Aligning Biomedical Ontologies

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Linköping Studies in Science and Technology

Dissertation No. 1110

Aligning Biomedical Ontologies

by

He Tan

Department of Computer and Information Science

Linköpings universitet

SE-581 83 Linköping, Sweden

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The amount of biomedi al information that is disseminated over the Web

in reases everyday. Thisri hresour eisusedto ndsolutionsto hallenges

a ross the life s ien es. The Semanti Web for life s ien es shows promise

for ee tivelyande iently lo ating,integrating, queryingandinferring

re-latedinformationthatisneededindailybiomedi alresear h. Oneofthekey

te hnologiesintheSemanti Web isontologies,whi hfurnish thesemanti s

of the Semanti Web. A large number of biomedi al ontologies have been

developed. Many of these ontologies ontain overlapping information, but

it isunlikely thateventually there will be one singlesetof standard

ontolo-gies to whi h everyone will onform. Therefore, appli ations often need to

dealwithmultipleoverlappingontologies,buttheheterogeneityofontologies

hampers interoperability between dierent ontologies. Aligning ontologies,

i.e. identifying relationships between dierent ontologies, aims to over ome

this problem.

Anumber ofontologyalignment systemshave been developed. Inthese

systems various te hniques and ideashave been proposedto fa ilitate

iden-ti ation of alignments between ontologies. However, there still is a range

of issues to be addressed when we have alignment problems at hand. The

work inthis thesis ontributes to three dierent aspe ts of identi ation of

high quality alignments: 1) Ontology alignment strategies and systems. We

surveyed the existing ontology alignment systems, and proposed a general

ontology alignment framework. Most existing systems an be seen as

in-stantiations of the framework. Also, we developed a system for aligning

biomedi al ontologies (SAMBO) a ording to this framework. We

imple-mented various alignment strategies in the system. 2) Evaluation of

on-tology alignment strategies. We developed and implemented the KitAMO

frameworkfor omparative evaluationof dierent alignment strategies, and

we evaluated dierent alignment strategies using the implementation. 3)

Re ommending optimal alignment strategies for dierent appli ations. We

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I would like to express my deepest gratitude to my supervisor Professor

Patri k Lambrix, who has introdu ed me to resear h. Thank you for your

invaluable guidan e, dis ussions and omments during the work. I

appre- iate your en ouragement and support. Your steady, reliable and attentive

presen e provides su h a omfortable work environment. I would like to

express my appre iation to Professor Nahid Shahmehrifor providing useful

omments andpointing out important aspe tsof theresear h world.

I am thankful to my olleagues at the Laboratory for Intelligent

Infor-mationSystems(IISLAB)andattheDivisionforDatabaseandInformation

Te hniques(ADIT).

Thanks to my friends. I appre iate the times we share hallenges and

elebratejoysoflife. Lastbutnotleast,Ifeelgratefultomyparents. Thank

you foryour understanding, supportand endlesslove. Thewarmth yougive

is the thingI herish most inthe world.

We a knowledge the nan ial support of the Swedish Resear h

Coun- il (Vetenskapsrådet), the Swedish National Graduate S hool in Computer

S ien e (CUGS), and of the EU Network of Ex ellen e REWERSE (Sixth

Framework Programmeproje t506779).

Tan,He

Linköping, Sweden

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Thisthesis ontains revisedversionsof thefollowing papers:

1. P. Lambrix, H. Tan, SAMBO - A System for Aligning and Merging

Biomedi al Ontologies, Journal of Web Semanti s, Spe ial issue on

semanti web for thelifes ien es, 2006,4(3):196-206.

2. H.Tan,V.Jakoniene,P.Lambrix,J.Aberg,N.Shahmehri,Alignment

of biomedi al ontologies using life s ien e literature, Pro eedings of

the International Workshop on Knowledge Dis overy in Life S ien e

Literature,LNBI3886, Singapore, 2006,1-17.

3. B. Chen, H. Tan, P. Lambrix, Stru ture-based ltering for ontology

alignment, Pro eedings of the IEEE WETICEWorkshop on Semanti

Te hnologies in Collaborative Appli ations, 2006, 364-369.

4. P.Lambrix,H.Tan,AToolforEvaluatingOntologyAlignment

Strate-gies,Journal on Data Semanti s,LNCS 4380,2007, VIII:182-202.

5. H.Tan,P.Lambrix,AMethodforRe ommendingOntologyAlignment

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Related Papers

The followingare relatedresear h arti lesnot in luded inthethesis.

1. P.Lambrix, A. Edberg, C. Manis, H.Tan, Merging DAML+OIL

bio-ontologies, Pro eedings of the International Workshop on Des ription

Logi s, Rome,Italy,2003.

2. P. Lambrix, H. Tan, Merging DAML+OIL Ontologies, J. Barzdins,

A. Caplinskas, (eds.), Databases and Information Systems (Sele ted

Papers from the Sixth International Balti Conferen e DB&IS'2004),

IOSPress, 2005,249-258.

3. P.Lambrix,H.Tan,AFrameworkforAligningOntologies,Pro eedings

of the Third Workshop on Prin iples and Pra ti e of Semanti Web

Reasoning,LNCS3703, Dagstuhl, Germany, 2005,17-31.

4. P.Lambrix, H.Tan,Ontology alignment and merging, C. Burger, D.

Davidson, R. Baldo k, (eds.),Anatomy Ontologies forBioinformati s:

Prin iples and Pra ti e, Springer,2007.

5. T. Wä hter, H.Tan,A.Wobst, P.Lambrix, M. S hroeder, A

Corpus-driven Approa h for Design, Evolution and Alignment of Ontologies,

Pro eedingsoftheWinterSimulationConferen e,2006,1595-1602.

In-vited ontribution.

Other published arti les

1. P. Lambrix, H. Tan, V. Jakoniene, L. Strömbä k, Biologi al

Ontolo-gies, C. Baker, K.H. Cheung, (eds.), Semanti Web: Revolutionizing

Knowledge Dis overy in the LifeS ien es,Springer, 2007,85-99.

2. L.Strömbä k,V.Jakoniene,H.Tan,P.Lambrix,Representing,storing

anda essingmole ularintera tiondata: areviewofmodelsandtools,

Briengs in Bioinformati s,2006,7(4):331-338.

EU Network of Ex ellen e REWERSE deliverables

1. R.Ba kofen,A.Burger,A.Bus h,G.Dawelbait,F.Fages,V.Jakoniene,

P.Lambrix, K. M Leod,S.Soliman, H.Tan, S.Will,Implementation

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Dawelbait, A.Doms,F.Fages, A.Hotaran,V.Jakoniene,L.Krippahl,

P.Lambrix,K.M Leod,W.Nutt,B.Olsson,M.S hroeder,A.S hroi,

S.Soliman,H.Tan,D.Tilivea,S.Will,Requirementsandspe i ation

of use ases,REWERSE Deliverable A2-D3, 2005.

3. R.Ba kofen, M. BadeaM, P.Barahona, A.Burger, G. Dawelbait, A.

Doms,F.Fages,A.Hotaran,V.Jakoniene,L.Krippahl,P.Lambrix,K.

M Leod,S.Möller, W.Nutt, B. Olsson,M.S hroeder, S.Soliman, H.

Tan,D.Tilivea,S.Will,Usageofbioinformati stoolsandidenti ation

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Introdu tion ...1 Motivation ...3 Ba kground ...5 ProblemStatement ...7 Contributions ...9 Resear h Methods ...11 Paper Summaries ...11 RelatedWork ...13 Con lusion ...16 Referen es ...19

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1 Motivation

In re ent de ades,information inthebiomedi al area hasbeen growing

ex-plosively: the te hnologi al revolution has brought vast quantities of data

ongenomes,enzymes,pathwaysanddiseases. Forexample,thesequen esof

genomes for humans have been ompleted [CMP03,FJTO03℄. In the

post-genomi era,thisinformationisusedtondsolutionsto hallengesa rossthe

life s ien es,forinstan e, dete ting systemi fun tionalbehaviors ofthe ell

andtheorganism[CGGG03 ℄. Biomedi alinformation isdisseminatedovera

hugenumberofheterogeneous andautonomoussour esthatareoften

avail-able ontheInternet. For instan e, 968 datasets freelyavailable on theWeb

arelistedinthe2007databaseissueofNu lei A idsResear h[Gal07 ℄.

Ee -tivelyande ientlylo ating,integrating,queryingandinferringrelateddata

andknowledgearedi ultiesthatresear hersexperien edailyinbiomedi al

resear h [Lam05℄. The Semanti Web for life s ien es [HCLS, REW℄ shows

great promise toalleviate these di ulties (e.g.[SBL06,BC07℄).

TheSemanti Webisanextension ofthe urrentWebwhere information

is given well-dened meaning, better enabling ma hines to automati ally

pro ess information [BHL01 ℄. One of the key te hnologies in the

Seman-ti Web is ontologies, whi h will furnish the semanti s for the Semanti

Web [SBH06℄. Ontologies (e.g. [Gom99 ℄) an be seen as dening the

ba-si on epts and relations of a domain of interest, as well as the rules for

ombining these on epts and relations. They leadto abetter

understand-ing of a eld and to more ee tive and e ient handling of information in

that eld. The benets of the use of ontologies in lude reuse and sharing

of knowledge a ross platforms, and improved do umentation, maintenan e,

reliability and interoperability (e.g. [JU99℄). A re ent example is that the

RoyalSo ietyofChemistry(RSC) Publishing hasde ided toannotate their

publisheds ienti literaturewiththeGeneOntology(GO)[GO ℄andthe

Se-quen e Ontology (SO) [SO℄terms, enabling more qui kandee tivesear h

and automateddis overy of relevant resear h [GORSC℄.

Alargenumberofbiomedi alontologies have been developed,andmu h

eort hasbeen madeto advan etheir use to fa ilitate handling vast

quan-tities of information inbiomedi al resear h [BS06℄. For example, the Gene

Ontology onsortium [GO℄,OpenBiomedi alOntologies(OBO)[OBO ℄,and

SNOMEDinternational [SNOMED ℄,areinternationalresear h ooperations

for development and use of biomedi al ontologies. The National Center for

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Institutes ofHealth (NIH),whose goal isto supportbiomedi al resear hers

using ontologies in theirwork.

Ontologies are developed in dierent ommunities based on their own

needs. It isinevitable that manybiomedi al ontologies ontainoverlapping

information,butusuallythereisnostraightforward waytomapthe

overlap-pingregions. Forinstan e,inthedomainofanatomy,anumberofontologies

have been developed, e.g. Foundational Modelof Anatomy (FMA) [FMA℄,

theAnatomi alDi tionary fortheAdultMouse(MA)[HMC+05℄,anatomi

ategories of theMedi al Subje t Headings (MeSH) and the U.S. National

Can er Institute (NCI) thesaurus [NCI℄. All these anatomi al ontologies

haveagreatdealin ommon,butarerepresentedindierentlanguages,and

imposedierentsyntax,stru tures,semanti s,s opesandperspe tives.

Fur-thermore,itisunlikelythateventuallytherewillbeonesinglesetofstandard

ontologies to whi h everyone will onform [NRM04℄, although mu h eort

has been devotedto developing standard ontologies inthebiomedi al area,

e.g. theCommon Anatomy Referen e Ontology (CARO) [HN07℄being

de-velopedunderNCBO.Therefore,appli ationsmustoftendealwithmultiple

ontologies. Appli ationsoftenneed ontologies fromdierent areas,orwhi h

express dierent views on one area, for example, for querying data over a

rangeofsour esannotatedwithtermsfromdierentontologies. Appli ation

developersoftenneedtodevelop ustom,task-spe i andsmallerontologies

and linkthem to thestandard ontologies. Ontology buildersmay use

exist-ingontologiesasthebasisforthe reationofnewontologiesbyextendingthe

existingontologiesorby ombining knowledgefromdierentsmaller

ontolo-gies. Inall ofthese ases,interoperabilityof multiple ontologies is hindered

by their heterogeneity, whi h has be ome a riti al issue for realizing the

vision of the Semanti Web [Noy05, BLS07 ℄.

Aligning ontologies, i.e. identifying relationships between dierent

on-tologies, aims to over omethe problem. Currently, thousands of ontologies

havebeen developed,and billionsof sour esontheWebareannotated with

ontologyterms[SWG ℄. Highqualityontologyalignmentisa ornerstone for

seamless interoperability between sour esusing dierent ontologies. It is a

ne essarypre ondition for ee tively lo ating, integrating, querying and

in-ferringinformationspreadoverheterogeneoussour es. Ourwork ontributes

to solutions for e iently identifying highqualityalignments. In thisthesis

wefo uson threeissues: developing toolsfor aligningontologies,evaluating

ontologyalignment strategies, andre ommendingoptimal alignment

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to the eldof the life s ien es whi h isseen to be ahuge area for potential

appli ation developmentoftheSemanti Web,theresults anbegeneralized

and areappli able to dierent areas.

2 Ba kground

2.1 Ontologies

Ontology asthe study of existen edates ba kto an ient Greek philosophy.

Ontology studies what kinds of things exist, how these things an be

in-terrelated and what they mean in reality. The term hasbeen used in very

dierent ways within information s ien e [GG95 ℄. In this thesis, we follow

anearlydenitionprovidedintheeldoftheArti ialIntelligen e[NFF91℄:

Anontologydenesthebasi on epts andrelations ofadomainofinterest,

aswellastherulesfor ombiningthese on eptsandrelations. Thedenition

givesaroughimage ofhowanontologyisbuilt. A ordingtothedenition,

an ontology in ludesnot onlythetermsthatareexpli itly dened,but also

terms that an be inferredusing rules.

Thebenetsof theuseof ontologies in lude reuseand sharing of

knowl-edgea rossplatforms,andimproveddo umentation,maintenan e,reliability

andinteroperability(e.g.[JU99 ℄). Ontologiesareusedfor ommuni ation

be-tween people and organizations by providinga ommon terminology overa

domain. They provide thebasisfor interoperability between systems. They

an beusedformakingthe ontentininformationsour esexpli itandserve

as an index to a repository of information. Additionally, they an be used

as a basis for integration of information sour es and as a query model for

information sour es. They also support learly separating domain

knowl-edgefromappli ation-based knowledgeaswellasvalidationofdatasour es.

Overall, ontologies lead to a better understanding of a eld and to more

ee tive and e ient handling ofinformation inthat eld.

Ontologies anbe lassiedintodierenttypesdependingonwhi hofthe

omponents arerepresented andthe kindof information they an represent

(e.g [SGB01 , LTJS07℄). Controlled vo abularies are a simple type of

ontol-ogy. There are essentially lists of on epts that are enumerated expli itly.

When the on epts are organized into a hierar hi al stru ture, we obtain a

taxonomy. The relations in a taxonomy an be is-a and part-of. Thesauri

are more omplex ontologies, inwhi h there arealso relations that onne t

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ontolo-gies areofthesethreetypesofontologies. Othertypesofontologies aredata

models and knowledge bases. In data models axioms whi h des ribe fa ts

that are always true intopi area of the ontology areused inaddition to

on epts and relations. Knowledge bases areoften basedon a logi . In this

thesis we fo uson ontrolled vo abularies, taxonomies andthesauri.

Ontology languages allow users to represent an ontology. The main

re-quirements of ontology languages are [AH03℄: 1, a well-dened syntax, 2,

well-dened semanti s, whi h is ane essary ondition for pre isely

des rib-ing the meaning of knowledge, 3, e ient reasoning support, whi h an be

used to, e.g. he k the onsisten y of the ontology, dete t unsatisable of

on epts and remove redundan y. 4, su ient expressive power, whi h

de-termines what kinds of knowledge an be represented in the ontology, and

5, onvenien e ofexpression. There areanumberoflanguages, e.g. RDF(s)

[RDF℄, F-Logi [KLW95℄ and OWL [OWL℄. Few of the existing ontology

languages fulllallof theserequirements.

2.2 Heterogeneity of ontologies

The heterogeneity of ontologies may o ur at dierent levels (e.g. [Kle01 ,

BEF04 ℄). At language level, ontologies are represented in dierent

lan-guages, su h as RDF(s) and OWL. At terminology level, dierent namings

of terms bring out the mismat hing between ontologies, for example,

syn-onyms, homonyms and dierent en odings. At on ept level, we en ounter

the heterogeneity of ontologies due to dierent s ope, granularity and

per-spe tive. At pragmati s level, knowledge represented in ontologies may be

indierent ontext.

The fo us of our work is to over ome the heterogeneity of ontologies

on the terminology and on ept levels. Aligning, mapping and arti ulating

ontologies aretheterms oftenusedto refer tothe task, and dierent

mean-ings of the terms aregiven inthe literature. In this thesis we usealigning

ontologies. It is dened as identifying the relationships between on epts

or relations from two dierent sour e ontologies. The relationships an be

equivalen e aswellasis-a,part-ofor anyotherkindofrelation. This

deni-tiongivesapi tureofhowtospe ify orresponden ebetweentwoontologies.

Thisdenition overs the ommon meaning ofmappingontologies. Aligning

ontologies usually is to bring together a set of ontologies, and arti ulating

ontologies is sometimes dened as aligning parts of ontologies. We also

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whi hisa(minimal)unionoftwoexistingontologiesbasedontheiridentied

alignments.

Translating ontology is the ommon term referring to ta kling

het-erogeneityonthe language level, whi h isdened as onverting an ontology

representedin one ontology language to another language while preserving

the semanti s. Some tools have been developed for fa ilitating this

pro- ess, e.g. Jena [Jena℄ is a widely used tool whi h supports ontologies in

RDFs, DAML+OIL [DAML℄ and OWL. In the biomedi al area OWL has

been widely a epted, e.g. in OBO and NCBO. We do not fo us on this

issue,andassumethatontologiestobealignedareinOWL.Thepragmati s

heterogeneityis relatedtotheuseof ontologies. Thereis notmu h workon

this issue.

2.3 Aligning Ontologies

Anumberofsystemsforaligningontologieshavebeendeveloped,andvarious

methods for dis overing alignments areproposed inthese systems. Mostof

the existingsystemsaresemi-automati ,i.e. auserisinvolvedinidentifying

alignments. Todete talignments, insu hasystemthere areusually

mat h-erswhi h omputesimilarityvaluesbetween termsfromdierent ontologies,

and algorithmswhi hlter out alignment suggestionsbasedon resultsfrom

mat hers. If there are several mat hers, algorithms may be available to

ombine their results. So, alignment strategies are omposed of dierent

mat hers, ombination and ltering algorithms. Further, in su h a

system there areintera tive omponents where theuser de ides alignments

based onsuggestions.

3 Problem Statement

Aligning ontologies be omesa riti alissuewhen ollaborating appli ations

use dierent ontologies. A lot of resear h is urrently dealing with this

hallenge. A number of ontology alignment systems have been developed.

Inthesesystemsvariouste hniquesandideashavebeenproposedtofa ilitate

identifyingalignmentsbetween ontologies. However, thereis stilla rangeof

issues to be addressedwhen we have alignment problemsat hand.

No alignment system is suitable for all kinds of alignment problems.

Developers may need to build an alignment system from s rat h for their

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inparti ular for ontologies fromthe eldof life s ien es. However, itis not

lear how su h systems should be developed and what would be a suitable

ar hite ture for su h a system. Further, in the eld of life s ien es domain

knowledgeisavailable intheform ofbiomedi aldomain lexi ons,databases

and olle tionsof arti les. Thisknowledge ouldbeutilized for ndinghigh

qualityalignments, but not mu h work has been done on this. So, therst

problem to be ta kled isinthis thesisis:

Problem 1: There areveryfewguidelines onbuildingalignment

sys-tems.

Although most of alignment systems and strategies are developed for

their own need, manyof them an be extendedfor otherappli ations. The

evaluationand omparisonofdierentsystemsandtheiralignmentstrategies

wouldgiveustheirstrengthsandweaknesses,andvaluableinsight into their

properties. It would leadto improvement ofthe existing alignment systems

andstrategies,butalsoprovidevaluableknowledgewhentheyarereusedfor

other alignment problems. Thus the se ondproblemis that,

Problem 2: Up to now there are few omparative evaluations, and

also thereare veryfew toolsto supportevaluations.

In most appli ations where alignments between multiple ontologies are

required, domain experts want to reuse the existing alignment strategies.

It is unlikely that there is one single strategy that outperforms the others

for all dierent kinds of ontologies, therefore domain experts must hoose

appropriatestrategiesfromdierentavailablestrategies. Itisaverydi ult

task.

Problem 3: Currently, very little knowledge is available about

ex-isting alignment methods,their appli ability,and theontologies to be

aligned, andyetno toolexiststhatfa ilitates thetask,e.g. by

re om-mending appropriate alignment strategies.

Asdis ussedinse tion2,our urrentworkwillfo usonontologieswhi h

are ontrolled vo abularies, taxonomies and thesauri. We assume that

on-tologies to be aligned are in OWL, and we do not onsider the ontext of

ontologies in appli ation. Further, we fo us on semi-automati alignment

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4 Contributions

Thisthesis ontributesto aligning biomedi alontologies asfollows.

4.1 Ontology alignment systems and strategies

- Asarststepindealingwithproblem1,weadaptedthemethodforthe

lassi ation ofautomati s hemamat hingapproa hesin[RB01 ℄and

used itfor lassi ation of ontology alignment approa hes. We

lassi-ed ontology alignment approa hesbased on theknowledge they use.

The lassi ationaugmentsourunderstandingofalignment strategies.

Anoverviewoftheexistingontologyalignmentsystemsandthe

strate-gies they useisgiven inpaper1.

- As the se ond step in oping with problem 1, we proposed, based on

ontribution1,ageneralframeworkforaligningontologies. The

frame-workprovidesabasisforbuildingalignmentsystems,andprovides

sup-portfor experimenting withdierent alignment omponentsand their

ombinations. Part of the framework is similar to some steps in the

alignment pro ess introdu ed in [ES04b ℄, but our framework fo uses

onanar hite tureforalignmentsystems. Mostoftheexistingsystems

an be seen as an instantiations of our framework. This ontribution

is reportedinpaper1.

- Wedeveloped SAMBO (System for Aligningand Merging Biomedi al

Ontologies) a ording to the general framework. Currently, it is the

only alignment tool targeted towards biomedi alontologies. We

stud-iedthe hara teristi softheexistingbiomedi alontologies andsought

availablesour es inthe area, whi h an be utilized for alignment. We

implemented several alignment algorithms, ltering algorithms and a

weightedsum ombination algorithm. Wedeveloped andimplemented

strategiesbasedonthetextualdes riptionsoftheterms, is-aand

part-ofhierar hies ofthe ontologies,retrievedinstan es anduseofthesauri.

In paper 2 we report on algorithms that utilize life s ien e literature

to ompute the similarity between terms. In paper 3 we propose an

approa h that applies information about the stru ture of ontologies

inthe ltering phase. All other implemented alignment strategies are

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4.2 Evaluation of alignment systems and strategies

- As the rst step in dealing with problem 2, we evaluated the

dier-entalignmentstrategiesaswellastheir ombinationswithinSAMBO.

Wealso ompared SAMBOwithotherontologyalignmentsystems. In

the evaluation we fo used on the quality of the suggestions whi h is

measured using pre ision and re all. We use pre ision as itis usually

dened ininformation retrieval, i.e. thenumberof orre tsuggestions

divided by the number of suggestions. Similarly, re all is dened as

the number of orre tsuggestions dividedby thetotalnumber of

or-re t suggestions, in this ase the expe ted suggestions. Our work is

one of the two existing evaluations whi h fo us on the quality of the

suggestions. In addition, we studied the evaluation results to dete t

strengthsandweaknessesofdierentstrategiesandtheir ombinations.

The resultsof the evaluations aredis ussed inpaper 1.

- Currently, there is no tool that provides an integrated environment

for omparative evaluations on alignment strategies and their

om-binations. The paper 4 ontributes to this issue. Based on our

gen-eralalignmentframeworkandexperimentswithSAMBO,weproposed

the KitAMO framework whi h is an integrated system to

ompara-tively evaluate non-intera tive alignment omponents and their

om-binations. We implemented a prototype a ording to the framework.

With the implementation we experimented with omparative

evalua-tions ofdierent alignment strategiesandtheir ombinations interms

of the performan e and the quality of the alignment suggestions. We

alsostudied howtheevaluationresults anbeusedtodete tstrengths

and weaknessesof dierent strategies andtheir ombinations.

4.3 Re ommendation on alignment strategies

- Up to now two methods have been proposed to deal with problem

3. They require prior knowledge about the alignment problem and

existing alignment strategies. However, su h knowledgeis usually not

available whenmaking re ommendations. In our work we proposeda

methodinwhi h the prior knowledgeis not ne essary,and whi h also

minimizestheeortfromusers. Wedis ussedthemethodinthesetting

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5 Resear h Methods

Survey Inthepro ess ofunderstanding theeldof ontology alignment, we

surveyedthes ienti publi ationsonontologyalignmentandarelatedarea,

s hemamat hing. We experimentedwithpubli ly available alignment tools

using a set of biomedi al ontologies. We studied their fun tionalities and

quality of alignment. The survey results were usedas a basis for the

las-si ation ofalignment te hniques and systems,and for a general alignment

framework.

Prototyping Prototyping leads to a better and deeper understanding of

resear hproblemsandtheirproposedsolutions. Wedevelopedouralignment

systembasedonthegeneralframework,andimplementeddierentalignment

strategiesinthesystem. Weperformedanumberofevaluationsand

ompar-isons on alignment strategies and systems. The experiments demonstrated

the appli abilityof thegeneral framework,and gavea better understanding

of the alignment framework. In addition, it formed a basisfor thework on

problem 2and 3.

When dealing with problem 2,we developed a prototype for evaluating

alignment strategies. We experimented withour implemented strategies to

demonstrate and evaluate the appli ability of the prototype. The

exper-iments also indi ated that the prototype ould be applied to support the

re ommendation method we proposed for problem3. We implemented

sev-eral algorithmsinthe re ommendation method. We tested theappli ability

of the method and evaluated thequality of there ommendations when

dif-ferent algorithms were used. Thisexperiment ould, inits turn, be seen as

an appli ation study oftheevaluation prototype.

6 Paper Summaries

In this se tion we give short summaries of the ve papers en losed in this

thesis. In paper 1, 2, and 3 we investigate some fundamental problems of

aligning ontologies. Paper 4 deals with the omparative evaluation of the

dierent alignment strategies. In paper 5 we approa h making

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Paper1: SAMBO-ASystemforAligningandMergingBiomedi al

Ontologies

Inthis paperwe present ageneral framework for aligningontologies. In

the framework the alignments between two sour e ontologies an be

deter-mined underthe supervisionofthe userbasedon thesuggestions generated

bydierent alignment strategiesas well astheir ombinations. Further, we

identifythe typesofalignment strategiesusedintheexistingalignment

sys-tems. We des ribeSAMBO, analignment systemwhi h exemplies theuse

of theframework. InSAMBO we implement several mat hers thatare

rep-resentatives of the dierent types of alignment strategies, a ltering and a

ombinationalgorithm. Wedis usstheevaluationsinwhi hwe ompare the

qualityof suggestions generated bythe dierent mat hers and their

ombi-nations with the ltering algorithm, and the amount of time they take to

generate the suggestions. We also ompare SAMBO with two other

align-ment systems with respe t to the quality of suggestions. We use several

well-known biomedi alontologies intheevaluations.

Paper 2: Alignment of biomedi al ontologies usinglife s ien e

lit-erature

Inthispaperwepresentourexperimentsonutilizinglifes ien eliterature

relatedto on eptsfromthesour eontologies. Weproposeabasi algorithm

as well asextensions that take the stru ture ofthe ontologies into a ount.

The algorithms build on the intuition that a similarity measure between

terms anbe omputed basedon the probabilitythatdo umentsabout one

term are also about the other term and vi e versa. The related literature

is retrieved from PubMed [PubMed ℄, a free digital ar hive of biomedi al

and life s ien es journal literature. We ompare the basi mat herwith its

extensions, and withother alignment strategiesimplemented inSAMBO in

terms of the quality of suggestions and the amount of time they take to

generate the suggestions. We also present the results of evaluations on the

dierent ombinationsof these mat hers withotherimplementedalignment

strategies.

Paper 3: Stru ture-based ltering for ontology alignment

Inthispaperweproposeamethodwhereweusethestru tural

informa-tion inthe ltering stage inthe alignment pro ess. The approa h is based

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the re all in reases when thethresholds de reases in thethreshold ltering

algorithm. To keep the pre ision and in lude more orre tsuggestions, the

approa h augments thethreshold lteringalgorithm withthestru tural

in-formation. We applythealgorithm tolter outthesuggestionsfromseveral

mat hers implemented in SAMBO. We ompare the quality of the

sugges-tions lteredoutwithandwithoutthestru tureinformation,and also

om-pare theamount oftimefor omputation of thesuggestionsand ltering.

Paper 4: A Tool for Evaluating Ontology Alignment Strategies

Inpaper4weproposetheKitAMOframeworkfor omparativeevaluation

ofdierentontologyalignmentstrategiesandtheir ombinations. Wepresent

our urrentimplementation oftheframework. Withtheimplementation, we

illustrate how the system an be used to evaluate and ompare alignment

strategies and their ombinations in terms of performan e and quality of

the proposed alignments, and give suggestions on how the results an be

analyzed to obtaindeeperinsights into theproperties of thestrategies.

Paper5: AMethodforRe ommendingOntologyAlignment

Strate-gies

In this paper we propose a method that provides re ommendations on

alignment strategiesfor a given alignment problem. The method makes the

re ommendationsbasedontheevaluationofthedierentavailablealignment

strategies on severalsmall sele ted pie esfrom theontologies. In thepaper

we des ribe the basi steps of the method, and then illustrate and dis uss

the method in the setting of an alignment problem with two well-known

biomedi al ontologies.

7 Related Work

7.1 Ontology alignment systems and strategies

There area numberof systems devotedto aligning ontologies,su has

Chi-maera [MFRW00℄,FCA-Merge[SM01 ℄,PROMPT [NM03℄,IF-Map[YM03℄,

GLUE [DMDH03℄, QOM [ES04a℄, COMA++ [ADMR05℄, SST[ZK06℄.

Dif-ferent alignment strategies are implemented in these systems. In paper 1

we lassify the various alignment approa hes based on the knowledge they

use into strategies basedon linguisti mat hing, stru ture-based strategies,

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aux-iliary information, and their ombinations. We give a brief overview of 13

systems and dis uss their alignment strategies a ording to this

lassi a-tion. In [EBB04 ℄ alignment approa hes are lassied into lo al methods

and global methods. Lo al methods are the mat hers based on internal

knowledge, whi h are ategorized into, terminologi al methods, stru tural

methods, extensional (based on instan es) methods, and semanti methods

(basedonmodels). Globalmethodsin ludemat hers omposedofseveral

lo- almethods, ombinationalgorithms, methods involvingtheontologiesasa

whole,learningmethods,anduserinput. 21systemsaredis usseda ording

to the lassi ations.

Generalalignmentframeworksareproposedinworksbytwoothergroups.

[BEF04 ℄ denes the alignment pro ess as a fun tion and dis usses various

onstraintsthat anbeappliedoninput, parametersandoutputofthe

fun -tion. However,howto ompute thefun tionisnot given.[ES04a ℄introdu es

an alignment pro ess withsix main steps: feature engineering, sele tion of

next sear h steps, similarity omputation, similarity aggregation,

interpre-tation and iteration. Feature engineering isto transform therepresentation

of ontologies into a format other steps an use. Sele tion of next sear h

steps restri tsasear hspa eof andidatealignmentsto redu etherun-time

omplexitybyredu ingthenumberof andidatesalignments. Mostexisting

alignmentsystemsusually he kall andidatealignments. Further, [ES04b ℄

gives 17 rules for the omputation of similarity. In paper 1 we propose our

general alignment framework. Part ofthe framework issimilar tothesteps,

similarity omputation, similarity aggregation, interpretation and iteration

in the pro ess des ribed in [ES04a ℄. The main goal of our framework is to

givea basisforbuildingalignment systems,andprovides supportfor

exper-imenting withdierent alignment omponentsand their ombinations.

Arepositoryofinformation onontologyalignmentsystems anbefound

on the ontologymat hing website (http://www.ontologymat hin g.org /). It

is updatedfrequently.

7.2 Evaluation of alignment systems and strategies

Currently, omparativeevaluationsofontologyalignmentsystemshavebeen

performedbyafewgroups. TheEUOntoWebproje t[OntoWeb℄evaluated

thesystemsPROMPTbasedonProtégé(withextensionAn hor-PROMPT),

Chimaera (des ribed, not evaluated), FCA-Merge and ODEMerge. This

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visu-alization, but did not in lude tests on the qualityof thealignment

sugges-tions. Ourwork in ludes PROMPT, Chimaera,FOAM and SAMBO inthe

evaluations. The evaluations fo us on the quality of thealignment

sugges-tions as well as the time it takes to generate suggestions with these tools.

In addition, the interfa e of the systems are dis ussed in [LE03℄, and in

paper1 thedierent alignment algorithms andtheir ombinations with

dif-ferent ombination weights were evaluated with dierent ltering threshold

values. Sin e 2004, the Ontology Alignment Evaluation Initiative (OAEI,

http://oaei.ontologymat hing.org /) has organized yearly international

eval-uation ampaigns. The2006 ampaign has4tra ksgathering6data sets: a

omparison tra k, an expressive ontologies tra k, a dire tories and thesauri

tra k,anda onsensusworkshop. Ea h tra khasadierent evaluation

pur-pose. 10systemsparti ipatedinthe ampaignand 3ofthesystemsusedall

the datasets. The ampaignfo usedonthequalityofthealignment

sugges-tions. The results are reported at the international workshop on ontology

mat hing(OM-2006).

Further, tools for evaluations are developed in the latter two proje ts.

The Alignment API [Euz06℄ is the tool developed in the OAEI, whi h the

parti ipantsmay useinthe ampaign. The Alignment API in ludesseveral

evaluators whi h an ompute the pre ision, re all, fallout and f-measure

of an alignment result and a weighted symmetri dieren e between two

alignments. OLA [ELTV04℄ is a GUI appli ation implemented on top of

this API. We propose the KitAMO framework (paper 4) whi h provides

an integrated system for omparative evaluation and analysis of alignment

strategies andtheir ombinations.

7.3 Re ommendation on alignment strategies

Currently, very little resear h ta kles the problem of sele ting alignment

strategies that are optimal for given alignment problems. In [MJE06 ℄ it

is argued that nding appropriate alignment strategies should be based on

knowledge about the strategies and their previous use. As a rst step a

number offa tors (relatedto input, output, approa h,usage, ost and

do -umentation) were identied that are relevant when sele ting an alignment

strategy. The relevant data is olle ted by questionnaires. This method

requiresmu heortfrommanydierentusers. TheAnalyti Hierar hy

Pro- ess isusedto dete t suitablealignment approa hes. In[ESS05℄,APFEL, a

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APFEL a set of feature parameters are de lared for the sour e ontologies,

thesimilarityassessment,andthe dierentmat hers, ombinationandlter

algorithms. To generate training data, an existing parametrization is used

and alignment suggestions aregenerated. Thesesuggestions need tobe

val-idated by the user. A ma hine learning approa h is then used to learn an

optimal parametrization. The main di ulty in this approa h is hoosing

thetrainingdata. Thevalidationofdata alsorequireseortfromusers that

have good knowledge about thearea of the ontologies. Inpaper 5 we have

proposedandtestedamethodthatisbasedontheassumptionthatstrategies

thatworkwellforaligningsele tedsmallsegmentsoftheontologieswillalso

work well for aligning the whole ontologies. The re ommendation is made

basedon twowell-known measurements ofqualityof alignment suggestions.

8 Con lusion

8.1 Summary

Aligning ontologies aims to over ome the heterogeneity of ontologies. High

quality ontology alignment is riti al for seamless interoperability between

sour es using dierent ontologies. The goal of our work isto nd solutions

for e iently identifying high quality alignments between ontologies. This

thesis ontributes tothreeproblems inthe eld: 1,development ofontology

alignment strategiesand systems,2,evaluationof alignment strategies, and

3,re ommendingsuitablealignmentstrategiesforgivenalignmentproblems.

Mu h resear h has joined the eld of aligning ontologies. On the

pub-li ation list of the ontology mat hing website, there are 14 onferen e and

journal papers in 2003, and in2006 there are 48 papers. A number of v

ar-ious alignment systems and methods have been developed. In this thesis

we lassiedontologyalignmentstrategiesbasedontheknowledgetheyuse.

The lassi ationleadstoaneasywaytounderstand theeld. Weproposed

a general framework for aligning ontologies. The framework an provide a

basis for building alignment systems, and support for experimenting with

dierent alignment strategies. Most of theexisting systems an be seen as

an instantiations of our framework. Based on the framework we developed

SAMBO, a system for aligning biomedi al ontologies. In the system we

developed dierent alignment strategies targeted at biomedi alontologies.

To improve the work in the eld, omparative evaluation of dierent

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eval-uated dierent strategies implemented inSAMBO, and ompared SAMBO

with other systems. The evaluation experiments led to KitAMO, a

proto-type for omparative evaluations of alignment strategies. We implemented

the prototype,andevaluatedits appli ability. However,therearestill many

open problems in this issue, su h asevaluation riteria and onstru tion of

test ases.

Theout omeof omparativeevaluation anprovidevaluableinputto

an-other hallenge, re ommendingsuitablealignment strategiesforgiven

align-ment problems. Currently, there is very little resear h on the problem. In

this thesis we proposed a method for re ommendation of alignment

strate-gies. We implemented dierent algorithms for the dierent steps in the

method, demonstrated the feasibility of the method, and evaluated the

al-gorithms.

8.2 Future Work

In the futurewe will ontinue our workfor thethree problems asfollows.

Evaluationofalignmentstrategiesandsystems Wehaveproposedthe

KitAMOframeworkfor omparativeevaluationsofthenon-intera tive

align-ment omponents, and implemented a prototype of KitAMO. The urrent

implementation supports evaluation of alignment algorithms. In thefuture

wewillextendthesystemto supportevaluationof ombinationandltering

methods. Currently, evaluation inKitAMO an be based on the quality of

alignmentsuggestionsandthetimethatalignmentalgorithmsneedto

gener-atesuggestions. Thequalityofsuggestionsismeasured usingthere alland

pre ision. Sin e the user may need other evaluation riteria and

measure-ment methods for dierent evaluationpurposes,KitAMO will in lude more

ommonly usedevaluationapproa hes, e.g. falloutand f-measure, and may

allowusers to plug-intheir own evaluationstrategiesbasedon agiven API.

The urrenttest asesaresmallpie esfromlargebiomedi alontologies,and

they are taxonomies. We would like to build a testbed whi h in ludes test

ases withontologies ofdierent kindsandsizes, supportingdierent

evalu-ation purposes. Further,thevisualization omponentsof thesystem an be

improved.

Re ommendationof alignmentstrategies Wehaveproposedamethod

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Even thoughwehaveshownthefeasibilityofourmethodandhaveobtained

good results for the alignment problem in the experiment, it is ne essary

to perform more experiments with dierent kinds of ontologies. We also

wanttoinvestigatetheinuen eofthedierent hoi esinthedierentsteps

of the method. This in ludes investigating dierent segment pair sele tion

algorithms, re ommendation measures and re ommendation algorithms. It

will alsobe interesting to lookat how theideas from[MJE06℄and [ESS05 ℄

an be usedto augment our approa h. For instan e, when more knowledge

is obtained regarding the dierent strategies and their previous use (as in

[MJE06 ℄), this knowledge ouldbe usedasarst stepto lter theavailable

strategiesandit anbeusedbythere ommendationstrategy. Alsothe

opti-mization approa h in[ESS05℄maybe usefulfor ndingbetter ombinations

aswellaswithinthere ommendationstep. Thestudieswillleadustodene

a theoreti al framework for re ommendation. Finally, we intend to develop

a toolthatsupports the framework byextending theKitAMO system.

SAMBO and ontology alignment strategies In SAMBO we have

ex-perimented with several alignment strategies. In the future we will

de-velop and implement new alignment algorithms, for example

onstraint-based algorithms, and we also want to experiment with dierent

ombina-tion andlteringapproa hes. Using KitAMO,we willevaluate thedierent

alignment strategies. The lessons learned in evaluation would lead to

im-proved strategies. On the other hand, dierent alignment strategies may

produ e new requirements on KitAMO. A lotof work has been devoted to

thenon-intera tive alignment omponents,but aligning ontologies is hardly

a fully automati task. The intera tive omponents need more study. We

have worked on integrating an intera tive ontology visualization tool into

SAMBO, whi h not only improves the user interfa e to visualize

informa-tion, but may also lead to the development of new alignment algorithms.

Further,SAMBOwillbeextendedto bettersupportontologiesfromoutside

of the biomedi al area. We also envision that after evaluation and

re om-mendation of alignment strategies, KitAMO an be asked to generate an

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Department of Computer and Information Science

Linköpings universitet

Dissertations

Linköping Studies in Science and Technology

No 14

Anders Haraldsson: A Program Manipulation

System Based on Partial Evaluation, 1977, ISBN

91-7372-144-1.

No 17

Bengt Magnhagen: Probability Based Verification

of Time Margins in Digital Designs, 1977, ISBN

91-7372-157-3.

No 18

Mats Cedwall: Semantisk analys av

process-beskrivningar i naturligt språk, 1977, ISBN

91-7372-168-9.

No 22

Jaak Urmi: A Machine Independent LISP

Compil-er and its Implications for Ideal Hardware, 1978,

ISBN 91-7372-188-3.

No 33

Tore Risch: Compilation of Multiple File Queries

in a Meta-Database System 1978, ISBN

91-7372-232-4.

No 51

Erland Jungert: Synthesizing Database Structures

from a User Oriented Data Model, 1980, ISBN

91-7372-387-8.

No 54

Sture Hägglund: Contributions to the

Develop-ment of Methods and Tools for Interactive Design

of Applications Software, 1980, ISBN

91-7372-404-1.

No 55

Pär Emanuelson: Performance Enhancement in a

Well-Structured Pattern Matcher through Partial

Evaluation, 1980, ISBN 91-7372-403-3.

No 58

Bengt Johnsson, Bertil Andersson: The

Human-Computer Interface in Commercial Systems, 1981,

ISBN 91-7372-414-9.

No 69

H. Jan Komorowski: A Specification of an

Ab-stract Prolog Machine and its Application to Partial

Evaluation, 1981, ISBN 91-7372-479-3.

No 71

René Reboh: Knowledge Engineering Techniques

and Tools for Expert Systems, 1981, ISBN

91-7372-489-0.

No 77

Östen Oskarsson: Mechanisms of Modifiability in

large Software Systems, 1982, ISBN

91-7372-527-7.

No 94

Hans Lunell: Code Generator Writing Systems,

1983, ISBN 91-7372-652-4.

No 97

Andrzej Lingas: Advances in Minimum Weight

Triangulation, 1983, ISBN 91-7372-660-5.

No 109

Peter Fritzson: Towards a Distributed

Program-ming Environment based on Incremental

Compila-tion,1984, ISBN 91-7372-801-2.

No 111

Erik Tengvald: The Design of Expert Planning

Systems. An Experimental Operations Planning

System for Turning, 1984, ISBN 91-7372-805-5.

No 155

Christos Levcopoulos: Heuristics for Minimum

Decompositions of Polygons, 1987, ISBN

91-7870-133-3.

No 165

James W. Goodwin: A Theory and System for

Non-Monotonic Reasoning, 1987, ISBN

91-7870-183-X.

No 170

Zebo Peng: A Formal Methodology for Automated

Synthesis of VLSI Systems, 1987, ISBN

91-7870-225-9.

No 174

Johan Fagerström: A Paradigm and System for

Design of Distributed Systems, 1988, ISBN

91-7870-301-8.

No 192

Dimiter Driankov: Towards a Many Valued Logic

of Quantified Belief, 1988, ISBN 91-7870-374-3.

No 213

Lin Padgham: Non-Monotonic Inheritance for an

Object Oriented Knowledge Base, 1989, ISBN

91-7870-485-5.

No 214

Tony Larsson: A Formal Hardware Description and

Verification Method, 1989, ISBN 91-7870-517-7.

No 221

Michael Reinfrank: Fundamentals and Logical

Foundations of Truth Maintenance, 1989, ISBN

91-7870-546-0.

No 239

Jonas Löwgren: Knowledge-Based Design Support

and Discourse Management in User Interface

Man-agement Systems, 1991, ISBN 91-7870-720-X.

No 244

Henrik Eriksson: Meta-Tool Support for

Knowl-edge Acquisition, 1991, ISBN 91-7870-746-3.

No 252

Peter Eklund: An Epistemic Approach to

Interac-tive Design in Multiple Inheritance

Hierar-chies,1991, ISBN 91-7870-784-6.

No 258

Patrick Doherty: NML3 - A Non-Monotonic

For-malism with Explicit Defaults, 1991, ISBN

91-7870-816-8.

No 260

Nahid Shahmehri: Generalized Algorithmic

De-bugging, 1991, ISBN 91-7870-828-1.

No 264

Nils Dahlbäck: Representation of

Discourse-Cog-nitive and Computational Aspects, 1992, ISBN

91-7870-850-8.

No 265

Ulf Nilsson: Abstract Interpretations and Abstract

Machines: Contributions to a Methodology for the

Implementation of Logic Programs, 1992, ISBN

91-7870-858-3.

No 270

Ralph Rönnquist: Theory and Practice of

Tense-bound Object References, 1992, ISBN

91-7870-873-7.

No 273

Björn Fjellborg: Pipeline Extraction for VLSI Data

Path Synthesis, 1992, ISBN 91-7870-880-X.

No 276

Staffan Bonnier: A Formal Basis for Horn Clause

Logic with External Polymorphic Functions, 1992,

ISBN 91-7870-896-6.

No 277

Kristian Sandahl: Developing Knowledge

Man-agement Systems with an Active Expert

Methodolo-gy, 1992, ISBN 91-7870-897-4.

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