Entity Resolution: TutorialEntityResolution:Tutorial

Li G t Ashwin MachanavajjhalaLise GetoorUniversityofMaryland

College Park MD

Ashwin MachanavajjhalaDukeUniversityDurham,NCCollegePark,MD ,

http://www.cs.umd.edu/~getoor/Tutorials/ER_VLDB2012.pdf

http://goo.gl/f5eym

WhatisEntityResolution?Problemofidentifyingandlinking/groupingdifferent

manifestationsofthesamerealworldobject.f f j

Examplesofmanifestationsandobjects:p j Differentwaysofaddressing(names,emailaddresses,FaceBook

accounts)thesamepersonintext. Webpageswithdifferingdescriptionsofthesamebusiness. Differentphotosofthesameobject.

Ironically,EntityResolutionhasmanyduplicatenames

DuplicatedetectionRecordlinkage

Coreference resolution

Object consolidation

Coreference resolutionReferencereconciliation

Fuzzymatch

Deduplication

ObjectidentificationObjectconsolidation

Entity clustering

y

Entityclustering

Household matching

Approximatematch

Merge/purgeIdentityuncertainty

ReferencematchingHouseholding

HouseholdmatchingMerge/purge

Hardeningsoftdatabases

Doubles

gg

ERMotivatingExamples LinkingCensusRecords Public HealthPublicHealth Websearch Comparison shopping Comparisonshopping Counterterrorism Spam detection Spamdetection MachineReading

ERandNetworkAnalysis

before after

Motivation:NetworkScience Measuringthetopologyoftheinternetusingtraceroute

IPAliasingProblem[Willinger etal.2009]

IPAliasingProblem[Willinger etal.2009]

TraditionalChallengesinER Name/Attributeambiguity

Thomas CruiseThomasCruise

MichaelJordan

TraditionalChallengesinER Name/Attributeambiguity Errors due to data entryErrorsduetodataentry

TraditionalChallengesinER Name/Attributeambiguity Errors due to data entryErrorsduetodataentry MissingValues

[Gilletal;Univ ofOxford2003]

TraditionalChallengesinER Name/Attributeambiguity Errors due to data entryErrorsduetodataentry MissingValues Changing Attributes ChangingAttributes

Dataformatting

/ Abbreviations/DataTruncation

BigDataERChallenges

BigDataERChallenges LargerandmoreDatasets

NeedefficientparalleltechniquesM H t it MoreHeterogeneity Unstructured,UncleanandIncompletedata.Diversedatatypes. No longer just matching names with names, but Amazon profiles withNolongerjustmatchingnameswithnames,butAmazonprofileswith

browsinghistoryonGoogleandfriendsnetworkinFacebook.

BigDataERChallenges LargerandmoreDatasets

NeedefficientparalleltechniquesM H t it MoreHeterogeneity Unstructured,UncleanandIncompletedata.Diversedatatypes.

More linked Morelinked Needtoinferrelationshipsinadditiontoequality

MultiRelational Dealwithstructureofentities(AreWalmart andWalmart Pharmacy

thesame?)

l d Multidomain Customizablemethodsthatspanacrossdomains

Multiple applications (web search versus comparison shopping) Multipleapplications(websearchversuscomparisonshopping) Servediverseapplicationwithdifferentaccuracyrequirements

Outline1. AbstractProblemStatement2 Algorithmic Foundations of ER2. AlgorithmicFoundationsofER3. ScalingERtoBigData4 Challenges & Future Directions4. Challenges&FutureDirections

Outline1. AbstractProblemStatement2 Algorithmic Foundations of ER2. AlgorithmicFoundationsofER

a) DataPreparationandMatchFeaturesb) Pairwise ERc) ConstraintsinERd) Algorithms

Record Linkage

5minutebreak

RecordLinkage Deduplication CollectiveER

3. ScalingERtoBigData4. Challenges&FutureDirections

Outline1. AbstractProblemStatement2 Algorithmic Foundations of ER2. AlgorithmicFoundationsofER3. ScalingERtoBigData

a) Blocking/Canopy Generationa) Blocking/CanopyGenerationb) DistributedER

4. Challenges & Future Directions4. Challenges&FutureDirections

Outline1. AbstractProblemStatement2 Algorithmic Foundations of ER2. AlgorithmicFoundationsofER3. ScalingERtoBigData4 Challenges & Future Directions4. Challenges&FutureDirections

ScopeoftheTutorial Whatwecover:

FundamentalalgorithmicconceptsinER ScalingERtobigdatasets TaxonomyofcurrentERalgorithms

Whatwedonotcover: Schema/ontology resolutionSchema/ontologyresolution Datafusion/integration/exchange/cleaning Entity/InformationExtraction PrivacyaspectsofEntityResolution DetailsonsimilaritymeasuresTechnical details and proofs Technicaldetailsandproofs

ERReferences Book/SurveyArticles

DataQualityandRecordLinkageTechniques[T Herzog F Scheuren W Winkler Springer 07][T.Herzog,F.Scheuren,W.Winkler,Springer, 07]

DuplicateRecordDetection[A.Elmagrid,P.Ipeirotis,V.Verykios,TKDE07] AnIntroductiontoDuplicateDetection[F.Naumann,M.Herschel,M&P

synthesislectures2010]y ] EvaluationofEntityResolutionApproachedonRealworldMatchProblems

[H.Kopke,A.Thor,E.Rahm,PVLDB2010] DataMatching[P.Christen,Springer2012]

Tutorials RecordLinkage:SimilaritymeasuresandAlgorithms

[N.Koudas,S.Sarawagi,D.Srivatsava SIGMOD06] DatafusionResolvingdataconflictsforintegration

[X.Dong,F.Naumann VLDB09]Entity Resolution: Theory Practice and Open Challenges EntityResolution:Theory,PracticeandOpenChallengeshttp://goo.gl/Ui38o [L.Getoor,A.Machanavajjhala AAAI12]

PART 1ABSTRACT PROBLEM STATEMENTPART1ABSTRACTPROBLEMSTATEMENT

AbstractProblemStatementRealWorld DigitalWorld

Records//Mentions

Deduplication ProblemStatement Clustertherecords/mentionsthatcorrespondtosameentityy

Deduplication ProblemStatement Clustertherecords/mentionsthatcorrespondtosameentityy Intensional Variant:Computeclusterrepresentative

RecordLinkageProblemStatement Linkrecordsthatmatchacrossdatabases

AB

ReferenceMatchingProblem Matchnoisyrecordstocleanrecordsinareferencetable

ReferenceblTable

AbstractProblemStatementRealWorld DigitalWorld

Deduplication ProblemStatement

Deduplication withCanonicalization

GraphAlignment(&motifsearch)

Graph1 Graph2

Relationshipsarecrucial

Relationshipsarecrucial

Notation R:setofrecords/mentions(typed) H: set of relations / hyperedges (typed)H:setofrelations/hyperedges (typed) M:setofmatches(recordpairsthatcorrespondtosameentity) N: set of non matches ( d i di t diff t titi ) N:setofnonmatches(recordpairscorrespondingtodifferententities) E:setofentities L set of links L:setoflinks

T (M N E L ) di l ld True(Mtrue,Ntrue,Etrue,Ltrue):accordingtorealworldvs Predicted(Mpred,Npred,Epred,Lpred ):byalgorithm

RelationshipbetweenMtrue andMpred Mtrue (SameAs ,Equivalence) M (Similar representations and similar attributes)Mpred (Similarrepresentationsandsimilarattributes)

MtrueRxR Mpredtrue pred

Metrics Pairwise metrics

Precision/Recall,F1 #ofpredictedmatchingpairs

Clusterlevelmetrics purity,completeness,complexityPrecision/Recall/F1: Cluster level closest cluster MUC B3 Precision/Recall/F1:Clusterlevel,closestcluster,MUC,B3 ,RandIndex

Generalizedmergedistance[Menestrina etal,PVLDB10]

Littleworkthatevaluationscorrectpredictionoflinks

TypicalAssumptionsMade Eachrecord/mentionisassociatedwithasinglerealworldentity.y

Inrecordlinkage,noduplicatesinthesamesource If two records/mentions are identical then they are true Iftworecords/mentionsareidentical,thentheyaretruematches

( ) M(,) Mtrue

ERversusClassificationFindingmatchesvs nonmatchesisaclassificationproblem

Imbalanced:typicallyO(R)matches,O(R^2)nonmatches

Instancesarepairsofrecords.PairsarenotIID

(,) Mtrue( ) MAND

( , ) Mtrue

(,) MtrueAND(,) Mtrue

ERvs (Multirelational)ClusteringComputingentitiesfromrecordsisaclusteringproblem

Intypicalclusteringalgorithms(kmeans,LDA,etc.)number of clusters is a constant or sub linear in R.numberofclustersisaconstantorsublinearinR.

In ER: number of clusters is linear in R and averageInER:numberofclustersislinearinR,andaverageclustersizeisaconstant.Significantfractionofclustersaresingletons.g

PART 2ALGORITHMIC FOUNDATIONS OF ERPART2ALGORITHMICFOUNDATIONSOFER

OutlineofPart2a) DataPreparationandMatchFeatures

b) Pairwise ER Determiningwhetherornotapairofrecordsmatch

c) ConstraintsinER5minutebreak

d) Algorithms Recordlinkage(Propagationthroughexclusitivity negativeconstraint), Deduplication (Propagationthroughtransitivitypositiveconstraint), Collective(PropagationthroughGeneralConstraints)

MOTIVATING EXAMPLE:MOTIVATINGEXAMPLE:BIBLIOGRAPHICDOMAIN

Entities&RelationsinBibliographicDomain

Wrote AuthorName Author Mention

NameString

Paper

Research Area NameString

I tit ti Institute MentionWorksAtp

Title# of AuthorsTopicWord1

InstitutionName

Institute MentionNameString

Word1Word 2WordN

Paper MentionTitleString

Cites VenueName

Venue MentionNameStringAppearsIn gpp

:cooccurrencerelationships:resolutionrelationships

:entityrelationships

PART 2DATA PREPARATION &PART2aDATAPREPARATION&MATCHFEATURES

Normalization Schemanormalization

SchemaMatching e.g.,contactnumberandphonenumberC d tt ib t f ll dd t it t t i Compoundattributes fulladdressvs str,city,state,zip

Nestedattributes Listoffeaturesinonedataset(airconditioning,parking)vs eachfeatureaboolean attributeboolean attribute

Setvaluedattributes Setofphonesvs primary/secondaryphone

Record segmentation from text Recordsegmentationfromtext Datanormalization

Oftenconverttoalllower/allupper;removewhitespaced i d i l h i k hi l detectingandcorrectingvaluesthatcontainknowntypographicalerrorsorvariations,

expandingabbreviationsandreplacingthemwithstandardforms;replacingnicknames with their proper name formsnicknameswiththeirpropernameforms

Usuallydonebasedondictionaries(e.g.,commercialdictionaries,postaladdresses,etc.)

MatchingFeatures Fortworeferencesxandy,computeacomparisonvectorof

similarityscoresofcomponentattribute. [1stauthormatchscore,

papermatchscore,venuematchscore,yearmatchscore,.]

Similarityscoresy Boolean(matchornotmatch) Realvaluesbasedondistancefunctions

SummaryofMatchingFeaturesH dl

Equalityonaboolean predicate AlignmentbasedorTwotieredGoodforNames

HandleTypographicalerrors

Editdistance Levenstein,SmithWaterman,Affine

Setsimilarity

JaroWinkler,SoftTFIDF,MongeElkan PhoneticSimilarity

Soundex Jaccard,Dice

VectorBased Cosine similarity, TFIDF

Translationbased Numericdistancebetweenvalues DomainspecificCosinesimilarity,TFIDF

Useful packages

pGoodforTextlikereviews/tweets Usefulfor

abbreviations,alternate names. Usefulpackages:

SecondString,http://secondstring.sourceforge.net/ Simmetrics:http://sourceforge.net/projects/simmetrics/

Li Pi htt // li i /li i /i d ht l

alternatenames.

LingPipe,http://aliasi.com/lingpipe/index.html

RelationalMatchingFeatures Relationalfeaturesareoftensetbased

Setofcoauthorsforapaperf i i i Setofcitiesinacountry

Setofproductsmanufacturedbymanufacturer

Canusesetsimilarityfunctionsmentionedearlier CommonNeighbors:Intersectionsize Jaccards Coefficient: Normalize by union sizeJaccard s Coefficient:Normalizebyunionsize AdarCoefficient:Weightedsetsimilarity

C b t i il it i t f l Canreasonaboutsimilarityinsetsofvalues AverageorMax Otheraggregates

PART 2 bPAIRWISE MATCHINGPART2bPAIRWISE MATCHING

Pairwise MatchScoreProblem:Givenavectorofcomponentwisesimilaritiesforapairof

records(x,y),computeP(xandymatch).

Solutions:1. Weightedsumoraverageofcomponentwisesimilarityscores.

Thresholddeterminesmatchornonmatch. 0.5*1stauthormatchscore +0.2*venuematchscore +0.3*papermatchscore.p p Hardtopickweights.

Matchonlastnamematchmorepredictivethanloginname. Match on Smith less predictive than match on Getoor or Machanavajjhala Matchon Smith lesspredictivethanmatchon Getoor or Machanavajjhala .

Hardtotuneathreshold.

Pairwise MatchScoreProblem:Givenavectorofcomponentwisesimilaritiesforapairof

records(x,y),computeP(xandymatch).

Solutions:1. Weightedsumoraverageofcomponentwisesimilarityscores.

Thresholddeterminesmatchornonmatch.2 Formulate rules about what constitutes a match2. Formulaterulesaboutwhatconstitutesamatch.

(1stauthormatchscore>0.7ANDvenuematchscore>0.8)OR(papermatchscore>0.9ANDvenuematchscore>0.9)M ll f l ti th i ht t f l i h d Manuallyformulatingtherightsetofrulesishard.

BasicMLApproach r =(x,y)isrecordpair, iscomparisonvector,Mmatches,U non

matches

Decisionrule)|()|(

UrPMrPR

)|(

Match rtRMatch-Non rtR

Fellegi &Sunter Model[FS,Science69] r =(x,y)isrecordpair, iscomparisonvector,Mmatches,U non

matches

Decisionrule)|()|(

UrPMrPR

)|(

Match rtR l

Match-NonMatchPotential

rtRrtRt

u

ul

NaveBayes Assumption: )|()|( MrPMrP ii

MLPairwise Approaches Supervisedmachinelearningalgorithms

Decisiontrees [Co hin ala et al IS01] [Cochinwala etal,IS01]

Supportvectormachines [Bilenko &Mooney,KDD03];[Christen,KDD08]

Ensembles of classifiers Ensemblesofclassifiers [Chenetal.,SIGMOD09]

ConditionalRandomFields(CRF) [Gupta & Sarawagi VLDB09][Gupta&Sarawagi,VLDB09]

Issues:Training set generation Trainingsetgeneration

Imbalancedclasses manymorenegativesthanpositives(evenaftereliminatingobviousnonmatchesusingBlocking)

Misclassification cost Misclassificationcost

CreatingaTrainingSetisakeyissue Constructingatrainingsetishard sincemostpairsofrecordsareeasynonmatches.y 100recordsfrom100cities. Only106 pairsoutoftotal108 (1%)comefromthesamecity

Somepairsarehardtojudgeevenbyhumans Inherentlyambiguous

E.g.,ParisHilton(personorbusiness)

Missingattributes Starbucks,Torontovs Starbucks,QueenStreet,Toronto

AvoidingTrainingSetGeneration Unsupervised/SemisupervisedTechniques

EMbasedtechniquestolearnparameters [Winkler06,Herzogetal07]

GenerativeModels [Ravikumar & Cohen UAI04][Ravikumar &Cohen,UAI04]

ActiveLearning CommitteeofClassifiers

[Sarawagi etalKDD00,Tajeda etalIS01] Provably optimizing precision/recallProvablyoptimizingprecision/recall

[Arasu etalSIGMOD10,Bellare etalKDD12] Crowdsourcing

[W t l VLDB 12 M t l VLDB 12 ] [WangetalVLDB12,MarcusetalVLDB12,]

CommitteeofClassifiers [Tejada etal,IS01]

ActiveLearningwithProvableGuarantees

Mostactivelearningtechniquesminimize01loss[Beygelzimer etalNIPS2010].

However,ERisveryimbalanced: Numberofnonmatches>100*numberofmatches. Classifyingallpairsasnonmatcheshaslow01loss(

ActiveLearningwithProvableGuarantees

Monotonicity ofPrecision[Arasu etalSIGMOD10]

ThereisalargerfractionofmatchesinC1thaninC2.

Algorithmsearchesfortheoptimalclassifierusingbinaryp g ysearchoneachdimension

ActiveLearningwithProvableGuarantees

[Bellare etalKDD12]

O (log2 n) calls to a blackbox 0 1 loss active learning algorithmO (log2 n) calls to a blackbox 0 1 loss active learning algorithmO(log2 n)callstoablackbox 01lossactivelearningalgorithm.

Exponentiallysmallerlabelcomplexitythan[Arasu etalSIGMOD10](in the worst case).

O(log2 n)callstoablackbox 01lossactivelearningalgorithm.

Exponentiallysmallerlabelcomplexitythan[Arasu etalSIGMOD10](in the worst case).

1. PrecisionConstrainedWeighted01LossProblem(using a Lagrange Multiplier )

(intheworstcase).(intheworstcase).

(usingaLagrangeMultiplier).2. Givenafixedvaluefor,weighted01Losscanbeoptimizedby(onecallto)a

blackbox activelearningclassifier.3 Ri ht l f i t d b hi ll ti l l ifi3. Rightvalueof iscomputedbysearchingoveralloptimalclassifiers.

Classifiersareembeddedina2dplane(precision/recall) Searchisalongtheconvexhulloftheembeddedclassifiers

Crowdsourcing Growinginterestinintegratinghumancomputationindeclarative

workflowengines. ERisanimportantproblem(e.g.,forevaluatingfuzzyjoins) [WangetalVLDB12,MarcusetalVLDB12,]

Opportunity:utilizecrowdsourcing forcreatingtrainingsets,orforactivelearning.

Keyopenissue:Handlingerrorsinhumanjudgments InanexperimentonAmazonMechanicalTurk:

Pairwise matchingjudgment,eachgivento5differentpeople Majorityofworkersagreedontruthononly90%ofpairwise judgments.

SummaryofSingleEntityERAlgorithms

Manyalgorithmsforindependentclassificationofpairsofrecordsasmatch/nonmatch

MLbasedclassification&FellegiSunterP Ad d f h Pro:Advancedstateoftheart

Con:Buildinghighfidelitytrainingsetsisahardproblem

ActiveLearning&Crowdsourcing forERareactiveareasofresearch.

PART 2CONSTRAINTSPART2cCONSTRAINTS

Constraints Importantformsofconstraints:

Transitivity:IfM1andM2match,M2andM3match,thenM1andyM3match

Exclusivity:IfM1matcheswithM2,thenM3cannotmatchwithM2Functional Dependency: If M1 and M2 match then M3 and M4 must FunctionalDependency:IfM1andM2match,thenM3andM4mustmatch

Transitivityiskeytodeduplication Exclusivityiskeytorecordlinkage Functionaldependenciesfordatacleaning,e.g.,

[Ananthakrishna etal.,VLDB02][Fan,PODS08][Bohannonetal ICDE07]al,ICDE07]

Positive&NegativeEvidence Positive

Transitivity:IfM1andM2match,M2andM3match,thenM1andyM3match

Exclusivity:IfM1doesntmatchwithM2,thenM3canmatchwithM2M2

FunctionalDependency:IfM1andM2match,thenM3andM4mustmatch

Negative Transitivity:IfM1andM2match,M2andM3donotmatch,thenM1

and M3 do not matchandM3donotmatch Exclusivity:IfM1matcheswithM2,thenM3cannotmatchwithM2 FunctionalDependency:IfM1andM2donotmatch,thenM3and

M4cannotmatch

Positive&NegativeEvidence Positive

Transitivity:IfM1andM2match,M2andM3match,thenM1andyM3match

Exclusivity:IfM1doesntmatchwithM2,thenM3canmatchwithM2M2

FunctionalDependency:IfM1andM2match,thenM3andM4mustmatch

Negative Transitivity:IfM1andM2match,M2andM3donotmatch,thenM1

and M3 do not matchandM3donotmatch Exclusivity:IfM1matcheswithM2,thenM3cannotmatchwithM2 FunctionalDependency:IfM1andM2donotmatch,thenM3and

M4cannotmatch

ConstraintTypesHardConstraint SoftConstraint

PositiveEvidence IfM1,M2matchthenM3,M4mustmatchIf t t h th i

IfM1,M2matchthenM3,M4morelikelytomatchIf t t h thIftwopapersmatch,theirvenues

matchIftwovenuesmatch,thentheirpapersaremorelikelytomatch

NegativeEvidence MentionM1andM2mustrefertodistinctentities(Uniqueness)Coauthors are distinct

IfM1,M2dontmatchthenM3,M4lesslikelytomatchIf institutions dont matchCoauthorsaredistinct

IfM1,M2dontmatchthenM3,M4cannotmatch

Ifinstitutionsdon tmatch,thenauthorslesslikelytomatch

Iftwovenuesdontmatch,thentheirpapersdontmatch

ConstraintTypesHardConstraint SoftConstraint

PositiveEvidence IfM1,M2matchthenM3,M4mustmatchIf t t h th i

IfM1,M2matchthenM3,M4morelikelytomatchIf t t h thIftwopapersmatch,theirvenues

matchIftwovenuesmatch,thentheirpapersaremorelikelytomatch

NegativeEvidence MentionM1andM2mustrefertodistinctentities(Uniqueness)Coauthors are distinct

IfM1,M2dontmatchthenM3,M4lesslikelytomatchIf institutions dont matchCoauthorsaredistinct

IfM1,M2dontmatchthenM3,M4cannotmatch

Ifinstitutionsdon tmatch,thenauthorslesslikelytomatch

Iftwovenuesdontmatch,thentheirpapersdontmatch

ConstraintTypesHardConstraint SoftConstraint

PositiveEvidence IfM1,M2matchthenM3,M4mustmatchIf t t h th i

IfM1,M2matchthenM3,M4morelikelytomatchIf t t h thIftwopapersmatch,theirvenues

matchIftwovenuesmatch,thentheirpapersaremorelikelytomatch

NegativeEvidence MentionM1andM2mustrefertodistinctentities(Uniqueness)Coauthors are distinct

IfM1,M2dontmatchthenM3,M4lesslikelytomatchIf institutions dont matchCoauthorsaredistinct

IfM1,M2dontmatchthenM3,M4cannotmatch

Ifinstitutionsdon tmatch,thenauthorslesslikelytomatch

Iftwovenuesdontmatch,thentheirpapersdontmatch

ConstraintTypesHardConstraint SoftConstraint

PositiveEvidence IfM1,M2matchthenM3,M4mustmatchIf t t h th i

IfM1,M2matchthenM3,M4morelikelytomatchIf t t h thIftwopapersmatch,theirvenues

matchIftwovenuesmatch,thentheirpapersaremorelikelytomatch

NegativeEvidence MentionM1andM2mustrefertodistinctentities(Uniqueness)Coauthors are distinct

IfM1,M2dontmatchthenM3,M4lesslikelytomatchIf institutions dont matchCoauthorsaredistinct

IfM1,M2dontmatchthenM3,M4cannotmatch

Ifinstitutionsdon tmatch,thenauthorslesslikelytomatch

Iftwovenuesdontmatch,thentheirpapersdontmatch

ConstraintTypesHardConstraint SoftConstraint

PositiveEvidence IfM1,M2matchthenM3,M4mustmatchIf t t h th i

IfM1,M2matchthenM3,M4morelikelytomatchIf t t h th

Notethatsomeoftheconstraintsmayberelational

Iftwopapersmatch,theirvenuesmatch

Iftwovenuesmatch,thentheirpapersaremorelikelytomatch

yandrequirejoins

May be directionalNegativeEvidence MentionM1andM2mustreferto

distinctentities(Uniqueness)Coauthors are distinct

IfM1,M2dontmatchthenM3,M4lesslikelytomatchIf institutions dont match

Maybedirectionalorbidirectional

Constraints can be recursiveCoauthorsaredistinct

IfM1,M2dontmatchthenM3,M4cannotmatch

Ifinstitutionsdon tmatch,thenauthorslesslikelytomatch

Constraintscanberecursive,e.g.,iftwoauthorshavematchingcoauthors,thenh hIftwovenuesdontmatch,thentheir

papersdontmatchtheymatch

AdditionalConstraints AggregateConstraints[Chaudhuri etal.SIGMOD07]

count constraintscountconstraints EntityAcanlinktoatmostNBs

Authorshaveatmost5papersatanyconference

Oth t lik l Otheraggregateslikesum,averagemorecomplex

Again, these can be either hard or soft constraints,Again,thesecanbeeitherhardorsoftconstraints,providepositiveornegativeevidence

MatchDependencies

Whenmatchingdecisionsdependonothermatching decisions (in other words, matchingmatchingdecisions(inotherwords,matchingdecisionsarenotmadeindependently),werefer to the approach as collectiverefertotheapproachascollective

MatchExtent Global: Iftwopapersmatch,thentheirvenuesmatch

This constraint can be applied to all instances of venueThisconstraintcanbeappliedtoallinstancesofvenuementions

AlloccurrencesofSIGMODcanbematchedtoInternationalConference on Management of DataConferenceonManagementofData

Local:Iftwopapersmatch,thentheirauthorsmatch This constraint can only be applied locallyThisconstraintcanonlybeappliedlocally

DontwanttomatchalloccurrencesofJ.SmithwithJeffSmith,onlyinthecontextofthecurrentpaper

Ex.SemanticIntegrityConstraintsType Example

Aggregate C1=Noresearcher haspublishedmorethanfiveAAAIpapersinayear

Subsumption C2=IfacitationXfromDBLP matchesacitationYinahomepage,theneachauthormentionedinYmatchessomeauthormentionedinX

Neighborhood C3=IfauthorsXandYsharesimilarnamesandsomecoauthors,theylik l t t harelikelytomatch

Incompatible C4 =NoresearcherexistswhohaspublishedinbothHCIandnumericalanalysis

f i i h d h i il hLayout C5=Iftwomentionsinthesamedocumentsharesimilarnames,theyarelikelytomatch

Key/Uniqueness C6=MentionsinthePClistingofaconferenceisto differentresearchersresearchers

Ordering C7=Iftwo citationsmatch,thentheirauthorswillbematchedinorder

Individual C8=TheresearcherwiththenameMayssam Sariahasfewerthanfi ti i DBLP ( d t t d t)fivementionsinDBLP(newgraduatestudent)

[Shen,Li&Doan,AAAI05]

AlgorithmsforHandlingConstraints Recordlinkage propagationthroughexclusivity

Weightedkpartitematching

Deduplication propagationthroughtransitivityC l i l i Correlationclustering

Collective propagation through general constraintsCollective propagationthroughgeneralconstraints Similaritypropagation

Dependencygraphs,CollectiveRelationalClusteringP b bili ti h Probabilisticapproaches LDA,CRFs,MarkovLogicNetworks,ProbabilisticRelationalModels,

Hybridapproaches Dedupalog

PART 2 dALGORITHMSPART2dALGORITHMS

RECORD LINKAGERECORDLINKAGE

11assumption Matchingbetween(almost)deduplicated databases. Each record in one database matches at most one recordEachrecordinonedatabasematchesatmostonerecordinanotherdatabase.

Pairwise ERmaymatcharecordinonedatabasewithmorethanonerecordinseconddatabase

WeightedKPartiteMatching

WeightedEdges

WeightedEdges Edges Edges

Edgesbetweenpairsofrecordsfromdifferentdatabases Edgeweights

o Pairwise matchscoreL dd f hio Logoddsofmatching

WeightedKPartiteMatching

Findamatching(eachrecordmatchesatmostoneotherrecord)fromotherdatabase)thatmaximizethesumofweights.

GeneralproblemisNPhard(3Dmatching) Successive bipartite matching is typically used [G pta & Sara agi VLDB Successivebipartitematchingistypicallyused.[Gupta&Sarawagi,VLDB

09]

DEDUPLICATIONDEDUPLICATION

Deduplication =>Transitivity Oftenpairwise ERalgorithmoutputinconsistentresults

(x,y)Mpred ,(y,z)Mpred ,but(x,z)Mpred

Idea:Correctthisbyaddingadditionalmatchesusingtransitivelclosure

In certain cases this is a bad ideaIncertaincases,thisisabadidea. Graphsresultingfrompairwise ERhave

diameter>20[Rastogi et al Corr 12]

AddedbyTransitive[Rastogi etalCorr 12]

Needclusteringsolutionsthatdealwiththisproblemdirectlyby

TransitiveClosure

reasoningaboutrecordsjointly.

ClusteringbasedER Resolutiondecisionsarenotmadeindependentlyforeachpairofrecords

Basedonvarietyofclusteringalgorithms,but Numberofclustersunknownaprioiri Many,manysmall(possiblysingleton)clusters

Oftentakeapairwisesimilaritygraphasinput

Mayrequiretheconstructionofaclusterrepresentativeorcanonicalentity

ClusteringMethodsforER HierarchicalClustering

[Bilenko etal,ICDM05][ , ]

NearestNeighborbasedmethods [Chaudhuri etal,ICDE05]

CorrelationClustering [SoonetalCL01,Bansal etalML04,NgetalACL02,

Ailon etalJACM08,Elsner etalACL08,Elsner etalILPNLP09]

IntegerLinearProgrammingviewofER rxy {0,1},rxy =1ifrecordsx andyareinthesamecluster. w+xy [0,1],costofclusteringxandytogetherxy [ ] g y g wxy [0,1],costofplacingxandyindifferentclusters

TransitiveTransitiveclosure

CorrelationClustering

Clustermentionssuchthattotal cost is minimi ed

42

totalcostisminimizedSolidedgescontributew+xy totheobjectiveDashededgescontributewxy totheobjective

3

1

y

Costbasedonpairwise similarities

5

Additive:w+xy =pxy andwxy =(1pxy) Logarithmic:w+xy =log(pxy)andwxy =log(1pxy)oga t c xy og(pxy) a d xy og( pxy)

CorrelationClustering SolvingtheILPisNPhard[Ailon etal2008JACM]

Anumberofheuristics[Elsner etal2009ILPNLP] Greedy BEST/FIRST/VOTE algorithmsGreedyBEST/FIRST/VOTEalgorithms GreedyPIVOTalgorithm(5approximation) LocalSearch

GreedyAlgorithmsS 1 P h d di dStep1:PermutethenodesaccordingarandomStep2:AssignrecordxtotheclusterthatmaximizesQuality

Start a new cluster if Quality < 0StartanewclusterifQuality0[Soon et al 2001 CL] [Soonetal2001CL]

VOTE:Assigntoclusterthatminimizesobjectivefunction. [Elsner etal08ACL]

PracticalNote: Runthealgorithmformanyrandompermutations,andpicktheclusteringwith

bestobjectivevalue(betterthanaveragerun)

Greedywithapproximationguarantees

PIVOTAlgorithm [Ailon etal2008JACM] Pickarandom(pivot)recordp.(p ) p Newcluster=

12

={1,2,3,4}C={{1,2,3,4}} ={2,4,1,3}C={{1,2},{4},{3}} ={3,2,4,1}C={{1,3},{2},{4}}

1

43

{ , , , } {{ , }, { }, { }}

Whenweightsare0/1, E(cost(greedy))

CANONICALIZATIONPART2dCANONICALIZATION

Canonicalization Mergeinformationfromduplicatementionstoconstructaclusterrepresentativewithmaximalinformationp

Starbucks Starbucks,

3457HillsboroughRoad Starbucks3457HillsboroughRoad,Durham,NCPh:(919)3334444

Durham,NCPh:null

Starbacks,

CriticallyimportantinWebportalswhereusersmust beshownaconsolidatedview

HillsboroughRd,DurhamPh:(919)3334444

Eachmentiononlycontainsasubsetoftheattributes

Mentionscontainvariations(ofnames,addresses)

Someofthementionshaveincorrectvalues

CanonicalizationAlgorithms Rulebased:

Fornames:typicallylongestnamesareused. Forsetvaluesattributes:UNIONisused.

F t i [C l tt t l KDD07] l dit di t f fi di Forstrings,[Culotta etalKDD07]learnaneditdistanceforfindingthemostrepresentativecentroid.

Canusemajorityruletofixerrors(if4outof5sayabusinessisclosed,thenbusinessisclosed). Thismaynotalwaysworkduetocopying[DongetalVLDB09],orwhen

underlyingdatachanges[PaletalWWW11]

CanonicalizationforEfficiency StanfordEntityResolutionFramework[Benjelloun VLDBJ09]

Considerablackbox matchandmergefunction Matchisapairwise boolean operator Merge:constructcanonicalversionofamatchingpair

Canminimizetimetocomputematchesbyinterleavingmatchingandmerging esp.,whenmatchandmergefunctions

satisfymonotonicity properties. r345

r12 r45

r1 r2 r3 r4 r5

COLLECTIVE ENTITY RESOLUTIONCOLLECTIVEENTITYRESOLUTION

CollectiveApproaches Decisionsforclustermembershipdependsonotherclusters

Nonprobabilisticapproachesp pp SimilarityPropagation

ProbabilisticModelsG i M d l GenerativeModels

UndirectedModels

HybridApproachesy pp

SIMILARITY PROPAGATIONSIMILARITYPROPAGATION

SimilarityPropagationApproaches Similaritypropagationalgorithmsdefineagraphwhichencodes

thesimilaritybetweenentitymentionsandmatchingdecisions,andcomputematchingdecisionsbypropagatingsimilarityvalues. Detailsofconstructedgraphandhowthesimilarityiscomputedvaries Algorithms are usually defined procedurallyAlgorithmsareusuallydefinedprocedurally Whileprobabilitiesmaybeencodedinvariouswaysinthealgorithms,there

isnoglobalprobabilisticmodeldefined

Approachesoftenmorescalablethanglobalprobabilisticmodels

DependencyGraph[Dong et al SIGMOD05 ]

Constructagraphwherenodesrepresentsimilaritycomparisonsbetween attribute values (realvalued) and match decisions based

[Dongetal.,SIGMOD05]

betweenattributevalues(real valued)andmatchdecisionsbasedonmatchingdecisionsofassociatednodes(booleanvalued)

Asmentionsareresolved,enrichedtocontainassociatednodesofallmatchedmentions

Similaritypropagateduntilfixedpointisreached

Negativeconstraints(notmatchnodes)arecheckedaftersimilaritypropagationisperformed,andinconsistenciesarefixed

ExploittheDependencyGraphSlid f [D t l SIGMOD05]

(Distributed,Distributed )(a1,a4)

Slidesfrom[Dongetal,SIGMOD05]

(169180,169180)(RobertS.Epstein,Epstein,R.S.)

1 4

(p1,p2)

(a2,a5)

(MichaelStonebraker,Stonebraker,M.)

(a3,a6)(v1,v2)

(EugeneWong,Wong,E.) (ACM,ACMSIGMOD) (1978,1978)

Referencesimilarity Attributesimilarity

ExploittheDependencyGraph

(Distributed,Distributed )(a1,a4)

(169180,169180)(RobertS.Epstein,Epstein,R.S.)

1 4

(p1,p2)

(a2,a5)

(MichaelStonebraker,Stonebraker,M.)

(a3,a6)(v1,v2)

(EugeneWong,Wong,E.) (ACM,ACMSIGMOD) (1978,1978)

Reconciled Similar

CollectiveRelationalClustering[Bh h & G TKDD07]

Constructagraphwhereleafnodesareindividuali

[Bhattacharya&Getoor,TKDD07]

mentions Performhierarchicalagglomerativeclusteringtomerge

l t f ticlustersofmentions Similaritycomputedbasedonacombinationofattributeand relational similarityandrelationalsimilarity

Whenclustersaremerged,updatethesimilaritiesofanyrelated clusters (clusters corresponding to mentionsrelatedclusters(clusterscorrespondingtomentionswhichcooccurwithmergedmentions)

ObjectiveFunctionMi i i Minimize:

),(),( jiRRjiAA ccsimwccsimw weightfor

bweightfor

lsimilarityof

bSimilaritybasedonrelationaledges

b d

jji j

Greedy clustering algorithm: merge cluster pair with max reduction in objective function

attributes relationsattributes betweenci andcj

)()( ** ccsimccsim where for example for cluster representative c*),(),( jAttributesa

ijiA ccsimccsim

))()(()( cNcNsimccsimand

for cluster representative c*

))(),((),( jijaccardjiR cNcNsimccsim where N(c) are the relational neighbors of c

RelationalClusteringAlgorithm1. Findsimilarreferencesusingblocking2. Bootstrapclustersusingattributesandrelations3. Computesimilaritiesforclusterpairsandinsertintopriorityqueue

4. Repeatuntilpriorityqueueisemptyp p y q p y5. Findclosestclusterpair6. Stopifsimilaritybelowthreshold7 If no negative constraints violated7. Ifnonegativeconstraintsviolated8. Mergetocreatenewcluster9. Constructcanonicalclusterrepresentative10. Updatesimilarityforrelatedclusters

O(nklogn)algorithmw/efficientimplementation

SimilaritypropagationApproachesMethod Notes Constraints Evaluation

RelDC[Kalashnikovetl TODS06]

Referencedisambiguationi i

Modelchoicenodesidentifiedi f

Contextattraction

h

Accuracyandruntime forAuthor

l i dal,TODS06] usingusingRelationshipbaseddatacleaning(RelDC)

usingfeaturebasedsimilarity

measurestherelationalsimilarity

resolutionanddirectorresolutioninMovie database

g ( )

ReferenceReconciliation[Dongetal,

DependencyGraphforpropagating

ReferenceenrichmentExplicitly handle

Both positiveandnegativeconstraints

Precision/Recall,F1onpersonalinformation

SIGMOD05] similarities+enforcenonmatchconstraints

missingvaluesParameterssetbyhand

managementdata(PIM),Coradataset

constraints

CollectiveRelationalClustering

Modifiedhierarchicalagglomerative

Constructscanonical entityasmergesare

Focusoncoauthorresolution and

Precision/Recall,F1onthreebibliographicg

[Bhattacharya&Getoor,TKDD07]

ggclusteringapproach

gmade propagation

g pdatasets:CiteSeer,ArXiv,andBioBase,andsyntheticdata

PROBABILISTIC MODELS:PROBABILISTICMODELS:GENERATIVEAPPROACHES

GenerativeProbabilisticApproaches ProbabilisticsemanticsbasedonDirectedModels

Model dependencies between match decisions in a generativeModeldependenciesbetweenmatchdecisionsinagenerativemanner

Disadvantage:acyclicity requirement Varietyofapproaches

BasedonLatentDirichlet Allocation,BayesianNetworks Examples

LatentDirichlet Allocation[Bhattacharya&Getoor,SDM07] ProbabilisticRelationalModels[Pasula etal,NIPS02]

LDAforEntityResolution:DiscoveringGroups from CoOccurrence RelationsGroupsfromCo OccurrenceRelations

Stephen C JohnsonStephen P Johnson

Alfred V Aho

J ff D Ull

Ravi Sethi

M k C ss

Chris Walshaw Kevin McManus

M tin E tt

Bell Labs Group

Jeffrey D Ullman

Parallel Processing Research Group

Mark Cross Martin Everett

P1: C. Walshaw, M. Cross, M. G. Everett, P4: Alfred V. Aho, Stephen C. Johnson, J ff D UllS. Johnson

P2: C. Walshaw, M. Cross, M. G. Everett,S. Johnson, K. McManus

Jefferey D. Ullman

P5: A. Aho, S. Johnson, J. Ullman

P3: C. Walshaw, M. Cross, M. G. Everett P6: A. Aho, R. Sethi, J. Ullman

LDAERModel Entity label a and group label z for

each reference r : mixture of groups for each co-

: mixture of groups for each co-

occurrence z: multinomial for choosing entity a

for each group z

z Va: multinomial for choosing

reference r from entity a Dirichlet priors with and

aT

r

T

AV

InferenceusingblockedGibbssamplingforefficiency(andimprovedaccuracy)

PR A

GenerativeApproachesMethod Learning/Inference

MethodEvaluation

[Li Morie & Generative Truncated EM to learn F1 on person[Li,Morie,&Roth,AAAI04]

Generativemodelformentionsindocuments

Truncated EMtolearnparametersandMAPinferenceforentities(unsupervised)

F1on personnames,locationsandorganizationsinTRECdataset

ProbabilisticRelationalM d l [P l

ProbabilisticRelationalM d l

Parameterslearnedonseparatedcorpora,i f d i

%ofcorrectlyidentifiedlModels[Pasula

etal.,NIPS03]Models inferencedoneusing

MCMCclusters onsubsetsofCiteSeer data

Latent Dirichlet Latent Dirichlet Blocked Gibbs Precision/RecallLatentDirichletAllocation[Bhattacharya&Getoor,

LatentDirichletAllocationModel

BlockedGibbsSamplingUnsupervisedapproach

Precision/Recall/F1onCiteSeerandHEPdata

SDM06]

PROBABILISTIC MODELS:PROBABILISTICMODELS:UNDIRECTEDAPPROACHES

UndirectedProbabilisticApproaches ProbabilisticsemanticsbasedonMarkovNetworks

Advantage: no acyclicity requirementsAdvantage:noacyclicity requirements Insomecases,syntaxbasedonfirstorderlogic

Advantage:declarativeg

ExamplesExamples ConditionalRandomFields(CRFs)[McCallum&Wellner,NIPS04]

MarkovLogicNetworks(MLNs)[Singla &Domingos,ICDM06] ProbabilisticSimilarityLogic[Broecheler &Getoor,UAI10]

MarkovLogic AlogicalKBisasetofhardconstraints onthesetofpossibleworlds

Makethemsoftconstraints;whenaworldviolatesaformula,itbecomeslessprobablebutnotimpossible

Giveeachformulaaweight Higher weight Stronger constraintHigherweight Strongerconstraint

isfies s it satf formulaweights oP(world) exp

[Richardson&Domingos,06]

MarkovLogic AMarkovLogicNetwork(MLN) isasetofpairs(F,w)where F isaformulainfirstorderlogic w isarealnumber

# true groundings of ith clause

Fi

ii xnwZXP )(exp1)( Fi

Iterate over all first-order MLN formulasNormalization Constant

[Richardson&Domingos,06]

ERProblemFormulationinMLNs Given

A DB of records representing mentions of entities in the real ADBofrecordsrepresentingmentionsofentitiesintherealworld,e.g.papermentions

A set of fields e g author title venueAsetoffieldse.g.author,title,venue

Eachrecordrepresentedasasetoftypedpredicatese.g.HasAuthor(paper,author),HasVenue(paper,venue)(p p , ), (p p , )

Goald h h f h d /f ld f h Todeterminewhichoftherecords/fieldsrefertothesame

underlyingentity

Slidesfrom[Singla &Domingos,ICDM06]

HandlingEquality IntroduceEquals(x,y) orx=y

I t d th i f lit Introducetheaxiomsofequality Reflexivity: x=x

Symmetry: x=y y=x Transitivity: x=y y=z z=x PredicateEquivalence:

x1 = x2 y1 y2 (R(x1, y1) R(x2,y2))x1 x2 y1 y2 (R(x1,y1) R(x2,y2))

Positive,SoftEvidence Introducereversepredicateequivalence

S l ti ith th tit i id b t Samerelationwiththesameentitygivesevidenceabouttwoentitiesbeingsame

R(x1,y1) R(x2,y2) x1 =x2 y2 =y2 Nottruelogically,butgivesusefulinformation

ExampleHasAuthor(C1 J Cox) HasAuthor(C2 Cox J ) C1 = C2HasAuthor(C1,J.Cox) HasAuthor(C2,CoxJ.) C1=C2(J.Cox=CoxJ.)

FieldComparison Eachfieldisastringcomposedoftokens

Introduce HasWord(field word)IntroduceHasWord(field,word)

Usereversepredicateequivalence

HasWord(f1,w1) HasWord(f2,w2) w1=w2 f1=f2 Example

HasWord(J.Cox,Cox) HasWord(CoxJ.,Cox) (Cox=Cox)(J.Cox=CoxJ.)

h d ff h f h d Canhavedifferentweightforeachword

TwolevelSimilarity Individualwordsasunits:Cantdealwithspellingmistakes

Breakeachwordintongrams:IntroduceHasNgram(word,ngram)

Usereversepredicateequivalenceforwordcomparisons

RecordMatching SimplestVersion:Fieldsimilaritiesmeasuredbypresence/absenceofwordsincommon

HasWord(f1,w1) HasWord(f2,w2) HasField(r1,f1)HasField(r2,f2) w1=w2 r1=r2

E l ExampleHasWord(J.Cox,Cox) HasWord(CoxJ.,Cox) HasAuthor(P1,J.Cox) HasAuthor(P2,CoxJ.) (Cox=Cox) (P1=P2)) ( , ) ( ) ( )

Transitivity(f1 = f2) (f2 = f3) ( f3 = f1)(f1 f2) (f2 f3) ( f3 f1)

AdditionalConstraintsHasAuthor(c a ) HasAuthor(c a ) Coauthor(a a )HasAuthor(c,a1) HasAuthor(c,a2) Coauthor(a1,a2)Coauthor(a1,a2) Coauthor(a3,a4) a1=a3 a2=a4

Inference Usecheapheuristics(e.g.TFIDFbasedsimilarity)toidentifyplausiblepairsy p p

Inference/learningoverplausiblepairs

Inferencemethod:lazygrounding+MaxWalkSAT

Learning:supervisedandtransfer(learn/handsetononeg p ( /domainandtransferred)

ProbabilisticSoftLogic[Broecheler & Getoor UAI10]

DeclarativelanguagefordefiningconstrainedcontinuousMarkov random field (CCMRF) using first order logic

[Broecheler &Getoor,UAI10]

Markovrandomfield(CCMRF)usingfirstorderlogic(FOL)

Soft logic: truth values in [0 1] Softlogic:truthvaluesin[0,1] LogicaloperatorsrelaxedusingLukasiewicz tnorms Mechanisms for incorporating similarity functions and Mechanismsforincorporatingsimilarityfunctions,andreasoningaboutsets

MAP inference is a convex optimization MAPinferenceisaconvexoptimization Efficientsamplingmethodformarginalinference

FOLtoCCMRF

PSLconvertsaweightedruleintopotentialfunctionsbypenalizing its distance to satisfactionpenalizingitsdistancetosatisfaction,

isthetruthvalueofgroundruleunderinterpretationx

Thedistributionovertruthvaluesis

: weight of rule r

:weightofruler :allgroundingsofruler

:PSLprogramp g

UndirectedApproachesMethod Learning/Inference

MethodEvaluation

[McCallum & Conditional Graph partitioning F1 on DARPA[McCallum&Wellner,NIPS04]

ConditionalRandomFields(CRFs)capturing

Graphpartitioning(Boykov etal.1999),performedviacorrelationclustering

F1on DARPAMUC&ACEdatasets

transitivityconstraints

[Singla &D i

MarkovLogicN k

Supervisedlearningd i f i

ConditionalLoglik lih d dDomingos,

ICDM06]Networks(MLNs)

andinferenceusingMaxWalkSAT &MCMC

likelihood andAUConCoraandBibServdata

[Broecheler &Getoor,UAI10]

ProbabilisticSimilarityLogic

Supervisedlearningandinferenceusing

Precision/Recall/F1Ontology

(PSL) continuousoptimization

Alignment

HYBRID APPROACHESHYBRIDAPPROACHES

HybridApproaches Constraintbasedapproachesexplicitlyencoderelationalconstraints Theycanbeformulatedashybridofconstraintsandprobabilisticmodels

Orasconstraintoptimizationproblem Examples

ConstraintbasedEntityMatching[Shen,Li&Doan,AAAI05] Dedupalog [Arasu,Re,Suciu,ICDE09]

Dedupalog [Arasu etal.,ICDE09]

PaperRef(id, title, conference, publisher, year)Wrote(id authorName Position)

DatatobededuplicatedDatatobe

deduplicatedWrote(id, authorName, Position) deduplicateddeduplicated

TitleSimilar(title1,title2)A th Si il ( th 1 th 2)

(Thresholded)FuzzyJ i O t t

(Thresholded)FuzzyJ i O t tAuthorSimilar(author1,author2) JoinOutputJoinOutput

Step(0)Createinitialapproximatematches;thisisinputtoDedupalog.

Step(1)Declaretheentities ClusterPapers,Publishers,&Authors

Paper!(id) :- PaperRef(id,-,-,-)Publisher!(p) :- PaperRef(-,-,-,p,-)Author!(a) :- Wrote(-,a,-) P bli h (UNA) d P (NOT UNA)

Dedupalog isflexible:UniqueNamesAssumption(UNA)

Dedupalog isflexible:UniqueNamesAssumption(UNA)

( ) ( , , ) Publishers(UNA)andPapers(NOTUNA)

Slidesbasedon[Arasu,Re,Suciu,ICDE09]

Step(2)DeclareClusters

PaperRef(id, title, conference, publisher, year)Wrote(id authorName Position)

InputintheDB

Clusterpapers,publishers and authorsWrote(id, authorName, Position)

TitleSimilar(title1,title2)A th Si il ( th 1 th 2)

Paper!(id) :- PaperRef(id,-,-,-)Publisher!(p) :- PaperRef(- - - p -)

publishers,andauthors

AuthorSimilar(author1,author2) Publisher!(p) :- PaperRef(-,-,-,p,-)Author!(a) :- Wrote(-,a,-)

Author*(a1,a2) AuthorSimilar(a1,a2)

Clustersaredeclared using*(likeIDBsorViews):TheseareoutputClustersaredeclared using*(likeIDBsorViews):Theseareoutput

Author1 Author2

AA Arvind Arasu

*IDBsareequivalencerelations:A D d l i

ClusterauthorswithsimilarnamesAA Arvind Arasu

Arvind A Arvind Arasu

qSymmetric,Reflexive,&TransitivelyClosedRelations:i.e.,Clusters

ADedupalog program isasetofdataloglikerules

152

SimpleConstraints

Paperswithsimilartitlesshouldlikelybeclusteredtogether

Paper*(id1,id2) PaperRef(id1,t1,-), PaperRef(id2,t2,-),TitleSimilar(t1,t2)

Author*(a1,a2) AuthorSimilar(a1,a2) ()Softconstraints:( )Payacostifviolated.

Paper*(id1 id2)

Additional ConstraintsAdditionalConstraintsClusteringtwopapers,thenmustclustertheirfirstauthors

Author*(a1,a2)

Dedupalog viaCC

Semantics:TranslateaDedupalog Programtoasetofgraphs

Nodesarereferences(inthe!Relation) EntityReferences:Conference!(c)

VLDBJConference*(c1,c2) ConfSim(c1,c2)

Positiveedges

Negative edges are implicit[ ]

VLDBconfVLDB

Negativeedgesareimplicit[] ICDE

InternationalConf.DEICDT

Forasinglegraphw.o.hardconstraintswe can reuse prior work for O(1) apx

156

wecanreusepriorworkforO(1)apx.

CorrelationClustering

Conference*(c1 c2)

Voting

Extendalgorithmtowhole languageviavotingtechnique.Support different entity types recursive programs etc

Thm: ArecursivehardThm: ArecursivehardManydedupalog programsManydedupalog programs

Supportdifferententitytypes,recursiveprograms,etc.

constraintsnoO(1)apxconstraintsnoO(1)apxy p g p g

haveanO(1)apxy p g p g

haveanO(1)apx

Thm:AllsoftprogramsO(1) Expert:multiwaycuthard

Systemproperties:(1)StreamingalgorithmSystemproperties:(1)Streamingalgorithm(2)linearin#ofmatches(notn2)(3)Userinteraction(2)linearin#ofmatches(notn2)(3)Userinteraction

Features:Supportforweights,referencetables(partially),andcorrespondinghardnessresults.Features:Supportforweights,referencetables(partially),andcorrespondinghardnessresults.

HybridApproachesMethod Evaluation

ConstraintbasedEntity

Twolayermodel:Layer1:Generativemodelfordatasetsthatsatisfy

ResearchersandIMDBwith

Matching[Shen,Li&Doan,AAAI05];

constraints;Layer2:EMalgorithmandtherelaxationlabelingalgorithmtoperformmatching.Ineachiteration,useEM to estimate parameters of the generative model

noiseadded

AAAI05];buildson(Li,Morie,&Roth,AIMag 2004)

EMtoestimateparametersofthegenerativemodelandamatchingassignment,thenemploysrelaxationlabelingtoexploittheconstraints

Dedupalog[Arasu,Re,Suciu,ICDE09]

Declarativespecificationforrichcollectionofconstraintswithnicesyntactic sugaraddedtodatalogforER.Inference:Correlationclustering+voting

Precision/RecallonCora,subsetofACMdataset

Summary:CollectiveApproaches Decisionsforclustermembershipdependsonotherclusters

Similaritypropagationapproachesy p p g pp ProbabilisticModels

GenerativeModelsU di d M d l UndirectedModels

HybridApproaches

Nonprobabilisticapproachesoftenscalebetterthangenerativeprobabilisticapproaches

Undirected/constraintbasedmodelsareofteneasiertospecify Scalingundirectedmodelsactiveareaofresearch

PART 3SCALING ER TO BIGDATAPART3SCALINGERTOBIG DATA

ScalingERtoBigData Blocking/CanopyGeneration Distributed ERDistributedER

PART 3BLOCKING/CANOPY GENERATIONPART3aBLOCKING/CANOPYGENERATION

Blocking:Motivation Navepairwise:|R|2 pairwise comparisons

1000 business listings each from 1,000 different cities across1000businesslistingseachfrom1,000differentcitiesacrosstheworld

1trillioncomparisons 11.6days (ifeachcomparisonis1s)

Mentionsfromdifferentcitiesareunlikelytobematches BlockingCriterion:City 1billioncomparisons 16minutes(ifeachcomparisonis1s)

Blocking:Motivation Mentionsfromdifferentcitiesareunlikelytobematches

May miss potential matchesMaymisspotentialmatches

Blocking:MotivationMatchingPairsofRecords

PairsofRecordssatisfying

Blockingcriterion

SetofallPairsofRecords

BlockingAlgorithms1 Hashbasedblocking

EachblockCi isassociatedwithahashkeyhi. Mentionx ishashedtoCi ifhash(x)=hi. Withinablock,allpairsarecompared. Each hash function results in disjoint blocks.Eachhashfunctionresultsindisjointblocks.

Whathashfunction? Deterministicfunctionofattributevalues BooleanFunctionsoverattributevalues

[Bilenko etalICDM06,MichelsonetalAAAI06,[ , ,DasSarma etalCIKM12]

minHash (minwiseindependentpermutations)[Broder etalSTOC98]

BlockingAlgorithms2 Pairwise Similarity/Neighborhoodbasedblocking

Nearby nodes according to a similarity metric are clusteredNearbynodesaccordingtoasimilaritymetricareclusteredtogether

Resultsinnondisjointcanopies.

Techniques SortedNeighborhoodApproach[HernandezetalSIGMOD95] CanopyClustering[McCallumetalKDD00]

SimpleBlocking:InvertedIndexonaKey

Examplesofblockingkeys: First three characters of last nameFirstthreecharactersoflastname City+State+Zip CharacterorTokenngrams Minimuminfrequentngrams

LearningOptimalBlockingFunctions Usingoneormoreblockingkeysmaybeinsufficient

2,376,206 Americans shared the surname Smith in the 2000 US2,376,206American ssharedthesurnameSmithinthe2000US NULLvaluesmaycreatelargeblocks.

Solution:Constructblockingfunctionsbycombiningsimplefunctions

ComplexBlockingFunctions Conjunctionoffunctions[MichelsonetalAAAI06,Bilenko etalICDM06]

{City}AND{lastfourdigitsofphone}

Chaintrees[DasSarma etalCIKM12]If ({Ci } NULL LA) h {l f di i f h } AND { d } If({City}=NULLorLA)then {lastfourdigitsofphone}AND{areacode}

else {lastfourdigitsofphone}AND{City}

BlkTrees [DasSarma etalCIKM12]

LearninganOptimalfunction[Bilenko etalICDM06] Findkblockingfunctionsthateliminatethemostnonmatches,whileretainingalmostallmatches., g Needatrainingsetofpositiveandnegativepairs

AlgorithmIdea:RedBlueSetCover

PositiveExamples

Blocking Keys

PickkBlockingkeyssuchthat(a)Atmost bluenodesarenotcovered

PickkBlockingkeyssuchthat(a)Atmost bluenodesarenotcovered

NegativeExamples

BlockingKeys(b)Numberofrednodescoveredisminimized(b)Numberofrednodescoveredisminimized

LearninganOptimalfunction[Bilenko etalICDM06] AlgorithmIdea:RedBlueSetCover

PositiveExamples Pick k Blocking keys such thatPick k Blocking keys such thatp

BlockingKeys

PickkBlockingkeyssuchthat(a)Atmost bluenodesarenotcovered(b)Numberofrednodes

PickkBlockingkeyssuchthat(a)Atmost bluenodesarenotcovered(b)Numberofrednodes

Greedy Algorithm

NegativeExamplescoveredisminimizedcoveredisminimized

GreedyAlgorithm: Constructgoodconjunctionsofblockingkeys{p1,p2,}. Pick k conjunctions {p p p } such that the following is Pickkconjunctions{pi1,pi2,,pik},suchthatthefollowingisminimized

minHash (Minwise IndependentPermutations) LetFx beasetoffeaturesformentionx

(functions of) attribute values(functionsof)attributevalues characterngrams optimalblockingfunctions

Let bearandompermutationoffeaturesinFx E.g.,orderimposedbyarandomhashfunction

minHash(x)=minimumelementinFx accordingto

WhyminHash works?Surprisingproperty:Forarandompermutation,

How to build a blocking scheme such that only pairs withHowtobuildablockingschemesuchthatonlypairswithJacquardsimilarity>sfallinthesameblock(withhighprob)?

`Probabilitythat

(x,y)mentionsarebl k d t thblockedtogether

Similarity(x,y)

BlockingusingminHashes ComputeminHashes usingr*kpermutations(hashfunctions))

Band of rminHashesBandofrminHashes

Signatures that match on 1 out of k bands go to thek blocks

Signature sthatmatchon1outofk bands,gotothesameblock.

minHash AnalysisFalseNegatives:(missingmatches)P(pair x y not in the same block

Sim(s) P(notsameblock)P(pairx,y notinthesameblock

withJacquardsim =s)block)

0.9 108

0.8 0.00035should be very low for high similarity pairs

False Positives: (blocking nonmatches)

0.7 0.025

0.6 0.2

shouldbeverylowforhighsimilaritypairs

FalsePositives:(blockingnonmatches)P(pairx,y inthesameblock

with Jacquard sim = s)

0.5 0.52

0.4 0.81withJacquardsim s)0.3 0.95

0.2 0.994

0.1 0.9998

CanopyClustering[McCallumetalKDD00]Input:MentionsM,

d(x,y),adistancemetric, Inmultiplecanopies

thresholdsT1 >T2Algorithm:1 Pi k d l t f M

canopies

1. PickarandomelementxfromM2. CreatenewcanopyCx using

mentionsys.t.d(x,y)

PART 3 bDISTRIBUTED ERPART3bDISTRIBUTEDER

DistributedERM d i l f l k Mapreduceisverypopularforlargetasks Simpleprogrammingmodelformassivelydistributeddata

Hadoop providesfaulttoleranceandisopensource

MapPhase(perrecordcomputation)

ReducePhase(globalcomputation)

Shuffle

ERwithDisjointBlocking

Map Phase Reduce PhaseComputeBlocksinMap RemainingERinReduce

MapPhase(perrecordcomputation)

ReducePhase(globalcomputation)

Shuffle

BlockID Noneedtocomparerecordsacross

reducersreducers

NondisjointBlocking Howtoblock?

Hashbased: need an efficient technique to group records ifHash based:needanefficienttechniquetogrouprecordsiftheymatchonnoutofkblockingkeys[Vernica etalSIGMOD10]

Distancebased:canopyclusteringonmapreduce[Mahout] IterativeBlocking[Whang etalSIGMOD09]

Problem:Informationneededforarecordisinmultiplereducers.p

Informationneededforarecordisinmultiplereducers. Example 1:Example1:

Reducer1:amatcheswithb Reducer2:amatcheswithcN d t i t i d t tl l b Needtocommunicateinordertocorrectlyresolvea,b,c

Problem:Informationneededforarecordisinmultiplereducers.p

Example2:Dedup papersandauthorsId Author1 Author2 PaperA1 JohnSmith RichardJohnson IndicesandViewsA2 JSmith RJohnson SQLQueriesA Dr Smyth R Johnson Indices and ViewsA3 Dr.Smyth RJohnson IndicesandViews

Slideadaptedfrom[Rastogi etalVLDB11]talk

Problem:Informationneededforarecordisinmultiplereducers.p

Canopyclusteringresultsinnondisjointclusters[McCallumetalKDD00]

JohnSmithRichard

J.Smith JohnS.

J h J bRichardJohnson SmithRichard

SmithRichardM.Johnson

R.Smith

JohnJacob

CanopyCanopy CCCanopyf

Canopyf

Johnson

pyfor

Richard

pyfor

Richard

CanopyforSmithCanopyforSmith

forJohnforJohn

Slideadaptedfrom[Rastogi etalVLDB11]talk

Problem:Informationneededforarecordisinmultiplereducers.p

CoAuthor(A1,B1) CoAuthor(A2,B2)match(B1,B2)match(A1,A2)

CoAuthor rulegroundstothecorrelationmatch(RichardJohnson,RJohnson)=>match(J.Smith,JohnSmith)

JohnSmith

J.Smith JohnS.

SteveRichard Smith JohnJacobSteve

JohnsonR.Smith

CanopyCanopy CanopyCanopyR

Johnson

Johnson

Canopyfor

Johnson

Canopyfor

Johnson

CanopyforSmithCanopyforSmith

forJohnforJohn

Slideadaptedfrom[Rastogi etalVLDB11]talk

Problem:Informationneededforarecordisinmultiplereducers.p

Solution1:EfficientlyfindConnectedComponents[Rastogi etal2012,KangetalICDM2009]

C l i Cl i / C ll i ER i h+CorrelationClustering/CollectiveERineachcomponent

Solution2:CorrelationClustering/CollectiveERineachcanopyg / py+MessagePassing[Rastogi etalVLDB11]

Problem:Informationneededforarecordisinmultiplereducers.p

Solution1:EfficientlyfindConnectedComponents[Rastogi etal2012,KangetalICDM2009]

C l i Cl i / C ll i ER i h+CorrelationClustering/CollectiveERineachcomponent

Connectedcomponentscanbelargeinrelational/multientityER.p g / y

Solution2:CorrelationClustering/CollectiveERineachcanopy+MessagePassing[Rastogi etalVLDB11]

MessagePassing

Simple Message Passing (SMP)SimpleMessagePassing(SMP)1. RunentitymatcherM locallyineachcanopy2. IfM findsamatch(r1,r2)insomecanopy,passitas1 2

evidencetoallcanopies3. RerunMwithineachcanopyusingnewevidence

il h f d i h4. Repeatuntilnonewmatchesfoundineachcanopy

Runtime: O(k2 f(k) c)Runtime:O(k2 f(k)c) k:maximumsizeofacanopy f(k):TimetakenbyERoncanopyofsizek c:numberofcanopies

Slideadaptedfrom[Rastogi etalVLDB11]talk

FormalPropertiesforawellbehavedERmethod

Convergence:No.ofstepsno.ofmatches

Consistency:Outputindependentofthecanopyorder

Soundness:Eachoutputmatchisactuallyatruematch

y p p py

Completeness:Eachtruematchisalsoaoutputmatch

JohnSmith

J.Smith JohnS.

JohnJacobRichardSmith

RichardJohnson

SmithR.SmithRichardM.

Johnson

Slideadaptedfrom[Rastogi etalVLDB11]talk

Completeness

Papers2and3matchonlyifacanopyknows thatknowsthatmatch(a1,a2)match(b2,b3)

t h( 2 3)match(c2,c3)

Simplemessagepassingwillnotfindanymatches thus,nomessagesarepassed,noprogress

Solution:Maximalmessagepassing Sendamessageifthereisapotentialformatch

Slideadaptedfrom[Rastogi etalVLDB11]talk

SummaryofScalability O(|R|2)pairwise computationscanbeprohibitive.

Blockingeliminatescomparisonsonalargefractionofnonmatches.

EqualitybasedBlocking: Construct(oneormore)blockingkeysfromfeatures

Records not matching on any key are not compared Recordsnotmatchingonanykeyarenotcompared.

Neighbohood basedBlocking: Formoverlappingcanopiesofrecordsbasedonsimilarity. Onlycomparerecordswithinacluster.

Computingconnectedcomponents/MessagePassinginadditiontoblocking can help distribute ERblockingcanhelpdistributeER.

P t 4CHALLENGES AND FUTUREPart4CHALLENGESANDFUTUREDIRECTIONS

Challenges Sofar,wehaveviewedERasaonetimeprocessappliedtoentire

database;noneoftheseholdinrealworld. Temporal ERTemporalER

ERalgorithmsneedtoaccountforchangeinrealworld Reasoningaboutmultiplesources[Pal&M etal.WWW12] Modeltransitions[LietalVLDB11]

Reasoningaboutsourcequality Sourcesarenotindependent CopyingProblem[DongetalVLDB09]

QueryTimeER Howdoweselectivelydeterminethesmallestnumberofrecordstoresolve,so

wegetaccurateresultsforaparticularquery? Collective resolution for queries [Bhattacharya & Getoor JAIR07]Collectiveresolutionforqueries[Bhattacharya&GetoorJAIR07]

ER&Usergenerateddata Deduplicated entitiesinteractwithusersintherealworld

Userstag/associatephotos/reviewswithbusinessesonGoogle/Yahoo Whatshouldbedonetosupportinteractions?

OpenIssues ERisoftenpartofbiggerinferenceproblem

Pipelinedapproachesandjointapproachestoinformationextractionand graph identificationandgraphidentification

HowcanwecharacterizehowERerrorsaffectoverallqualityofresults?

ER Theory ERTheory Needbettersupportfortheorywhichcangiverelationallearning

bounds ER & Privacy ER&Privacy

ERenablesrecordreidentification HowdowedevelopatheoryofprivacypreservingER?ER B h k ERBenchmarks NeedforlargescalerealworldERdatasetswithgroundtruth Syntheticdatausefulforscalingbuthardtocapturerichcomplexities

f l ldofrealworld

Summary Growingomnipresenceofmassivelinkeddata,andtheneed

forcreatingknowledgebasesfromtextandunstructureddatamotivate a number of challenges in ERmotivateanumberofchallengesinER

EspeciallyinterestingchallengesandopportunitiesforERand/socialmedia/usergenerateddata

As data noise and knowledge grows greater needs &Asdata,noise,andknowledgegrows,greaterneeds&opportunitiesforintelligentreasoningaboutentityresolution

M th h ll Manyotherchallenges Largescaleidentitymanagement Understandingtheoreticalpotentials&limitsofER

THANK YOU!THANKYOU!

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