scaling ontology alignment

101 Pages·2013·1.8 MB·English

SCALING ONTOLOGY ALIGNMENT
by
RYANE.FRECKLETON
B.S.C.p.E,UniversityofColoradoColoradoSprings,2008
AthesissubmittedtotheGraduateFacultyofthe
UniversityofColoradoColoradoSprings
inpartialfulfillmentofthe
requirementsforthedegreeof
MasterofScienceinComputerScience
DepartmentofComputerScience
2013
c CopyrightbyRyanE.Freckleton2013
�
AllRightsReserved
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ThisthesisfortheMasterofScienceinComputerSciencedegreeby
RyanE.Freckleton
hasbeenapprovedforthe
DepartmentofComputerScience
by
Dr. JugalKalita,Chair
Dr. CharlesShub
Dr. LisaHines
Dr. SuzetteStoutenburg
Date
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Freckleton,RyanE.(M.S.C.S.,ComputerScience)
ScalingOntologyAlignment
ThesisdirectedbyProfessorDr. JugalKalita
Abstract
As ontologies become more prevalent in biomedicine and other fields, effective on-
tology alignment is a necessary for their economical and practical use. An ontology is a
groupofconceptsderivedfromacorpusofknowledge. Ontologyalignmentdeterminesthe
relationshipsbetweentheseconceptsacrossdifferentontologies. Thereforeontologyalign-
mentisanareaofactiveresearch,especiallyscalingontologyalignment,asthenumberand
sizeofontologiesincreasesdramatically.
This thesis describes an approach and implementation of ontology alignment called
Parallel Ontology Bridge, which maintains good alignment quality while increasing scala-
bility and speed of ontology alignment by matching linguistic and structural features in a
support vector machine. This approach is based on Ontology Bridge [1] and provides the
same advantages. It is able to handle non-equivalence relationships very effectively and is
a general approach to ontology alignment that can be used across many domains. Parallel
OntologyBridgeincreasesscalabilitybyusingmap-reduce,anapproachtobreakingdown
problems and running them in parallel. This thesis describes how this is done. Parallel
OntologyBridgeisalmosttwoordersofmagnitudefasterthanOntologyBridgeandshows
verygoodscalabilitywhilemaintainingqualityasmeasuredthroughF-Measure.
TheresultsofParallelOntologyBridgearecomparedagainstseveralotherscalability
approaches, both with experimental data and theoretical maximum scalability. Parallel
Ontology Bridge is significantly more scalable in the experimental data and maintains this
advantageduringtheoreticalanalysis.
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TomyMontessoriteacher,whoalwaysknewthejoyoflearningandunderstanding.
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Acknowledgements
I’d like to acknowledge all the people that have positively affected the creation of this
thesis. My employer, The MITRE Corporation, my coworkers, my advisory committee,
myfamily.
I’d like to especially thank Dr. Suzette Stoutenburg. Without her help and previous
workinthisareaIwouldnotbeabletocreatethisthesis.
I’despeciallyliketothankmymother,IreneFreckletonandfather,GroverFreckleton
for their emotional support as well as deep discussions on the concepts of ontology align-
ment and graphical presentation. I’d also like to thank my Aunt Karen, whose excitement
wasinfectiousandwaywithwordshelpedmakethisthesissuccinctandclear.
Myfriend,Dr. GregoryPlettgavemeincomparablehelpandadvicewithtypesetting.
TimFlink,myfriendandfellowgraduatestudent,sawarchitecturalissuesIwasblindto.
My friend and colleague Dr. Norman Facas gave unparalleled advice on organization
andtheappropriatelayoutofgraphsanddata.
Thank you Dr. Lisa Hines, for giving me one-on-one attention get up to speed on
biology and medicine. Dr. Charlie Shub, thank you for your continued support and fo-
cus. Your mentorship during my undergraduate studies prepared me for this thesis and my
professionalcareer.
Finally, I’d like to thank my advisor Dr. Jugal Kalita. His expertise in artificial intel-
ligencehasbeenunparalleled.
Withouttheirassistance,feedbackandsupportthiswouldnotbepossibletocomplete.
It’sbeenalong,sometimesstressfuljourneyonthispathofknowledge. Iappreciateallthat
you’vedoneforme.
Thankyou.
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