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Graph Exploration: Taking the User into the Loop
Davide Mottin,AnjaJentzsch,EmmanuelMüllerHasso Plattner Institute,Potsdam,Germany
2016/10/24CIKM2016,Indianapolis,US
CIKM 2016 TUTORIAL
Where we are
D. MOTTIN, A. JENTZSCH, E. MÜLLER
ExploratoryGraphAnalysis(35min)
RefinementofQueryResults(35min)
FocusedGraphMining(35min)
103
Background(5 min)Graphmodels,subgraphisomorphism,subgraphmining,graphclustering
RealWorld-UseCase(15min)LinkedDatagraphs
Challengesanddiscussion
CIKM 2016 TUTORIAL
The Web of Data
• 1,019datasets• 84+billionRDFtriples• 808+millionRDFlinksbetweendatasets
D. MOTTIN, A. JENTZSCH, E. MÜLLER 104
http://lod-cloud.net
CIKM 2016 TUTORIAL
Vocabularies on the Web of Data• TheWebofDataisheterogeneous⁃ Manyvocabulariesareinuse(576asofOctober2016)⁃ Manydifferentwaystorepresentthesameinformation
D. MOTTIN, A. JENTZSCH, E. MÜLLER 105
https://lov.okfn.org
CIKM 2016 TUTORIAL
RDF Data Model
D. MOTTIN, A. JENTZSCH, E. MÜLLER 106
ns:cikm2016 ns:Event
dbpedia:Indianapolis
ACMConferenceonInformationandKnowledgeManagement(CIKM2016)
rdf:type
rdfs:label
ns:location
CIKM 2016 TUTORIAL
RDF Data Model
D. MOTTIN, A. JENTZSCH, E. MÜLLER 107
ns:cikm2016 ns:Event
dbpedia:Indianapolis
ACMConferenceonInformationandKnowledgeManagement(CIKM2016)
rdf:type
rdfs:label
ns:location
820445
dbpedia:populationTotal
JosephH.Hogsett
dbpedia:leaderName
CIKM 2016 TUTORIAL
Linked Data exploration use cases
• Datasetexploration• Graphmining• Queryformulationandrefinement
! ButLinkedDataismessy
D. MOTTIN, A. JENTZSCH, E. MÜLLER 108
CIKM 2016 TUTORIAL
Linked Data graph exploration challenges
● Nestedgraphs � Makesreasoningdifficult
● Loosestructure � Thingshavedifferentpropertysets
● Incomplete � Missingpropertydefinitions
● Poorlyformatted � Propertytypesusedinconsistently
● Inconsistent � Multiplerepresentationsclaimoppositethings
D. MOTTIN, A. JENTZSCH, E. MÜLLER 109
CIKM 2016 TUTORIAL
Linked Data exploration systems timeline
D. MOTTIN, A. JENTZSCH, E. MÜLLER 110
CIKM 2016 TUTORIAL
DBpedia Mobile
D. MOTTIN, A. JENTZSCH, E. MÜLLER 111
C.BeckerandC.Bizer.DBpedia mobile:Alocation-enabledlinkeddatabrowser.LDOW2008.
• displaysWikipediadataonmap• aggregatesdifferentdatasources
CIKM 2016 TUTORIAL
RelFinder
D. MOTTIN, A. JENTZSCH, E. MÜLLER 112
Heim,P.,Hellmann,S.,Lehmann,J.,Lohmann,S.,andStegemann,T.RelFinder:RevealingRelationshipsinRDFKnowledgeBases.SAMT2009.
• visualizationofpathsbetweenany2entities• pathidentificationoninstancelevel
CIKM 2016 TUTORIAL
gFacet
D. MOTTIN, A. JENTZSCH, E. MÜLLER 113
P.Heim,T.Ertl,andJ.Ziegler.Facetgraphs:Complexsemanticqueryingmadeeasy.TheSemanticWeb:ResearchandApplications.Springer,2010.
• Schemaexploration
• combinesgraph-basedvisualizationand
facetedfilteringtechniques
CIKM 2016 TUTORIAL
graphVizdb
D. MOTTIN, A. JENTZSCH, E. MÜLLER 114
Bikakis,N.,Liagouris,J.,Krommyda,M.,Papastefanatos,G.andSellis,T.graphVizdb:Ascalableplatformforinteractivelargegraphvisualization.ICDE,2016
• Graphlayoutisindexedwithaspatialdatastructure,i.e.,anR-tree,andstoredinadatabase
• Inruntime,useroperationsaretranslatedintoefficientspatialoperations(i.e.,windowqueries)inthebackend
CIKM 2016 TUTORIAL
LODeX
D. MOTTIN, A. JENTZSCH, E. MÜLLER 115
Benedetti,F.,Bergamaschi,S.andPo,L.Lodex:Atoolforvisualqueryinglinkedopendata.ISWC,2015
• ExploreaLinkedDatasetusingaschemasummary
• Pickgraphicalelementsfromittocreateavisualquery
• Browsetheresults• Refinethequery
CIKM 2016 TUTORIAL
Aemoo
D. MOTTIN, A. JENTZSCH, E. MÜLLER 116
A.Musetti,A.G.Nuzzolese,F.Draicchio,V.Presutti,E.Blomqvist,A.Gangemi,andP.Ciancarini.Aemoo:Exploratorysearchbasedonknowledgepatternsoverthesemanticweb.SemanticWebChallenge,2012.
• ExploratorysearchsystembasedonEncyclopedicKnowledgePatterns
• EKPareknowledgepatternsthatdefinethetypicalclassesusedtodescribeentitiesofacertainclass
CIKM 2016 TUTORIAL
Linked Jazz
D. MOTTIN, A. JENTZSCH, E. MÜLLER 117
M.C.Pattuelli,M.Miller,L.Lange,S.Fitzell,andC.Li-Madeo.Craftinglinkedopendataforculturalheritage:Mappingandcuration toolsforthelinkedjazzproject.Code4LibJournal,2013.
• revealsthenetworkofthesocialandprofessionalrelationswithintheAmericanjazzcommunity
CIKM 2016 TUTORIAL
Semantic Wonder Cloud
D. MOTTIN, A. JENTZSCH, E. MÜLLER 118
http://sisinflab.poliba.it/semantic-wonder-cloud/index/
CIKM 2016 TUTORIAL
inWalk
D. MOTTIN, A. JENTZSCH, E. MÜLLER 119
Castano,S.,Ferrara,A.andMontanelli,S.inWalk:InteractiveandThematicWalksinsidetheWebofData.EDBT,2014
CIKM 2016 TUTORIAL
ProLOD++ Mining Graph Patterns on the Web of Data
D. MOTTIN, A. JENTZSCH, E. MÜLLER 120
Jentzsch,A.,Dullweber,C.,Troiano,P.,Naumann,F.ExploringLinkedDataGraphStructures.ISWC2015.
ProLOD++● WebframeworkforvariousdataprofilingandminingtasksonLinkedDatasets
● ExplorativeresearchonLinkedDatasetgraphstofind⁃ frequentgraphpatterns⁃ commongraphpatternsforclasses⁃ generalgraphmodelforLinkedDatasets
https://prolod.org
CIKM 2016 TUTORIAL
ProLOD++Graph pattern mining
D. MOTTIN, A. JENTZSCH, E. MÜLLER 121
Jentzsch,A.,Dullweber,C.,Troiano,P.,Naumann,F.ExploringLinkedDataGraphStructures.ISWC2015.
DefinitionofcoresetoffrequentgraphpatternsinLinkedDatasetsbasedonsatellitecomponentanalysis
CIKM 2016 TUTORIAL
ProLOD++Graph patterns
D. MOTTIN, A. JENTZSCH, E. MÜLLER 122
Jentzsch,A.,Dullweber,C.,Troiano,P.,Naumann,F.ExploringLinkedDataGraphStructures.ISWC2015.
● Groupclass-coloured graphsbytheirpermutationgroups[Luks82]⁃ Permutationgroup:thesetofallautomorphisms ofagraph
drugtargetunknown
CIKM 2016 TUTORIAL
Loupe
D. MOTTIN, A. JENTZSCH, E. MÜLLER 123
Mihindukulasooriya,N.,Poveda-Villalón,M.,García-Castro,R.and Gómez-Pérez,A.Loupe - An OnlineTool for InspectingDatasets inthe Linked DataCloud.ISWC2015.
• Frequenttriplepatterns• Graphicalontologybrowsing
CIKM 2016 TUTORIAL
Requirements for Linked Data exploratory search systems
D. MOTTIN, A. JENTZSCH, E. MÜLLER 124
• Thesystemprovidesefficientoverviews• Thesystemhelpstheusertounderstandtheinformationspaceandtoshapehismentalmodel
• Theusercanexploremultiple,heterogeneousresultsandbrowsingpaths
• Thesystemeasesthememorizationofrelevantresults
• Thesysteminspirestheuserandshapeshisinformationneed• Thesystemprovokesdiscoveries
CIKM 2016 TUTORIAL
Challenges
D. MOTTIN, A. JENTZSCH, E. MÜLLER 125
● Displayingthegraphforexploration⁃ E.g.byclusteringoftopicaldomains⁃ Allowingtheusertodrilldown
● Livegraphexploration⁃ E.g.viafederatedSPARQLqueries▪ RequiresknowledgeonendpointURIs▪ Slowinreal-time
● Guidingtheusertointerestingpartsofthegraph⁃ Usuallydonebyentityinlinks▪ Limitedinsights
CIKM 2016 TUTORIAL
Tutorial outline
D. MOTTIN, A. JENTZSCH, E. MÜLLER
ExploratoryGraphAnalysis(35min)
RefinementofQueryResults(35min)
FocusedGraphMining(35min)
126
Background(5 min)Graphmodels,subgraphisomorphism,subgraphmining,graphclustering
RealWorld-UseCase(15min)LinkedDatagraphs
Challengesanddiscussion
CIKM 2016 TUTORIAL
Summary of Exploratory Graph Analysis
D. MOTTIN, A. JENTZSCH, E. MÜLLER 127
ApproximateQueries• UserqueryisimpreciseBy-Examplemethods• Userqueryisanexampleresult
• Onlyneedapartialknowledgeonthedata
• Noneedforcomplicatequerylanguages(useexamples,partialdescriptions)
• Thequeryadaptstouserneed• Enableexploratorysearchby
usingsmallqueriesonthedata
Query(agraph)
Graph
?
Query(anexample)
Graph
CIKM 2016 TUTORIAL
Challenges for Exploratory Graph Analysis
D. MOTTIN, A. JENTZSCH, E. MÜLLER 128
• Unsupportedinmostofthecurrentgraphdatabases• No”universal”indextoanswermultipletypeofqueries• Partitioningonlyforexactqueryanswering
Database
• Userinteractivityintheexplorationprocess• Nosolutionsforprobabilisticgraphs• Respondtoquerieswhilethegraphchanges• Findexamplesinstreamingsettings
InformationretrievalInformation
retrieval
• Exploitingquerylogsforpersonalizedqueryanswering• Retrieveresultsinformofdocumentsconvertingthequery
structures
Datamining
CIKM 2016 TUTORIAL
Summary of Focused Graph Mining
D. MOTTIN, A. JENTZSCH, E. MÜLLER 129
Thefocusonindividualuserinterest…asQuery totheGraphMiningSystem…asSeedNode(s) forLocalSearch…asAttributes andWeights
• getorinferuserinterestà unexpectedresults
• interactiveexplorationà intuitiveparametrization
• adaptivegraphminingà individuallocalsearch
CIKM 2016 TUTORIAL
Challenges for Focused Graph Mining
D. MOTTIN, A. JENTZSCH, E. MÜLLER 130
Datamining
scale
Userinteractivityinthegraphminingprocess• unsupportedinmostofthecurrentgraphminingalgorithms• hugevarietyofuserinteractions possible• feedbackloopneedstobeunified andbecomeexchangeable
Revolutionofformalmodelsandsearchalgorithms• insufficientextensionsofexistingmodelsandalgorithms• adaptivesteering ofalgorithmsvs.fixedparametrization• evaluationofalgorithmswithuserstudies
Scalabilityofalgorithmsforreal-timeinteraction• NP-hardproblems,heuristicalgorithms,…,stillnotscalable• exploittheuserinterest forpruningthesearchspace
CIKM 2016 TUTORIAL
Summary of Refinement of Query Results
D. MOTTIN, A. JENTZSCH, E. MÜLLER 131
Refinement• Theuserqueryistoorestrictiveortoo
genericTop-kResults• QueriestypicallyhaveinexactmatchesSkylineQueries• Findsmallsetofinterestingitemswith
manydimensionsandincrementalupdates
• Theusermighthaveaverygenericideaofhowtodescribethestructureofinterest
• Thesystemguidestheusertowardstheanswerwithsimplesteps
• Theresultsareexplainedwithreformulations
• Thequerymatchesareinexactandinteresting
CIKM 2016 TUTORIAL
Challenges for Refinement of Query Results
D. MOTTIN, A. JENTZSCH, E. MÜLLER 132
• Realtimeperformance• Profilingofqueriesforoptimizedperformance
• Personalizedreformulationsandinteractivity• Facetsearchdiscoveryingraphs
• Uncertaingraphdata
Database
InformationretrievalInformation
retrieval
Datamining
CIKM 2016 TUTORIAL
The missing tiles in graph exploration
D. MOTTIN, A. JENTZSCH, E. MÜLLER 133
Interactivity Adaptivity
Personalization Scalability
Questions?
CIKM 2016 TUTORIAL D. MOTTIN, A. JENTZSCH, E. MÜLLER 134
Slides:https://hpi.de//mueller/tutorials/graph-exploration.html