Linked Open Data Alignment & Querying

140 Pages·2012·2.77 MB·English

Linked Open Data Alignment & Querying
A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
By
PRATEEK JAIN
B. Tech., DA-IICT, India, 2006
2012
Wright State University
COPYRIGHTBY
PrateekJain
2012
WRIGHTSTATEUNIVERSITY
SCHOOLOFGRADUATESTUDIES
August21,2012
I HEREBY RECOMMEND THAT THE DISSERTATION PREPARED UNDER MY SU-
PERVISION BY Prateek Jain ENTITLED Linked Open Data Alignment & Querying BE AC-
CEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DoctorofPhilosophy.
AmitP.Sheth,Ph.D.
DissertationDirector
ArthurA.Goshtasby,Ph.D.
Director,ComputerSciencePh.D.Program
AndrewT.Hsu,Ph.D.
Dean,SchoolofGraduateStudies
Committeeon
FinalExamination
AmitP.Sheth, Ph.D.
PascalHitzler, Ph.D.
KrishnaprasadThirunarayan, Ph.D.
PeterZ.Yeh, Ph.D.
KunalVerma, Ph.D.
s
ABSTRACT
Jain, Prateek . Ph.D.,Department of Computer Science & Engineering, Wright State University, 2012.
LinkedOpenDataAlignment&Querying.
TherecentemergenceoftheLinkedDataapproachforpublishingdatarepresentsamajorstepforward
inrealizingtheoriginalvisionofawebthatcan”understandandsatisfytherequestsofpeopleandmachines
to use the web content” i.e. the Semantic Web. This new approach has resulted in the Linked Open Data
(LOD) Cloud, which includes more than 295 large datasets contributed by experts belonging to diverse
communities such as geography, entertainment, and life sciences. However, the current interlinks between
datasetsintheLODCloud aswewillillustrate aretooshallowtorealizemuchofthebenefitspromised. If
thislimitationisleftunaddressed,thentheLODCloudwillmerelybemoredatathatsuffersfromthesame
kindsofproblems,whichplaguetheWebofDocuments,andhencethevisionoftheSemanticWebwillfall
short.
This thesis presents a comprehensive solution to address the issue of alignment and relationship iden-
tification using a bootstrapping based approach. By alignment we mean the process of determining corre-
spondencesbetweenclassesandpropertiesofontologies. Weidentifysubsumption,equivalenceandpart-of
relationship between classes. The work identifies part-of relationship between instances. Between proper-
ties we will establish subsumption and equivalence relationship. By bootstrapping we mean the process of
being able to utilize the information which is contained within the datasets for improving the data within
them. Theworkshowcasesuseofbootstrappingbasedmethodstoidentifyandcreatericherrelationshipsbe-
tweenLODdatasets. TheBLOOMSproject(http://wiki.knoesis.org/index.php/BLOOMS)andthePLATO
project, bothbuiltaspartofthisresearch, haveprovidedevidencetothefeasibilityandtheapplicabilityof
thesolution.
Contents
1 Introduction 1
1.1 GoalsofthisDissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 ConceptualContributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.2 Artifacts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 ChapterOverview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 SemanticWebandStateoftheArt 7
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.1 DomainSpecificOntology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.2 UpperLevelOntology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.3 BasicRelationshipspresentinOntologies . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 RDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4 SPARQL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4.1 SPARQLQueryTypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3 LinkedData 16
3.1 Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2 LinkedOpenData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.4 Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4.1 AbsenceofSchemaLevelLinks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4.2 LackofConceptualDescriptionofDatasets . . . . . . . . . . . . . . . . . . . . . . 22
3.4.3 Lackofexpressivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.4.4 Difficultieswithrespecttoquerying . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4 OntologyAlignmentforConceptsonLinkedOpenData 28
4.1 OntologyMatching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1.2 Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.1.2.1 NameMatching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.1.2.2 DescriptionMatching . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.1.2.3 Constraint-basedMatching . . . . . . . . . . . . . . . . . . . . . . . . . 30
v
4.1.2.4 InstancebasedMatching . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.1.3 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.2 BLOOMSApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3.1 Evaluation: OntologyAlignmentEvaluationInitiativeOrientedTrack. . . . . . . . . 39
4.3.2 Evaluation: OntologyAlignmentEvaluationInitiativeBenchmarkTrack. . . . . . . 41
4.4 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5 ContextualOntologyAlignmentofLOD 44
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2 KnowledgeRequirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.3.1 ConstructBLOOMS+Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.3.2 ComputeClassSimilarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.3.3 ComputeContextualSimilarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.3.4 ComputeOverallSimilarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.4.1 DataSet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.4.2 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.4.3 ResultsandDiscussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.5 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
6 PartonomicalRelationshipIdentificationonLinkedOpenData 59
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
6.2 Winston’sApproachtoPart-of
Relationships—Ontologized . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
6.3.1 CandidateGeneration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
6.3.2 HypothesisGeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
6.3.3 HypothesisTesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.4.1 Intra-DatasetInstance-LevelPartonomyDiscovery . . . . . . . . . . . . . . . . . . 71
6.4.2 Inter-DatasetInstance-LevelPartonomyDiscovery . . . . . . . . . . . . . . . . . . 74
6.4.3 Assertionofschemalevellinks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.5 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
7 QueryingPartonomicalRelationshiponLODcloud 79
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
7.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
7.3 Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
7.4 PARQApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
7.4.1 SystemArchitecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
7.4.1.1 MappingRepository . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
7.4.1.2 TransformationRuleGenerator . . . . . . . . . . . . . . . . . . . . . . . 83
7.4.1.3 QueryRe-writer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
7.4.2 Meta-levelTransformationRules . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
7.4.3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
7.4.3.1 Explanation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
7.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
7.5.1 GeonamesResultsandDiscussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
7.5.2 AdministrativeGeographyOntologyResultsandDiscussion . . . . . . . . . . . . . 92
7.5.3 SummaryofResultsandLimitations . . . . . . . . . . . . . . . . . . . . . . . . . . 94
7.6 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
7.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
8 LOQUS:LinkedOpenDataSPARQLQueryingSystem 100
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
8.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
8.3 OurApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
8.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
8.5 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
8.6 ConclusionandFutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
9 Conclusion 112
9.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
9.2 FurtherWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
9.2.1 RicherRelationshipIdentificationonLOD . . . . . . . . . . . . . . . . . . . . . . 114
9.2.2 YellowPagesforLOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
9.2.3 FlexibleQuestionAnsweringusingLOD . . . . . . . . . . . . . . . . . . . . . . . 115
9.2.4 PropertyMatchingonLOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
9.2.5 LODIntegrationandEnhancement . . . . . . . . . . . . . . . . . . . . . . . . . . 117
9.3 FinalRemarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Bibliography 120
List of Figures
2.1 Exampleofanontology. Source: http://knoesis.org/research/semweb/projects/stt/ . . . . . . 10
2.2 ExampleofanRDFGraph. Source: http://www.w3.org/TR/rdf-primer/ . . . . . . . . . . . 13
3.1 RDFInterlinkingbetweendifferentdatasetsusing . . . . . . . . . . . . . . . . . . . . . . 18
3.2 DatasetsavailableaspartofLODinMay2007 . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3 DatasetsavailableaspartofLODin2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.4 PossibleLODintegrationwithSUMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.1 BLOOMS trees for Jazz Festival with sense Jazz Festival and for Event with sense Event.
Tosavespace,somecategoriesarenotexpandedtolevel4. . . . . . . . . . . . . . . . . . 34
5.1 BLOOMS+treesforRecordLabelandMusicCompany . . . . . . . . . . . . . . . . . . . 49
6.1 PLATOSystemArchitecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
7.1 PARQsystemflowchart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
7.2 PARQResultsonGeonames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
7.3 ComparsionPSPARQLandPARQonGeonamesforrespondent4 . . . . . . . . . . . . . . 99
7.4 ComparisonforOrdnanceSurveyDatasetforRespondent4 . . . . . . . . . . . . . . . . . . 99
8.1 LOQUSArchitecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
viii
List of Tables
3.1 SomeDatasetsthatarePartofLODCloud . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.1 Resultsontheorientedmatchingtrack. ResultsforRiMOMandAROMAhavebeentaken
from the OAEI 2009 website. Legends: Prec=Precision, Rec=Recall, A-API=Alignment
API,OMV=OMViaUO,NaN=divisionbyzero,likelyduetoemptyalignment. . . . . . . . 39
4.2 Comparisonofvarioussystemsonthebenchmarktrack. ResultsforRiMOMandAROMA
havebeenreusedfromtheOAEI2009website. Legends: Prec=Precision,Rec=Recall. . . . 41
5.1 Common nodes between the two trees in Figure 5.3.2, and their depth. The first column
givesthecommonnodesbetweenthetwotreesrootedatRecordLabelandMusicIndustry.
Thesecondcolumngivesthedepth(thedistancefromroot)ofthesenodesintheBLOOMS+
treerootedatRecordLabel–i.e. thesourcetree. . . . . . . . . . . . . . . . . . . . . . . . 50
5.2 SamplemappingsofLODontologiestoPROTON. . . . . . . . . . . . . . . . . . . . . . . 53
5.3 Results for various solutions on the task of aligning LOD schemas to PROTON. Legend:
S-Match-M=ResultofS-MatchMinimalSet,S-Match-C=ResultofS-MatchCompleteSet,
Prec=Precision,Rec=Recall,F=F-MeasurePRO=PROTONOntology,FB=FreebaseOntol-
ogy,DB=DBpediaOntology,GEO=GeonamesOntology . . . . . . . . . . . . . . . . . . . 55
5.4 SampleofcorrectmappingsfromLODontologiestoPROTONgeneratedbyBLOOMS+. . 55
5.5 SampleofincorrectmappingsfromLODontologiestoPROTONgeneratedbyBLOOMS+. 56
6.1 Sixtypeofpartonomicrelationwithrelationalelements . . . . . . . . . . . . . . . . . . . . 62
6.2 PrecisionofthesixdifferentrelationtypesbetweenDBpediaentities . . . . . . . . . . . . . 73
6.3 ThistableshowsPLATO’sperformanceonprecisionandrecallfortheDish-Ingredienttask,
and PLATO’s performance on precision for the Anatomy-Organ task. Recall was not re-
portedforthesecondtaskbecauseoftimeandresourcelimitations. . . . . . . . . . . . . . 75
6.4 PrecisionasmeasuredonSchemaLevelLinksBetweenDBpediaentities . . . . . . . . . . 76
7.1 ImportantPropertiesinGeonames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
7.2 ImportantPropertiesinAdministrativeGeographyOntology . . . . . . . . . . . . . . . . . 89
8.1 Resultexecutionofqueriesovergeonames . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
8.2 Resultexecutionofqueriesoverdbpedia . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
8.3 Resultexecutionofqueriesoverlinkedmdb . . . . . . . . . . . . . . . . . . . . . . . . . . 108
8.4 Resultofusersubmittedquery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
8.5 ResultexecutionofqueriesusingLOQUS . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
8.6 ComaparisonLODSPARQLQueryProcessingSystems . . . . . . . . . . . . . . . . . . . 110
ix
Acknowledgement
IwouldliketothankmyadvisorAmitP.Shethforallhisadvice, guidanceandsupport. Ihavebeenlucky
enough to work with a brilliant professor. Professor Sheth has taught me how to have a long term vision
and choose important problems to solve. He has taught me the importance of grounding my research. The
commonfactorbetweenthetwoofusisthedesiretopursueresearchthatwillbevaluablebothintheshort
term and the long term. Over the years, I have learnt a lot from him and hope to maintain this relationship
foralongtime.
Iwanttoexpressmygratitudetothemembersofmydissertationcommittee-PascalHitzler,KunalVerma,
PeterZ.YehandKrishnaprasadThirunarayan. Itrulyenjoyedmyinteractionwiththemandappreciatethe
extremelyvaluablefeedbacktheyhaveprovidedtomeoverthistime. IwouldliketothankPascal,Peterand
Kunalspecifically. Ihavebeenveryfortunatetohaveworkedwiththemonseveralwonderfulprojectsthat
havecontributedimmenselytowardsthisdissertation. Theyareextremelytalentedandgreatresearchersand
wonderfulcollaboratorsaswell. Theyarealsotrueandwonderfulfriends. Theytaughtmetheartofiden-
tifyingthecorrectproblemandpresentingsolutions. KunalandPetertookpersonalinterestinmyresearch
anditsapplicationinindustry. Iamsincerelythankfultothemforgivingmetheopportunitytobeanintern
undertheirguidanceatAccentureTechnologyLabs.
I want to thank Cory Henson for a wonderful time in the graduate school. I have throughly enjoyed our
discussionsrelatedtoresearch,football,baseballandlifeingeneral. Themanyhelpfulcommentsandfeed-
back I received from him have also been extremely valuable. He is a wonderful friend and I wish him my
bestforthecareerahead.
I would like to express my appreciation to members (both past and present) of the Kno.e.sis Center. In
particularIwouldliketothankPavanKapanipathi,SarasiLalithsenaandAjithRanabahufortheirhelpand
co-operationduringmystudies. Itwasmypleasuretohaveknownandinteractedwiththesewonderfulfolks.
I want to acknowledge Tonya Davis, Valerie Smith, Jennifer Limoli, Paula Price and Wendy Chetcuti and
x