggroup[14 ]underthe WWWConsort ium [79 ] . The OWLlanguagesupports moreexpress ib i l itythan RDF-S ,prov id ingthe ab i l itytodefineconstra intsandrestr ict ionsonthec lasses ,attr ibutes ,andinstances . There foreitisanimprovedvers iono fRDF-S ,prov id ingadd it iona lvocabu laryw ith forma lsemant ics[43 ] .

Duetothew idespreadusageo fonto log ies , manycurrent lyex istthat mode land

represents im i lardoma ins . Furthermore ,d ifferentnot ionsandtermscanbeusedto

descr ibethesameth ingsintheonto log iesusedw ith inso ftwaresystems . So ftware

deve lopershaveaspec ificseto frequ irementsandfactorstocons iderbe forebu i ld-

inganonto logyforaso ftwaresystem .Inadd it ion ,thereisaposs ib i l ityo fhuman

b iasbecauseo fd ifferentpercept ionsandunderstand ingso fthemean ingo faconcept .

Typ ica l ly ,awordoraseto fwordsareemp loyedtodescr ibeaconcept ,anditispos-

s ib lethatd ifferentcomb inat ionso fwords ,synonyms ,homonyms ,andotherre levant

featureso falanguageareusedtodefinethesameconcept ,wh ichcanbecon fus ing .

Th isindependenceinusageo flanguagebr ingsversat i l ityandbroadknow ledgeto

anonto logy ,but maya lsoleadtoanamb iguousorincomp letedefin it ionabouta

concept .

Toenab lecommun icat ionandshar ingo fin format ionbetween mu lt ip lesystems , d iscrepanc iesbetweenthe ironto log iesmustbereso lvedtoprov idetrans lat ion .Thus , in format ionshar ingrequ iresonto logya l ignment ,produc inga mapp ingbetweenthe ent it iesinthepa iro fonto log ies[42 ] .Inadd it ion ,a l ignmento fonto log iesrepresent- ingas im i lardoma inisrequ iredtoprov idea moreintegratedv iewo fthedoma in . Theemerg ingcha l lengeistoreso lveheterogene ityissuesandd iscovers im i lar it ies amongd ifferentonto log iesrepresent ingcomparab ledoma ins . Theapproachesand systemsdeve lopedtoper formth ischa l leng ingtaskarere ferredtoas“onto logya l ign- ment”[30 ] .

2 .2 Onto logy A l ignment

Onto logya l ignmentisarequ irementfor manyonto logy-dependentso ftwaresystems

inorderforthemtocommun icate w itheachother . Forexamp le ,Semant ic Web

app l icat ionsrequ ireeffect iveandstandardwayso fcommun icat ingbetweend ifferent

serv iceprov iders[43 ] . Theseserv iceprov idersexposecharacter ist icso fthe irserv ices

us ingonto log ies .Inordertoexchangein format ion ,theseonto log iesneedtobea l igned

[23 ] . Anumbero fd ifferenttypeso fa l ignmenta lgor ithmshavebeenproposedinthe

l iteraturetofindmapp ingsbetweentheconceptso ftheg ivenonto log ies . Genera l ly ,a

s ing leor mu lt ip letypeso fa l ignmenta lgor ithmsareusedinoverahundredd ifferent

proposeda l ignmentsystems[37 ] . Duetoth isw idespreadscope ,anout l ineo fthe

common lyusedtypeso fa lgor ithmsw i l lbeprov idedinth issect ion . Furthermore ,

abr ie fd iscuss ionaboutsystemsthatincorporatethosetypeso fa lgor ithmsw i l lbe

presenteda fterprov id ingaforma ldefin it iono ftheonto logya l ignment .

2 .2 .1 Defin it ion

Cons ider ingthesourceonto logyas Os andthetargetonto logyasOt,thesetwo onto log iesareprov idedasinputtotheonto logya l ignmentprocess ,wh ichproducesthe one-to-oneorone-to-many mapp ingsind icat ingthere lat ionsh ipthatex istsbetween there levantconcepts . W iththehe lpo fthesemapp ingre lat ionsh ips ,itcanbededuced thatthecorrespond ingonto logyconceptsrepresentthesameconceptua lent ity .

Themapp ingsaregenera l lypresentedintheformo f(Cs ,Ct ,i) ,whereCsandC t representtheconceptsfromsourceandtargetonto log ieswh i leishowstheconfidence leve lo fs im i lar itycomputedbythea l ignmenta lgor ithm .Therangeo ftheconfidence leve lva luesisnorma l lybetween0and1 .Inordertobecons idereda mapp ing ,the confidenceleve lo fthecomputeds im i lar ity must meetapre-definedthresho ldva lue . Ap ictor ia lrepresentat iono fth isprocessisshowninF igure2 .2 .

2 .2 .2 A lgor ithm Types

Anumbero fd ifferentapproachesex istforper form ingautomat iconto logya l ignment .

The mostcommonapproachusedbytheonto logya l ignmentsystemsisstr ing-based

match ing , wh ichemp loysaname-match ingapproachtothelabe lso ftheonto logy

concepts . Inthesestr ing-based methods ,thecomputat iono fthes im i lar ityva lue

betweentheconceptsisbasedontheamounto fre latednessamongthe irnamesor

labe ls .Certa instr ingnorma l izat iontechn iquesareapp l iedtotheinputstr ingsbe fore

theana lys iso fs im i lar ityiscarr iedout(e .g . ,casefo ld ing ,encod ing ,remova lo fb lanks

F igure2 .2 : Typ ica lflowo fanonto logya l ignmentprocess .

[37 ]) . Theoutcomeo ftheinputtextstothestr ing-basedtechn iquescanbereported asexact ,wh ichimp l iesthattheyareequa l ,orapprox imateuptoaleve lo fconfidence , wh ichisobta inedbyus ingd ifferents im i lar itymetr ics .Examp leso fstr ingcompar ison techn iquesinc ludeed itd istance ,soundexindex ,andprefix/suffixcompar ison[37 ] . A lthoughstr ing-basedtechn iquesprov idesupportfordeterm in ingconcepts im i lar ity , theycannotovercomethecomp lex ityintheusageo falanguagetodescr ibethose concepts . A l ignmentsystemsthatusestr ing-basedtechn iquesinc ludeCOMA[20 ] andCOMA++[1 ] ,OLA[27 ] ,Anchor-Prompt[61 ] ,andS-Match[36 ] .

Inordertoovercomethed ifficu ltyfacedbythestr ing-basedapproaches ,language-

basedapproachesareaddedtocomp lementthestr ing-basedapproaches . Theypro-

v ide moreadvancedsupportforconcept- labe l match ingbyincorporat ing methods

tocounterthecomp lex ityintroducedbylanguagesduetotheuseo fsynonyms ,

homonyms ,andphrases .Some methodsinc ludetoken izat ion ,remova lo fstopwords , andreduct iono faphraseorsentencetoitsstemwords[37 ] . Onto logya l ignmentsys- temsthatusethestr ing-basedtechn iquesaswe l laslanguage -basedana lys iso ftext inc ludeCOMA[20 ] ,COMA++[1 ] ,OLA[27 ] ,Anchor-Prompt[61 ] ,andS-Match[36 ] . Thestr ing-basedandlanguage-basedana lys istechn iquesofferasyntact icleve lo f match ing . However ,thequa l ityo fa l ignmentcanbegreat lyimprovedbyper form- ingsemant ic match ingana lys isontheconceptlabe ls .Inc lus iono fdoma in-spec ific thesaur iandlex ica ldatabases(e .g . , WordNet[33 ])forthematch ingstrategyenab les semant ic-basedcompar ison ,wh ichiso ftenre ferredtoasl ingu ist ic-based match ing [37 ] .S inceonto logyconceptsareorgan izedintheformo fh ierarch ica lre lat ionsh ips , theusageo fsynonyms ,homonyms ,etc . ,(prov idedbyal ingu ist icresource)inthe match ingstrategycanbeadvantageous . Do ingsoa lsohe lpsindeterm in ingthe natureo fthere lat ionsh ipsthatm ightex istamongtheconceptlabe ls ,suchasequ iva- lenceorgenera l izat ion[37 ] .Systemsthatper formmatch ingus ingl ingu ist icresources inc ludeOLA[27 ] ,Cup id[55 ] ,andCOMA[20 ] .

Thea l ignmentstrategycanbefurtherimprovedbyinc lud ingtype-basedmethods , wh ichusein format ionthatdefinesthetypeo fconcept(e .g . ,integer ,float ,str ing , date ,etc .)tofindappropr iate matches .Inadd it ion ,thel im itso ftheva luesonsuch conceptsisusedtoca lcu latethes im i lar ityamongtwoconceptlabe ls .Forexamp le ,i f aconceptfromanonto logyiso ftypeintegerandhasarangeo f0to2500 ,itcanbe markedasre levanttoas im i larconceptw ithad ifferentlabe linad ifferentonto logy w iththesametypeandrange[37 ] . Examp leso fsystemsthata lsouseth is method areOLA[27 ]andCOMA[20 ] .

A longw ithaboveapproaches ,someonto logya l ignmentsystemsa lsoaddtech-

n iquessuchasdec is ion-theoret icapproaches[72 ]thataskfortheknow ledgemanager ’s feedback( i .e . ,acceptorre jecta l ignments)tose lectfromtheava i lab lea l ignment strateg ies( i .e . ,semant ic ,lex ica l ,structura l)fortheg ivenpa iro fonto log ies[21 ] .For examp le ,i ftheknow ledge manageracceptsthe mapp ingsuggest ionthatwasfound us inglex ica la l ignmentstrategy ,itsimportancew i l lincreaseforthenexta l ignment taskamongothera l ignmentapproaches .Inotherwords ,thenextt imetheknow l- edge managerw i l lbeprov idedw iththesuggest ionproducedbythelex ica lstrategy ontopo fothers . Us ingtheknow ledgemanager ’sfeedbackinthea l ignmentprocessis h igh lydes irab le ,butitisd ifficu lttoach ieve[9 ] .Systemsthatusedec is ion-theoret ic approachesareCOMA[1 ]andQOM[23 ] .

Thecurrentstate-o f-the-art methodfor match ingisstructura l-based match ing , wh ichusestheh ierarch ica lstructureo ftheconceptinadd it iontothein format ion prov idedbythesames ing leconceptandfo l low ingothertr iv ia la l ignmentstrateg ies inordertocompute mapp ings[37 ] . The ma inideabeh indth istypeo f match ing istocons idertheonto log iesasgraphs ,andcomparetheh ierarch ica lre lat ionsh ips o fd ifferentconceptsassub-graphsus inggraph match inga lgor ithms . Forexamp le , i fthech i ldconcepto ftheparentconceptisthesameintermso flabe landother attr ibutestoach i ldo fad ifferentparentconcept(eveni ftheparentconceptslabe ls arenots im i lartoeachother) ,theyshou ldbe matchedtoeachother .

Inth isd imens ion ,s im i lar ityflood ing[56 ]isasuccess fu landcommontechn ique

usedinthecontexto fstructure match ing .Inth is method ,theideaisthati fthe

nodesares im i lartoeachother ,the irne ighborsshou lda lsobes im i lar .Th isapproach

propagatesth iss im i lar itydefin it iona longthedeeperleve lso finher itanceo fthecon-

find ingthere latedsemant icsintheonto log iesus ingthestructura ls im i lar ityana lys is . In it ia l ly ,theg ivenonto log iesaretrans formedintoalabe ledgraphstructure .The know ledge managerthense lectssomeanchors( i .e . , ma inent it ies)w ith inthegraph , orthesystemcana lsoprov idethoseautomat ica l lybyper form inglex ica l match ing . Thea lgor ithmthenana lyzesthesubgraphl im itedbytheseanchorsanddeterm ines wh ichc lassesfrequent lyappearins im i larpos it ionsons im i larpaths . Thesec lasses arel ike lytorepresentsemant ica l lys im i larconcepts . Theresu ltsproducedbyth is approachareprom is ing ;there foreitisnowusedin manya l ignmentsystemsasthe under ly inga l ignmenta lgor ithm[3 ,31 ]inc lud ingPROMPT[59 ] , COMA[1 ] , OLA [27 ] ,QOM[23 ] ,andR iMOM[54 ] .

S inceas ing letypeo fa l ignmenta lgor ithmcannotprov ideaccurateandcomp lete a l ignmentresu lts , mu lt ip lea l ignmenta lgor ithmtypesareincorporatedtocompute thea l ignments .Start ingfrombas ica l ignmentapproachessuchasstr ing-basedand language-basedtoo ls ,a l ignmentssystemsinc ludecomb inat ionso fd ifferenttypeso f a l ignmenta lgor ithmstocompute moreaccurateresu lts . Tab le2 .1presentsthel ist o fpopu lara l ignmentsystemsa long w iththetypeo fthea lgor ithmthathasbeen emp loyed .

2 .2 .3 HumanInvo lvement W ith inthe Onto logy A l ignment Process

Integrat ingd ifferentonto log iesandcomput ingassoc iat ionsbetweenthe irconcepts

iscons ideredad ifficu lttask . Desp itebe inganact iveresearchareafor manyyears

[30 ] ,thefu l lautomat iono ftheonto logya l ignmentprocessthatcanproducequa l ity

Tab le2 .1 : L isto fpopu lara l ignmentsystemsa long w iththetypeso fa lgor ithms emp loyedw ith inthemtoca lcu late mapp ings .

A l ignmentSystems A lgor ithmTypes

Str ing&Language Semant ic Type Dec is ion-theoret ic Structura l

COMA X X X X X

COMA++ X

OLA X X X X

PROMPT X X

S-Match X

Cup id X

QOM X X

R iMOM X

Anchor-Prompt X

mapp ingsonitsownisfarfrombe ingposs ib le[37 ] . Duetothecomp lex ityo fhuman

languageandvar iat ionsinhowthesamein format ion maybeencodedind ifferent

onto log ies ,theoutputo fautomat iconto logya l ignmenta lgor ithms mayinc ludeer-

rorsorom iss ions[66 ] . Hence ,thereisaneedtoincorporatehumandec is ion-mak ing

w ith intheonto logya l ignmentprocess . Thehuman m indisbettersu itedtounder-

stand ingthenuanceso flanguagethanautomat ica lgor ithms ,andisveryeffect iveat

mak ingdec is ionsbasedonincomp leteorconfl ict ingin format ion[74 ] . Thema inidea

istoproducebetterqua l ity mapp ingsbytak ingadvantageo fahuman ’sversat i le

know ledgeaboutthedoma inandab i l itytointerpretlanguage ,comb inedw iththe

effic ientab i l ityo f mach inesforstor inglargeamountso fin format ionandper form ing

repet it ivetasks[74 ] .

Humansaregoodatdeve lop ingnove lso lut ions ,adapt ingtothecurrents ituat ion , chang ingthestrategyi ftheor ig ina lso lut ionfa i ls ,andmak ingdec is ions[74 ] . Onthe otherhand , mach inescanoperatew ithvastamountso fdata ,reca l lin format ionac- curate ly ,per formrepet it ivetasksqu ick ly ,andcanma inta inper formanceforalonger t imeeveni fcarry ingoutmu lt ip letaskss imu ltaneous ly[74 ] .There fore ,itisanact ive areao fresearchtocomb inethesehumancapab i l it iesw ithimmenseab i l it ieso f ma- ch ines ,andfindagoodba lancebetweenautomat ionandmanua lworkinper form ing tasksathand[73 ] .

Systemscomb in inghumanand mach inecapab i l it iesinthecontexto fonto logy a l ignmentarere ferredtoassem i-automat icapproachesinthel iterature[28 ]andare ga in ingimportanceintheonto logya l ignmentresearchdoma in . Theyaregenera l ly compr isedo ftwoma inparts :firstistheunder ly inga l ignmenta lgor ithmthatisused bythesystemtofindthecand idate mapp ingsset ,andsecondisthedes igno fthe inter facethatisprov idedtotheknow ledge managerinordertosupportonto logy a l ignmenttasks .

Thesem i-automat icapproachesbu i ldupontheworko fautomat ic methods ,pro-

v id ingin format ionaboutthecand idatemapp ingstoa l lowtheknow ledgemanagerto

e itherconfirmorre jecteach mapp ing . Th is mapp ingva l idat ionprocessis moreeffi-

c ientthanmanua la l ignmentduetothefocuseddec is ionmak ingthatissupportedby

thesuggest iono fcand idate mapp ingsproducedbytheautomat ica lgor ithms .S ince

it maybeposs ib lefortheautomat ica lgor ithmsto m issimportant mapp ings ,these

sem i-automat icapproachesmusta lsoa l lowtheknow ledgemanagertomanua l lyadd

mapp ings .

Sem i-automat icapproachesforonto logya l ignmenthavebecomecruc ia ls inceau- tomat icprocessescannotsat is fythequa l ityrequ irementso f mapp ingre latedtasks [37 ,29 ] . Invo lv inghumansinth isprocesscan makethea l ignmentprocess more effic ient ,feas ib le ,anduse fu l[37 ] . W iththe irversat i legenera lknow ledge(wh ichis d ifficu lttoreproduceautomat ica l lyin mach ines)a longw iththesoph ist icatedand amp leprocess ingpowero fthe irv isua lsystem ,humansbr inginva luab leadvantages inso lv ingprob lems[29 ] . There fore ,inadd it iontothedeve lopmento fthestate-o f- the-arta l ignmenta lgor ithms ,care fu lcons iderat ionisa lsog iventothedes igno fthe inter faceo fthesesystemsthatfac i l itatestheinteract iono fknow ledgemanagersw ith thesesystems[37 ] .

2 .3 Onto logy A l ignmentInterfaces

A w iderangeo finter faceshavebeenproposedtosupportthehumane lemento f mapp ingva l idat ionandmapp ingadd it ionw ith insem i-automat iconto logya l ignment systems[28 ,37 ] . Acommonthemeamongtheseinter facesisthegraph ica lrepresen- tat iono fboththeonto log iesthemse lvesaswe l lasthemapp ings . Ratherthans imp ly prov id ingatextua ll isto fcand idate mapp ings ,theseinter facestakeadvantageo f in format ionv isua l izat iontechn iquestoconveyboththestructureo ftheonto log ies andthe mapp ingin format iontotheknow ledge managersinagraph ica lformat .

2 .3 .1 Informat ion V isua l izat ion

In format ionv isua l izat ion[82 ]isaninterd isc ip l inaryfie ldthatcomb ineshuman-computer

interact ion ,userinter facedes ign ,cogn it ivepsycho logy ,computergraph ics ,andin-

format ionsystemstobr idgethegapbetweenhumansand mach ines . Thecoreidea istobu i ldinteract ivev isua l methodstorepresentabstractdatainordertoamp l i fy cogn it ionandhe lpinuncover ingd ifferentaspectsaboutthedata .In format ionv i- sua l izat ionenhanceshumancogn it ionbyus ingcomputersupported ,interact ive ,and v isua lrepresentat ionso fabstract ,non-spat ia l ,sem i-structured ,orh ierarch ica ldata [49 ,53 ,82 ] . Thev isua l methodsdeve lopedforrepresentat ionso fabstractdatatake advantageo fthesw i ftprocess ingcapab i l ityo fthehumanv isua lapparatus[82 ] .

Thev isua lpercept ionsystemo fhumansisverypower fu landqu ickinautomat- ica l lyinterpret ingcerta inv isua lfeatures .In format ionv isua l izat iontakesadvantage o fth isqu ickprocess ingab i l itytoenhancetheunderstand ingandd iscoveryo fnew ins ightsaboutinvest igat iona ldata[53 ] . Thein format ionv isua l izat ionfie ldhasbeen ut i l izedtoaddressthed ifficu ltprob lemo fonto logya l ignment ,wh ichhasresu ltedin anumbero fd ifferentonto logya l ignmentinter faces[1 ,20 ,27 ,31 ,37 ,59 ,66 ] .

Cons ider ingtheamountandcomp lex ityo fdatathatisava i lab leford ifferentpur- poses ,effect ivev isua l izat ionrepresentat ionsforth isdataarerequ ired . Thesecan bedeve lopedus ingre levantin format ionv isua l izat iontheor iesandpr inc ip lestoe f- fect ive lyv iew ,eva luate , man ipu late ,andexp loresuchdata . V isua landinteract ive representat ionscanbefurtherenhancedbytak ingintocons iderat iontheuser ’sex- pectat ionsfromtheava i lab lein format ion . Theimp lementat iono ftheideasandthe chosendes ignmethodsforthepresentat iono fin format ion ,a longw iththeeva luat ion o ftheproductarea lsoimportantaspectso fin format ionv isua l izat ion .

Inthein format ionv isua l izat iondoma in ,theor iesandpr inc ip lesthathe lpinun-

derstand ingthework ingo fthehuman m indwhenperce iv ingobjects ,interpret ing

data ,andin ferenc inglog ica lconc lus ionsouto fv isua lrepresentat iono fthepresented

in format ionarea lsostud iedextens ive ly . Thereare manywe l l-definedtheor iesand pr inc ip lessuchaspre-attent iveprocess ing , Gesta ltLaws ,andco lourtheory ,wh ich canbeeffect ive lyusedtobu i lduse fu lv isua l izat ions .

Onephenomenonthatoccursconstant lyinthehumanv isua lsystemisre ferredto aspre-attent iveprocess ing .Thev isua lsystemo fthehumanbra inoperatesnatura l ly ataveryfastrate ,wh icha l lowsfastandseem ing lypara l le lprocess ingo fin forma- t ionassoonasitentersthroughtheret ina . Bystudy ingthev isua le lementsand cond it ionsthattr iggerpre-attent iveprocess ing ,dataattr ibutescanbe mappedto v isua le lementsthath igh l ightimportantaspectso fthepresentedin format iontoen- ab lequ ickprocess ing . The ma innot ionisthatthein format ionthatisrequ iredto beprocessedpre-attent ive ly ,needstorepresentedcare fu l lysothatitstandsoutfrom otherin format ion . Anotherimportantfactisthaton lyasma l lnumbero fitemsthat arev isua l lyd ifferentfromtherestcanbepre-attent ive lyprocessed .Th isnumbercan befoundbyper form ingrepeatedexper imentsfortheg ivenin format ionandbymea- sur ingtheresponset imeo ffind ingthein format ionfromtherest . Fa i l ingthat ,the pre-attent iveprocess ingcannotbeach ievedasthehumanv isua lsystemw i l lnotbe ab letodetectanyd ifferencequ ick lyandfocusontheimportantin format ion ,wh ich w i l lleadtothetyp ica lopt iono fper form ingaser ia lsearchtolookfortherequ ired in format ion .

Thereareanumbero fd ifferentv isua lvar iab lesthatcanbepre-attent ive lypro-

cessed(e .g . ,curvature ,l ineor ientat ion ,l inew idthshape ,s ize ,number ,etc) . Acurved

l inerepresent ingava luab lee lemento fin format ioncanbepre-attent ive lyprocessed

andqu ick lyident ifiedwhena l ltheotherdatanearth iscurvedl ineisshownasstra ight

canbeincreasedtoenab leitsqu ickprocess ingwh i lekeep ingw idthso ftheotherl ines constant .S im i lar ly ,al im itednumbero fova lshapescanbeprocessedsw i ft lyamong alargenumbero fc irc leshapesonag ivend isp lay . Furthermore ,ahugesquarecan beeas i lyd ifferent iatedfromtheseto fsma l lersquares .S im i lar ly ,amongthesetso f acerta innumbero fc irc les(e .g ,fourc irc les)c lose lyp lacedtoeachotherd istr ibuted overaspace ,asetconta in ingasma l lernumbero fc irc lesfromtherest(e .g . ,on lyone) canbesw i ft lyd ist ingu ished . Otherv isua lvar iab lesact ivat ingpre-attent iveprocess- ingarea lsoava i lab lethancanbeusedforpresent ingd ifferentk indso fin format ion w ith inin format ionv isua l izat ionsystems[82 ] .

Inordertodes igneffect ivein format ionv isua l izat ionsystemsforusers ,itisim- portanttostudyandunderstandhowhumansperce ivepatternsandinterpretin for- mat ion .Inth isd irect ion ,animportantstepwastakenbyagrouppsycho log istsin 1912fromtheGesta ltschoo lo fpsycho logy ,whodes ignedaser ieso fexper imentsand per formedanumbero feva luat ionstoexam inepatternpercept ion[82 ] . Theresu lts o ftheseeva luat ionsarecommon lyknownastheGesta l tLawso fPatternPercept ion inthefie ldo fin format ionv isua l izat ion .

TheGesta ltLaws[50 ]fundamenta l lydea lw ithtwotypeso fconcepts :percept ion o fre lat ionsh ips ,andpercept iono fforegroundfrombackground .Ingenera l ,a l lthe Gesta ltLawsperta intounderstandhowthehuman m indperce ivespatterns . How- ever ,threeGesta ltlawsthatfocusonhowhumansautomat ica l lyin fertheex istenceo f are lat ionsh ipbetweenth ingswhentheyarenearoneanother ,v isua l lys im i lartoeach other ,andconnectedtooneanother ,arere levantandimportantforth isresearch .

Thelawo fprox im i tysuggeststhatobjectsthatarepos it ionedneareachotherare

automat ica l lyperce ivedtobere lated . Duetotheuse fu lnesso fth isspat ia lprox im ity

interpretat ion ,itiscommon lyusedasades ignpr inc ip leinin format ionv isua l izat ion systems[82 ] . V isua lobjectsthatrepresentdatabe long ingtothesamegroupcanbe p lacednearbyeachothertofac i l itatetheautomat icpercept ionaboutthe irre lated- ness .Itiscons ideredtobethes imp lestwaytoaccentuatethere lat ionsh ipsbetween d ifferentdatae lementsandiscommon lyusedtov isua l lyrepresentc lusters[82 ] .

Asemphas izedbythelawo fs im i lar i ty,v isua lobjectsthataredrawns im i lar ly areperce ivedtobere lated[82 ] . Th islawiscommon lyusedasades ignpr inc ip le whenbu i ld inguserinter faces . Forexamp le ,same-s izeb lackco lourfi l ledc irc lesw i l l beperceptua l lygrouped ,whenp lacedinthesameinter faceassame-s izec irc lesthat arefi l ledw ithredco lor . There fore ,s im i lartypeo fv isua ltechn iquesaregenera l ly usedtoshowe lementsthatrepresentre lateddata .

Thelawo fconnec tedness[82 ]hasresu ltedinacommon lyuseddes ignpr inc ip leto showre lat ionsh ipbetweentwoobjects . Th islawassertsthatthes imp lestand most effect ivewayo fexpress ingthatanyk indo fre lat ionsh ipex istsbetweentwograph ica l objectsisbyconnect ingthemthroughal ine[82 ] . Th isdes ignpr inc ip lehasbeen usedandextendedinto manygraph ica lformsandstructuresthatareusedtoshow re lat ionsh ipsbetweenconta inedobjects(e .g . ,graphs ,trees ,node- l inkd iagrams) .

TheOpponentProcessTheoryo fCo lour[57 ]prov idesdeta i lsabouttheinterpre-

tat iono fco lorbythehumanv is ionapparatus .Itdescr ibestheprocessthroughwh ich

thehumaneyecanperce ivethed ifferencebetweenco lorsandthewaysinwh ichth is

rawin format ionisprocessedw ith inthehumanbra in . Thefactthatwecanperce ive

d ifferentco lours ,isafunct iono fourconesens it iv itytod ifferentwave lengthso fl ight .

Theab i l ityforthe m indtoeas i lydeterm inethed ifferencebetweenredandgreen ,

o fCo lour .

Accord ingtoth istheory ,thehumanbra incomb inesthest imu lusfromtheava i l- ab lethreecones( long , med ium ,short)be foreprocess ingthem ,wh ichresu ltsins ix e lementaryco loursthatareperce ivedasopponentpa irsa longthreechanne ls :red- green ,ye l low-b lue ,andb lack-wh ite . Wecanseethed ifferencesbetweenco lourson thesechanne lsmuchmoreeas i lythanw itharb itraryco lours .Forexamp le ,aredob- jectcanbeseenw ith inamajor ityo fgreenobjectsmuchbetterthani fitissurrounded byye l lowobjects . Us ingthegu ide l ineso fth istheory ,thes ixd ifferentco loursform ing thesechanne lsareeffect ivecho icestoencodethed ist inctnesso fd ifferenttypesdata attr ibutes[82 ] . Objectsthatareco loredus ingtheses ixpr imaryco lorsareautomat- ica l lyperce ivedasd ifferent ,andcanbecare fu l lyusedtorepresentd ifferentk indso f in format iononthesamescreen . Th istheorya lsoprov idesgu ide l inestouseco lorsto presentin format ionconta in ingorderandamountorintens ity .Forexamp le , mov ing a longtheb lack-wh itechanne l ,adarkerco lorcanbeusedtofi l lashapeorarearep- resent ingab iggeramountorh igherrank ,andal ightershadeo fthesameco lorcan beusedtoshowalessamountorlowerrank[76 ] .S im i lar ly ,rangeo fnegat iveva lues topos it iveva luescanbeshownus ingthered-greenco lourchanne l .

Anotherimportantaspecto fin format ionv isua l izat ionisthesupporto finterac-

t ion .Itisassumedthat manyin format ionv isua l izat ionsystemstodayhavebas ic

interact ionfeaturessuchaszoom ing ,fi lter ing ,andfocus ing .Interact ionistyp ica l ly

integratedintothev isua lrepresentat ions ,wh icha l lowsforachangeinthepresented

in format ion[85 ] . W iththeinteract ionsupport ,userscanadm in isterand man ipu-

latethepresentedin format ionaccord ingtothe irunderstand ingandrequ irements .

There fore ,interact iontechn iquescanimprovetheuser ’scogn it ion ,a l low ingthemto

eas i lyprocessandinvest igatethedataatadeeperleve l ,andthen makethere levant dec is ions .In format ionv isua l izat ionsystemsw ithoutinteract ionst i l lex ist ;theyare bas ica l lystat icimagesandaveryl im itednumbero ftaskscanbeper formedus ing suchsystems[19 ] .Intheabsenceo finteract ionsupport ,thesystemscaneas i lybe c lass ifiedasnotuser- fr iend lyandthe irva luecanbeadverse lyaffected ,spec ifica l ly thosesystemsthatdea lw ithlargedatasets[85 ] .

2 .3 .2 Representat ionof Onto log ies

Thefie ldo fin format ionv isua l izat ionprov idestheor iesandpr inc ip lestodea lw ith comp lexandlargeamountso fdata . S inceonto log iesconta inlargeco l lect ionso f datastructuresthatrepresentconstructso fdoma ins ,v isua l izat ionhasanenormous potent ia lindea l ingw ithonto log ies[53 ] . Consequent ly , manyonto logyv isua l izat ion systemsincorporated ifferentv isua l izat ion mechan ismsforpresent ingonto log ies .

Thepurposeo fprov id ingonto log iesinav isua lformatistoa l lowknow ledge managerstotakeadvantageo fthe irv isua lprocess ingcapab i l it ies . W ithaneffect ive v isua lrepresentat ion ,know ledge managersareab letoread i lyperce ive ,interpret , and makesenseo fthefeatures ,re lat ionsh ips ,andstructureamongthedata[10 ] . There fore ,show ingonto log iesingraph ica lformatenhancestheknow ledgemanagers ’ cogn it iveab i l it iestov isua l lyprocessthepresentedonto log ies ,wh ichcanhe lpthem ineffect ive lyper form ingtheassoc iatedtasks .

Becausetheonto log iesthemse lvesarestructuredasah ierarchyo fconcepts ,the

log ica lmethodforrepresent ingthemisinatreestructure[48 ] .Th isapproachisused

inv irtua l lya l lo fthepr iorresearchononto logya l ignmentinter faces[37 ,53 ] . The

structureo fonto log iesa longw iththe irre lat ionsh ipsareshownasah ierarchywhen tree-basedrepresentat ionsareemp loyed . Underth iscategory ,thesystemsgenera l ly presentonto log iess im i lartothew indows-exp lorerv iew[48 ] . Manysystemsthatare spec ifica l lydes ignedforonto logydeve lopmentfa l linth isgroup ,inc lud ingProt´ eg´ e [60 ] , OntoEd it[77 ] ,and OntoRama[24 ] . O ftenknow ledge managersarea lready fam i l iarw iththesemethods ,there forethesesystemsgenera l lyhaveh ighacceptab i l ity rateanduse fu lness[48 ] . S incetheonto log iesthemse lves maybe muchlargerthan whatcanfitonaregu larcomputerscreenevenus ingthetreestructure ,interact ive featuressuchasnodeco l laps ing/expans ion ,vert ica lscro l l ing ,andzoom ingareo ften imp lemented .

T ree-basedrepresentat ionsareh igh lystructuredandregu larinthe irin format ion layouts ,andares imp letoimp lementandd isp lay . Th isrepresentat ionoffersac lear v iewo fthec lassnamesandh ierarchy ,andthelabe lsrepresent ingthe mean ingo f thenodes .S inceeverynodeisind iv idua l lypos it ionedanda l locatedadefinedspace , tree-basedrepresentat ionsgenera l lyhavenoprob lemsintermso fover lapp ingand occ lus iono fnodelabe ls[48 ] .

However ,researchersarguethatthetree-basedrepresentat ionstyp ica l ly make

ineffic ientuseo ftheava i lab lescreenspace[32 ] . Theroots ideo fthetreeisle ft

comp lete lyempty ,lead ingtoovercrowd inginotherparts .Eventhetreeso fahundred

nodeso ftenneedmu lt ip lescreenstobecomp lete lyd isp layed ,orrequ irescro l l ings ince

on lyparto fthed iagramisv is ib leatag ivent ime .Inadd it ion ,certa ininteract ion

issues ,suchashav ingtodragthescro l lbarstonav igate ,leadtonegat iveop in ion

aboutthe iruse fu lness[48 ]asitisd ifficu lttokeeptracko ftheconcernednodes

w iththescro l lbarwhenthetreeislarge . OntoV iz[75 ]addressth isspace- l im itat ion

prob lembyg iv inganopt iono finteract ive lyco l laps ingnodes .Theknow ledgemanager canse lectthenodeswh ichtheywou ldl iketoseeandthese lectedconceptisthen shown w ithitssub -h ierarch iesorre latednodes . Inth isd irect ion ,SpaceT ree[64 ] usesexpans ionandcontract ionopt ionsforitssub-h ierarch iestoavo idtheprob lem o fspacel im itat ion . Theeva luat ionssuggestthatSpaceT ree[64 ]per formsbetterin compar isonw ithothersinspec ificonto logy-re latedtaskssuchasfind ingapart icu lar node .

Someresearchershaveexp loredtheuseo funstructuredgraphrepresentat ionsfor theonto log ies[48 ,53 ] . Thelacko fstructureo ftheserepresentat ionsimp l iesthat theycanbeverycompactandprov idesadvantagesintermso fava i lab lespaceon thed isp lay . However ,theyarelesseffect iveforthepurposeso fonto logya l ignment duetothefactthattheh ierarch ica lstructureo ftheonto logyisd ifficu lttoperce ive andunderstand . Furthermore ,itcanberatherd ifficu lttocomparesubsetso fthe onto log ica lstructure . Becauseo ftheseprob lemsandthepreva lenceo ftree-based representat ions ,inth isworkthetree-basedrepresentat ionshavebeenadopted .

2 .3 .3 Representat ionof Mapp ings

Ina l ignmentsystemsthatusetree-basedrepresentat ions ,sourceandtargetonto logy

treesarenorma l lyshownonoppos ites ideso fthescreen wh i lethe m idd lereg ion

isusedtorepresent mapp ingsbetweentheent it ies . Inth isd irect ion ,thesource

andtargetonto logytreep lacedareontheoppos ites ideso fthescreeninPROMPT

[59 ,62 ] ,however ,themapp ingrepresentat ionisrep lacedbyal istcomponentinstead

o fstra ightorcurvedl ines ,wh ichd isp laysa l ltheproduced mapp ingsinal istandis

showninthe m idd le .

The mapp ingbetweentwoconceptsrepresentstheassoc iat ionbetweentwodata e lements .S incehumansinterprettwoth ingsasbe ingre latedi ftheyareconnected byl inesassuggestedbythe Gesta ltLawo fconnectedness[50 ] ,theeas iest method to makeusersin ferandtracetheconnectednessbetweenthetwo matchedonto logy conceptsisbyl ink ingthemv iaal ine . There latednessencodedus ingl inesisauto- mat ica l lyperce ivedandqu ick lyprocessedthroughthepre-attent ivecapab i l it ieso f thehumanbra in . Th isencod ingcanbeusedbysystemstorepresentassoc iat ion amongobjects( i .e . , mapp ingsand matchedconcepts) .

Wh i leear lyapproachesusedstra ightl inestoconnectassoc iatedent it iesbetween

theonto log ies ,therearev isua ld ifficu lt ieswhenfo l low ingsuchconnect ionsdueto

thesharpcornersthatarecreatedbyedgecross ings .Forexamp le ,AgreementMaker

[16 ]d isp laystheh ierarch ica lre lat ionsh ipbetweentheonto logyc lasses( i .e . ,theinput

onto log ies)us ingthetree-basedrepresentat ion(seeF igure2 .3) .Thesourceandtarget

onto logytreesarep lacedontheoppos ites ideso fthescreen ,wh i lestra ightl inesare

usedtorepresentthemapp ingsbetweenthemappedc lassnodes[37 ] . Onefeatureis

thattheamounto fs im i lar ityinpercentageamongthemappedconceptsisd isp layed

a longthe mapp ingl inesasanumber ,wh ichcanbenot icedeas i lybytheknow ledge

manager . However ,v isua l lyencod ingth isin format iona longthemapp ingl inecauses

av isua l mess . Anotherfeatureo fthesystemisthat mapp ingl inescanbefi ltered

accord ingtothes im i lar itythresho ldsetbytheknow ledgemanager . Morein format ion

aboutanind iv idua lconceptinthetwog ivenonto log iesis madeava i lab leseparate ly

inadeta i lw indowwhentheknow ledgemanagerc l icksonthenoderepresent ingthat

concept . Theknow ledge managercana lsoaddthe irown mapp ingsbyse lect ingany

twonodesandthentr iggerthere levantoperat ioninthe menu .

S incethehumaneyecan moreread i lyfo l lowcurvedl ines[82 ] ,someonto logy a l ignmentinter facesnowusecurvestorepresentthe mapp ings[1 ,31 ] . COMA++

[1 ,42 ]hasas im i largraph ica luserinter faceto AgreementMaker[16 ] ,show ingthe onto log iesontheoppos ites ideso fthescreen(seeF igure2 .4) . However , mapp ings l inesfoundbytheunder ly ingmapp inga lgor ithmaredrawnus ingcurvedl inesrather thanstra ight . Thefeaturesintermso fre levant mapp ingoperat ions( i .e . ,va l idat ion andre ject ion)area lsos im i lar ,suchasa l low ingknow ledge managerstodefinethe ir ownmapp ingsformatchesthatthemapp inga lgor ithmm issed . Aconfidencemeasure isassoc iatedw itheach mapp ing ,wh ichshowstheleve lo fconfidencethe mapp ing a lgor ithmhasforthecorrectnesso fthepart icu lar mapp ing .

Thestatuso fthe mapp ingcana lsobeconveyedbyv isua lparameterso fthe curve .Insomesystems ,co lourisusedtorepresentthed ifferencebetweencand idate mapp ingsandconfirmed mapp ings[1 ,31 ] . L inesty le(e .g . ,so l id ,dashed) maya lso beusedtoconveyth isin format ion[31 ] ,a l low ingtheknow ledge managertoread i ly ident i fyfeatureso find iv idua l mapp ings .

Anopenissueinth isareaiswhethertov isua l lyrepresent mapp ingswhene ither

endo fthe mapp ingisnotv is ib leintheonto logyrepresentat ion . Th iss ituat ion may

occurwhenapart icu larport iono foneonto logyisco l lapsed ,orwhenanent ityis

notv is ib leduetothescro l l ingo falargeonto logy .Somesystemscont inuetoshow

these mapp ings ,w iththeendpo int ingtoaco l lapsednodeorbe ingd irectedoffthe

bottomortopo fthed isp lay[31 ,67 ] . Another methodistodynam ica l lyfi lterthese

mapp ings ,add ingthembackinastheco l lapsednodeisexpandedorastheknow ledge

F igure2 .3 :Screenshoto ftheinter faceo ftheAgreementMakersystem[15 ,16 ]

F igure2 .4 :Screenshoto ftheinter faceo ftheCOMA++system[28 ]

2 .4 Onto logy Mapp ings Representat ionIssues

Improvementsfortherepresentat iono fonto logy mapp ingshasbeeng ivenl itt leat- tent ioninthel iteraturecomparedtothedeve lopmento fa l ignmenta lgor ithms(Sec- t ion2 .2 .3)andonto logyrepresentat ions(Sect ion2 .3 .2) . Know ledge managerso f onto logya l ignmentsystemsfindtwo majorshortcom ings :alacko fava i lab i l ityo f thebas icv isua l izat ionandnav igat iono f mapp ingsthatre latetwoe lementso fthe schemas ,andtheinter facebecom ingeas i lyd isorgan ized whentheonto log iesand numbero f mapp ingsaretoolarge[67 ] .

Oneo fthefundamenta lprob lemsw ithv isua l lyrepresent ingmapp ingsw ithl inesor curvesisthatedgecross ingsmayoccurwhentheordero ftheent it iesisnotcons istent betweentheonto log ies . A lthoughtheuseo fcurvestorepresentthemapp ingsmakes theseedgecross ingseas iertofo l low ,theprob lemisnotso lvedw iththeuseo fcurves . Thecand idate mapp ingsproducedbythea l ignmentsa lgor ithmsaregenera l lyvery largeinnumber . Assuch ,whenrepresent ingarea l ist icseto f mapp ings ,theedge cross ings maycauseas ign ificantdegreeo fv isua lc lutter .

C lutterisanimportantconceptinda i lyl ivesandiscons ideredw ithgreats ign i f-

icanceinthein format ionv isua l izat iondoma in[69 ] .Itisdefinedasastateinwh ich

surp lusitemsorthe irrepresentat ionorpresentat ionsty le , mayleadtoadegrada-

t iono fper formanceatsometask[69 ] .Forexamp le ,c luttercaninter ferew ithnorma l

taskssuchassearch ingforanimportantitem(e .g . ,aresearchpaperonad isorgan ized

deskthathaslotso fotherirre levantpaperslay ingaround) .Inthelanguageo fuser

inter facedes ign ,itre ferstoas ituat ionthathasthepotent ia ltocreateobstac lesin

per form ingas imp letasksuchasv isua lsearch ing . Whendes ign inguserinter faces ,it

isoneo fthebas icprob lemsfacedbysystemsthatpresentextens iveamounto fin for- mat ionandhavel im itedd isp layspace .Severa lattemptshavebeen madetoaddress th isphenomenonandprov ided ifferent mechan ismsto measurev isua lc lutter[69 ] . Manyin format ionv isua l izat iontechn iquesa lsoex istforaddress ingth isprob lem[25 ] .

Inthecontexto fonto logya l ignmentinter faces ,th isc lutterdueto mapp ingedge cross ingsreducestheusab i l ityo ftheinter face , mak ingitd ifficu ltfortheknow ledge managertov isua l lytraceamapp ingfromsourcetodest inat ion ,ortomakecompar- isonsbetweenmapp ings . Wh i leoneapproachm ightbetofindanopt ima lorder ingo f ent it iesintheonto logyrepresentat ions ,thereisnoguaranteethatsuchanorder ing ex istsnorthatitisanorder ingthatw i l l makesensetotheknow ledge manager .

Mosto ftheonto logya l ignmentsystems ,inc lud ingtheonesd iscussedinSec- t ions2 .2 .3and2 .3 .3 ,prov ideverybas icv isua l izat iontoo lsandl im itedinteract ion opt ionsfor man ipu lat ing mapp ingsandtoconducta l ignmenttasks . Astheknow l- edge managerhastoper formtheexhaust ingtasko fva l idat ingcand idate mapp ings producedbythesystem ,thereisaneedforabetterorgan izat iono fonto logy map- p ingscomb inedw ithinteract ionsupportforonto logya l ignmenttasks( i .e . , mapp ing va l idat ion ,andadd it ion) .

Inth isd irect ion ,B izTa lk Mapper[67 ]attemptstoemp loyadvancedv isua l izat ion

approachestolessenthec lutter ingprob lem ,as we l las manyv isua ltechn iquesto

supporttheknow ledge managerinper form ing mapp ingtasks .Itsupportsd ifferent

k indso fauto-scro l l ing .Forexamp le ,g iventhesourceandtargetonto logytreespre-

sentedontheoppos ites ideso fthescreen ,whentheknow ledge managerse lectsan

item ,there levantobjectsonthescreenbecomeprom inentthroughh igh l ightpropa-

gat ion .Inadd it ion ,theinter faceisauto-scro l ledtobr ingtheh igh l ighted mapp ings

andthese lectediteminthe m idd leo fthescreen . Th iswaya l lthere levantl inks tothese lecteditemareshownas muchasposs ib le . Anotherinteract ivefeatureis mu lt i-se lect ,wh icha l lowsthemanagertoseemu lt ip lee lementsandthere lat ionsthat ex istbetweenthem . The mapp ingl inksarea lsobendab lesothatthe managerscan makechangesintheformat iono fthe mapp ingl inesaccord ingtothe irneeds .

Evenw iththesefeatures ,whentherearealargenumbero f mapp ings ,itrema ins d ifficu lttoavo idc lutterintherepresentat ion .Thefundamenta lprob lemisthateach ind iv idua lmapp ingcont inuestoberepresentedbyastra ightorcurvedl inew ith inthe inter face .Th iscausess ign ificantedgecross ingsevenw ithasma l lnumbero fmapp ings edgesthatresu ltsintotheintroduct iono fv isua lc lutterontheinter face . Anexamp le o fth isprob lemcanbeseeninF igure2 .5 . Theproposedapproachisaddress ingth is fundamenta lissuethroughtheimprovemento fthev isua lrepresentat iono ftheedges .

F igure2 .5 : Asnapshoth igh l ight ingthec lutter ingprob leminB izTa lk Mappersys-

tem[67 ] .