360° semanc technologies: web mining, text analysis ... · jun 2010 360° semanc technologies: web...

Jun2010

360°Seman+cTechnologies:WebMining,TextAnalysis,LinkedData

SearchandReasoning

SemTech2010Workshop,SanFrancisco

Presenta+onOutline

•  SEMANTICDATAMANAGEMENT(1:10-1:50)–  OWLIMseman+crepository–  Linkeddata,FactForge–  LifeSciences,LinkedLifeData

•  SEMANTICANNOTATIONFORSEARCH(2:00-3:10)–  Textmining–  Seman+cannota+onindexing–  Searchshowcases

•  PARTNERPRESENTATIONS(3:20-4:20)–  BPEng-Businessprocessmanagement(Italy)–  StructureDynamics(Canada)–  TopQuadrant(USA)

•  WRAP-UP(4:20-4:30)

SemTech2010:360°SemanScTechnologies Jun2010 #2

Jun2010

Ontotext

•  Ontotextisaseman+ctechnologyprovider•  GloballeaderinsemanScsearchandsemanScdatabases

•  Establishedinyear2000–  PartofSirma,atop-3soYwarecompanyinBulgaria

•  Staff:50employeesandmulSplecontractors

•  InvestmentacquiredinJuly2008–  Afinancialinvestorobtainedminorityshareinadealfor2.5MEURO

•  Ontotextisinvolvedintwojointventures:–  Innovantage:onlinerecruitmentintelligenceproviderinUK–  Namerimi:naSonalsearchengineinBulgaria

#3SemTech2010:360°SemanScTechnologies

Jun2010

ResearchProjects

•  OntotextisthemostsuccessfulBulgariancompanyinFP6–  OntotexthasparScipatedin20+ECresearchprojects–  >100MEuroisthetotalbudgetoftheprojectsOntotextispartof–  Thisisabove10%oftheECprojectsrelatedtosemanScs

•  Partneringwiththeleadingresearchcentersandcompanies–  SAP,SoYwareAG,IBM,ATOSOrigin,CapGemini,Wikimedia,…

•  About3MEurofundingfromECprojectsfor2010-2012


Jun2010

OntotextPosi+oning

•  Leadingseman+ctechnologyprovider–  Top-10coretechnologyprovider–  OfferingenginesandcomponentstovendorsandsoluSondevelopers

•  Uniquetechnologyporholio:–  Seman+cDatabases:high-performanceRDFDBMS,scalablereasoning

–  Seman+cSearch:text-mining(IE),InformaSonRetrieval(IR)

–  WebMining:focusedcrawling,screenscrapping,datafusion

–  WebServicesandBPM:WSannotaSon,discovery,etc.

•  Goodrecogni+onintheSemTechcommunity–  Ontotextpagesareranked1stfor“semanScannotaSon”and“semanScrepository”atGoogleandYahoo


Webuilduponlightweightseman+csthatiseasytounderstand,deploy,andmanage

Forinstance,thinkofontologiesasdatabaseschematawithsimpleinterpretaSonrules.

Plentyofobvious(butuseful)implicitfactscanbeinferredandmatchqueriesrightaway

Jun2010 #6SemTech2010:360°SemanScTechnologies

Whatdowedo?


Itissimple

myData:Maria

ptop:Agent

ptop:Person

ptop:Woman

ptop:childOf

ptop:parentOf

rdfs:range

owl:inverseO

f

inferred

myData:Ivan

owl:rela+veOf

owl:inverseOf owl:SymmetricProperty

rdfs:subPropertyOf

owl:inverseOf

owl:inverseOf

rdf:type

rdf:type

rdf:type

rdf:type


Physicaldatarepresenta+on:RDFvs.RDBMS

Person

ID Name Gender

1 MariaP. F

2 IvanJr. M

3 …

Parent

ParID ChiID

1 2

…

Spouse

S1ID S2ID From To

1 3

…

Statement

Subject Predicate Object

myo:Person rdf:type rdfs:Class

myo:gender rdfs:type rdfs:Property

myo:parent rdfs:range myo:Person

myo:spouse rdfs:range myo:Person

myd:Maria rdf:type myo:Person

myd:Maria rdf:label “MariaP.”

myd:Maria myo:gender “F”

myd:Maria rdf:label “IvanJr.”

myd:Ivan myo:gender “M”

myd:Maria myo:parent Myd:Ivan

myd:Maria myo:spouse myd:John

…


Getmorefacts–Matchmorequeries

myData:Maria

ptop:Agent

ptop:Person

ptop:Woman

ptop:childOf

ptop:parentOf

rdfs:range

owl:inverseO

f

inferred

myData:Ivan

owl:rela+veOf


rdfs:subPropertyOf

owl:inverseOf

owl:inverseOf<C1,rdfs:subClassOf,C2> <C2,rdfs:subClassOf,C3> ⇒  <C1,rdfs:subClassOf,C3> <I,rdf:type,C1> <C1,rdfs:subClassOf,C2> ⇒  <I,rdf:type,C2> <P1,owl:inverseOf,P2> <I1,P1,I2> ⇒  <I2,P2,I1> <P1,rdf:type,owl:SymmetricProperty> ⇒  <P1,owl:inverseOf,P1>

rdf:type

rdf:type

rdf:type

rdf:type

The database will return Ivan as result of query for Maria relativeOf ?x when the fact asserted was Ivan childOf Maria


TheSeman+csisEncodedinSimpleRules

myData:Maria

ptop:Agent

ptop:Person

ptop:Woman

ptop:childOf

ptop:parentOf

rdfs:range

owl:inverseO

f

inferred

myData:Ivan

owl:rela+veOf


rdfs:subPropertyOf

owl:inverseOf

owl:inverseOf

rdf:type

rdf:type

rdf:type

rdf:type

<C1,rdfs:subClassOf,C2> <C2,rdfs:subClassOf,C3> ⇒  <C1,rdfs:subClassOf,C3> <I,rdf:type,C1> <C1,rdfs:subClassOf,C2> ⇒  <I,rdf:type,C2> <P1,owl:inverseOf,P2> <I1,P1,I2> ⇒  <I2,P2,I1> <P1,rdf:type,owl:SymmetricProperty> ⇒  <P1,owl:inverseOf,P1>

ScalableReasoningMap(Jun’09)


0

20

40

60

80

100

120

140

0 5 10 15 20

Load

ingS

peed

(100

0st./sec,higherisb

etter)

Datasetsize(bill.explicitstatements)BigOWLIM AllegroGraph Virtuoso JenaTDB BigData ORACLE

Bubblesizeindicatesloadingcomplexity(biggerisbetter)

sub-$10,0008-core server

sub-$20004-coredesktop

clusterof148-coreblades

sub-$10,0008-coreserver

InterlinkingTextandData


Welink,yourdata,yourcontent,andtheweb!

In10weekswecanbuildasoluSonwhich:-integrates10databaseswiththelinkeddatacloud-mines10milliondocumentsandwebpages

andletsyousearchandnavigateallthisinforma+on-in10differentways-froma$10,000server


ElevatorPitch

Presenta+onOutline




•  WRAP-UP(4:20-4:30)


Jun2010

•  OWLIMisafamilyofscalableseman+crepositories•  SwibOWLIM:in-memory,fastest,scalesto~100millionstatements

•  BigOWLIM:file-based,sameAs&queryopSmizaSons,scalesto20billionstatements

•  OWLIMprovides–  Management,integraSonandanalysisofheterogeneousdata

–  Combinedwithlight-weight,high-performancereasoning

–  Theinferenceisbasedonlogicalrule-entailment

–  FullRDFS,restrictedOWLLite,OWLHorstandOWL2RLaresupported

–  Customseman+cscanbedefinedviarulesandaxiomaSctriples

Semantic Repository for RDFS and OWL


Jun2010

Complexity*

Naïve OWL Fragments Map

DL Rules, LP


OWL Full

OWL DL

OWL Lite

OWL Horst / Tiny

OWL DLP

RDFS

SWRL

OWL/WSML Flight

Datalog

OWL Lite- / DHL

OWL 2 RL

Expressivity supported by OWLIM

Jun2010

•  BigOWLIMisusedfordata-integraSoninlifesciences

•  SwiYOWLIMisbundledasanontologyserviceinGATE4.0

•  OWLIMisusedasasemanScrepositoryinKIM

•  TopBraidComposerbundlesOWLIMasareasoner

•  OWLIMisusedinmorethan10Europeanresearchprojects

•  FactForge(hrp://fachorge.net)isbasedonBigOWLIM

•  BigOWLIMhasbeensuccessfullyintegratedintothehighperformanceSeman+cWebPublishingstackpoweringtheBBC’s2010WorldCupWebsite

OWLIM in Use


Jun2010

BigOWLIMSta+s+cs(basedonpublishedresults)

•  BigOWLIMistheonlyenginethatcanreasonwithmorethan10billionstatements

•  BigOWLIM’squeryperformanceisatleastasgoodasanyotherenginethancanhandlesemanScson1Billionstatements

•  BigOWLIMistheonlyengineforwhichfull-cycleloadingandqueryevaluaSonresultsarepublishedforLUBM(8000)

•  BigOWLIMsuccessfullypassesLUBM(90000)–over20billionexplicitandimplicitstatements


Jun2010

•  IntroducSontoOWLIM–  Versions,MajorFeatures,InformaSon–  DialectsofOWLandcombinaSonswithRDFSandRules

•  BigOWLIM–  Advancedfeatures

•  Hands-on–  TryoutsomefeaturesusingFactForge

Outline


BigOWLIMReplica+onCluster

•  DistribuSonthroughdatareplicaSonisusedto:–  Improvescalabilityofconcurrentuserrequests–  Resilience–failover,onlineconfiguraSon

•  Howdoesitwork?–  EveryuserwriterequestispushedinatransacSonqueue–  EachdatawriterequestismulSplexedtoallrepositoryinstances–  Eachreadrequestisdispatchedtooneinstanceonly–  Toensureload-balancing,each

readrequestsissenttotheinstancewithsmallestexecuSonqueueatthispointinSme


Jun2010

Replica+onCluster-Behaviour

•  Thetotalloading/modifica+onperformanceoftheclusterisequaltothatofoneinstance

•  ThedatascalabilityoftheclusterisdeterminedbytheamountofRAMoftheweakestinstance

•  Thequeryperformanceoftheclusterrepresentsthesumofthethroughputsthatcanbehandledbyeachoftheinstances

•  Failover:–  Incaseoffailureofoneormoreinstances,theperformance

degrada+onisgraceful–  Theclusterisfullyopera+onalevenwhenthereisonlyoneinstance

working

•  Clustercanbereconfiguredwhenrunning#21SemTech2010:360°SemanScTechnologies

Replica+onCluster-TypesofNodes

•  Twotypesofnodes•  Flexibletopologiespossible•  Resiliencetofailureofworkersandmasters

SemTech2010:360°SemanScTechnologies #22Jun2010

Worker 1 Worker 3

Master

Worker 2

Master (hot standby)

Dispatches queries and updates to workers (read/write)

Dispatches queries to workers (read only)

Standard BigOWLIM instances

Queries & updates

Queries only

Replica+onCluster-Performance

•  Performancebenchmarks–  Tobepublishedsoon–  IniSalindicaSonslookgood–  ConcurrentqueryevaluaSonappearstoscalelinearlywithno.of

nodes

•  AttheBBC–  Millionsofreadrequestsperday–  Thousandsofupdatesperhour


Jun2010

•  InpreviousversionsofBigOWLIM,wheneverastatementwasdeleted,theenSrededucSveclosurewasinvalidated–  Whichwastriggeringfullre-inference

•  AparSalinvalidaSonmechanismhasnowbeenimplemented–  Itperformsasequenceofbackwardandforward-chainingiteraSonstofigure

outwhatpartofthededucSveclosureisnolongersupported

–  ItdoesNOTrequireany‘truthmaintenanceinformaSon’

•  ThecomplexityoftheinvalidaSoniscomparabletothecomplexityoftheaddiSon(doesn’tworkforowl:sameAs)–  Itisslower,butitissSllinthesameorderofmagnitude

–  Removing‘key’statementssSllcanbepainful

SmoothInvalida+on


Jun2010

•  BigOWLIMincludesarouSnewhichallowsforefficientcalculaSonofamodificaSonofPageRankoverRDFgraphs

•  ThecomputaSonoftheRDFRanksforFactForge(400MLODstatements)takes310sec–  201secforreadingtheRDFgraphfromdisk-basedstructuresintospecificin-

memoryrepresentaSon

–  98secwerespentin27RDFRankiteraSons

•  Resultsareavailablethroughasystempredicate

•  Example:getthe100mostimportantnodesintheRDFgraphSELECT ?n {?n onto:hasRDFRank ?r}

ORDER BY DESC(?r) LIMIT 100

RDFRank


Jun2010

RDFPriming


•  ScalableandcustomizableimplementaSonof‘AcSvaSonSpreading’

•  Allows‘priming’oflargedatasetswithrespecttoconceptsrelevanttothecontextandtothequery

•  ControlledusingspecialASKqueriesthat–  IniSateprimingfromspecifiednodes,withdecayfactorsand

thresholds•  e.g.acSvatefromthisnode

•  Toreturnmorespecificresultsforthisquery

PREFIX onto: <http://www.ontotext.com#> PREFIX dbpedia3:<http://dbpedia.org/resource/> ASK { dbpedia3:1955_Ford onto:activateNode dbpedia3:Ford_Motor_Company }

SELECT * where {?x <http://dbpedia.org/property/class> http://dbpedia.org/resource/V8>.}

Jun2010

Full-TextSearch


•  AlternaSveinformaSonaccessmethod(differentindices)

•  FindinformaSonbasedonstringelements(tokens)

•  Twoapproaches:NodeSearchandRDFSearch–  URIs,literals–  Criteriaarealistoftokenswithapredicate(exact,ignorecase,prefix,…)

–  Resultavailableasavariablebinding

PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX onto: <http://www.ontotext.com/> SELECT ?x, ?label WHERE { ?x rdfs:label ?label . <3d:> onto:prefixMatchIgnoreCase ?label. }

Jun2010

Full-TextSearch(2)


•  AlternaSveinformaSonaccessmethod(differentindices)

•  FindinformaSonbasedonstringelements(tokens)

•  Twoapproaches:NodeSearchandRDFSearch–  TextrepresentaSonofanode’s‘RDFmolecule’

–  CriteriaareLucenetokenswithspecial‘lucene’predicate

–  ResultsareURIs(orderedbyRDFrankifavailable)

PREFIX gossip: <http://www.....gossipdb.owl#> PREFIX onto: <http://www.ontotext.com/> SELECT * WHERE { ?person gossip:name ?name . ?name onto:luceneQuery "American AND life~" . }

Jun2010

•  InconsistencycheckingisperformedwhenatransacSoniscommired

•  Therearetwokindsofchecks:IF<premises>=>CHECK<constraints>

•  Similartoentailmentrules,exceptthatwheneverthepremisesaresaSsfied,acheckismadethatthe‘inferred’triplesexistintherepository

IF<premises>=>Inconsistent!

•  WheneverthepremisesaresaSsfied,aconsistencyviolaSonislogged

ConsistencyChecks


Jun2010

No+fica+ons

•  Theclientcansubscribeforno+fica+onsforincomingstatementsmatchingdesiredgraphpanerns

•  Theparernsarethenusedtofilterincomingstatements–  NoSfythesubscriberaboutthosestatementsthathelpformanew

soluSonofatleastoneofthegraphparerns–  Inferredstatementsaretreatedinthesameway

•  Thesubscribershouldnotrelyonanypar+cularorderordis+nctnessofthestatementnoSficaSons–  RetracSonofstatementsarenotnoSfied


SampleFactForgeQueries1

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX opencyc: <http://sw.opencyc.org/2008/06/10/concept/en/> PREFIX dbp-ont: <http://dbpedia.org/ontology/> PREFIX owlim: <http://www.ontotext.com/> PREFIX geo-ont: <http://www.geonames.org/ontology#> PREFIX dbpedia: <http://dbpedia.org/resource/> SELECT * WHERE { ?Person dbp-ont:birthPlace ?BirthPlace ; rdf:type opencyc:Entertainer ; owlim:hasPageRank ?RR .

?BirthPlace geo-ont:parentFeature dbpedia:Germany . } ORDER BY DESC(?RR) LIMIT 100

•  WhoarethemostimportantGermanentertainers•  ThisqueryinvolvesdatafromDBPedia,Geonames,andUMBEL(OpenCyc)•  Itinvolvesinferenceovertypes,sub-classes,andtransiSverelaSonships•  Rankingtheresultsby‘importance’–RDFrank

#31SemTech2010:360°SemanScTechnologies Jun2010

SampleFactForgeQueries2

PREFIX onto: <http://www.ontotext.com/> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> SELECT * WHERE { ?entity <http://xmlns.com/foaf/0.1/topic> ?topic . ?entity rdfs:label ?label . ?topic onto:luceneQuery "American AND life~" . }

•  GetthedescripSonsofenSSesthathaveaFOAFtopiccontaining‘American’andsomethingthatissimilarto‘Life’


Jun2010

OWLIM

hnp://www.ontotext.com/owlim

BasedonpublishedresultsandindependentevaluaSons:

OWLIMisthemostscalableandthemostefficient

seman+crepositoryintheworldandoffers

themostcomprehensivereasoningsupport


Presenta+onOutline




•  WRAP-UP(4:20-4:30)


Jun2010

LinkingDataAcrossDifferentServers


myData:Maria

ptop:Agent

ptop:Person

ptop:Woman

ptop:childOf

ptop:parentOf

rdfs:range

owl:inverseO

f

inferred

myData:Ivan

owl:rela+veOf


rdfs:subPropertyOf

owl:inverseOf

owl:inverseOf

rdf:type

rdf:type

rdf:type

rdf:type

Jun2010

LinkingOpenData

•  LinkingOpenData(LOD)W3CSWEOCommunityprojecthrp://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData

•  IniSaSveforpublishing“linkeddata”whichalreadyincludes50+interlinkeddatasetsandmorethan10billionfacts


Presenta+onOutline

•  LinkedData–  IntroducSontolinkeddataandLOD–  WhyPeopleDoNotUseLinkedData?

•  Reason-ableViews•  FactForge:gatewaytothecenterofthewebofdata

–  Contents:thelargestbodyofcommon-senseknowledge–  RDFSearch:probablythebestwaytoexploreunknowndatasets–  LoadingandInferenceStaSsScs–  QueryingmulSpledatasets,consideringtheirsemanScs

•  Or:WhoisthemostpopularGermanentertainer?

–  TheModiglianitestfortheSemanScWeb


WhyPeopleDoNotUseLinkedData?

•  PlentyofpeopleintheITworldhaveheardaboutlinkeddataandliketheidea

•  However,theimpactoflinkeddataintheenterprisesissSllverylimited

•  Because:–  Therearenowellestablishedopinionswhatlinkeddatacan“buy”fortheenterpriseandbestpracScesofusingit• Whataretheconcretebenefits?

–  Itisnotclearwhatitwouldcost• Whataretheproblems?• Whataretheassociatedrisks?


LinkedDataintheEnterprise:Why?

•  Tofacilitatedataintegra+on–  OnecanuseLODas“interlingua”forenterprisedataintegraSon–  AddiSonalpublicinformaSoncanhelpalignmentandlinking

•  Toaddvaluetoproprietarydata–  PublicdatacanallowmoreanalyScsontopofproprietarydata–  Forinstance,bylinkingtospaSaldatafromGeonames–  Benerdescrip+onandaccesstocontent,e.g.searchforimages

•  Makeenterprisedatamoreopen–  Tomaketheenterprisedataeasiertouseoutsidetheenterprise–  PublicidenSfiersandvocabulariescanbeusedtoaccessthem


LinkedDataintheEnterprise:Challenges

•  LODishardtocomprehend–  Diversitycomesataprice–  Oneneedstomakeaqueryagainst200differentschemataand

hundredsofthousandsofclassesandproperSes

•  LODisunreliable–  MostoftheserversbehindLODtodayareslow

•  HighdownSme–  Dealingwithdatadistributedonthewebisslow

•  AfederatedSPARQLquerythatuses2-3serverswithinseveraljoinscanbe*very*slow

–  Nosortofconsistencyisguaranteed•  LowcommitmenttotheformalsemanScsandintendedusageoftheontologiesandschemata


Presenta+onOutline







Reason-ableViewstotheWebofData

•  Reason-ableviewsrepresentanapproachforreasoningandmanagementoflinkeddata

•  Keyideas:–  Groupselecteddatasetsandontologiesinacompounddataset

•  Cleanup,post-processandenrichthedatasetsifnecessary•  DothisconservaSvely,inaclearlydocumentedandautomatedmanner,sothat:

–  theoperaSoncaneasilybeperformedeachSmewhennewversionofoneofthedatasetsappear

–  UserscaneasilyunderstandtheintervenSonyoumadetotheoriginaldataset

–  Loadthecompounddatasetinasingleseman+crepository–  PerforminferencewithrespecttotractableOWLdialects–  Defineasetofsamplequeriesagainstthecompounddataset

•  Thosedeterminethe“levelofservice”orthe“scopeofconsistency”contractofferedbythereasonableview


Reason-ableViews:Objec+ves

•  Makereasoningandqueryevalua+onfeasible

•  Guaranteeabasiclevelofconsistency–  Thesamplequeriesguaranteetheconsistencyofthedatainthesame

wayinwhichregressiontestsdoforthequalityofthesoYware

•  Guaranteeavailability–  Inthesamewayinwhichwebsearchenginesareusuallymorereliable

thanmostofthewebsites

•  Easierexplora+onandqueryingofunseendata–  LowerthecostofentrythroughURIauto-completeandRDFsearch–  Samplequeriesprovidere-usableextracSonparerns,whichreduce

theSmeforacquaintancewiththedatasetsandtheirinterconnecSons


TwoReason-ableViewstotheWebofLinkedData

•  FactForge:LinkedDataSeman+cRepository(inred)–  SomeofthecentralLODdatasets–  General-purposeinformaSon(notspecifictoadomain)–  1.2Bexplicitplus.9Minferredindexed,4Bretrievablestatements–  Thelargestupper-levelknowledgebase–  hnp://www.ontotext.com/FactForge/

•  LinkedLifeData-PIKB(inyellow)–  20+ofthemostpopularlife-sciencedatasets–  Complementedbygluingontologies–  2.7Bexplicitand1.4Binferred,totalof4.1Bindexedstatements–  Thelargestbodyofknowledgethatwasusedforreasoning–  hnp://www.linkedlifedata.com


LinkingOpenDataDatasetsandViews(redandyellow)


Presenta+onOutline







LinkedDataSeman+cRepository

•  Datasets:DBPedia,FreeBase,Geonames,UMBEL,MusicBrainz,Wordnet,CIAWorldFactbook,Lingvoj

•  Ontologies:DublinCore,SKOS,RSS,FOAF•  Inference:materializaSonwithrespecttoOWL2RL

–  SeemstocompletelycoverthesemanScsofthedata–  owl:sameAsop+miza+oninBigOWLIMallowsreducSonofthe

indices,withoutlossofsemanScsorperformance

•  Freepublicserviceathnp://FactForge.ontotext.com,–  IncrementalURIauto-suggest–  QueryandexplorethroughForestandTabulator–  RDFSearch:retrieverankedlistofURIsbykeywords–  SPARQLend-point


FactForgeLoadingandInferenceSta+s+cs


Dataset

ExplicitIndexedTriples('000)

InferredIndexedTriples('000)

Total#ofStoredTriples('000)

En++es('000ofnodesinthegraph)

Inferredclosurera+o

Sechmataandontologies 11 7 18 6 0.6DBpedia(categories) 2,877 42,587 45,464 1,144 14.8DBpedia(sameAs) 5,544 566 6,110 8,464 0.1UMBEL 5,162 42,212 47,374 500 8.2Lingvoj 20 863 883 18 43.8CIAFactbook 76 4 80 25 0.1Wordnet 2,281 9,296 11,577 830 4.1Geonames 91,908 125,025 216,933 33,382 1.4DBpediacore 560,096 198,043 758,139 127,931 0.4Freebase 463,689 40,840 504,529 94,810 0.1MusicBrainz 45,536 421,093 466,630 15,595 9.2Total 1,177,961 881,224 2,058,185 283,253 0.7

Post-processing

•  Severalkindsofpost-processingwereperformed−  Goal:toalloweasiernavigaSonandbrowsing−  Mechanisms:theresultsareavailablethroughsystempredicates−  Forinstance:preferredlabels,textsnippetsandRDFRanksforall

nodes

•  FinalStaSsScs−  NumberofenSSes(RDFgraphnodes):405M−  Numberofinsertedstatements(NIS):1,2B−  Numberofstoredstatements(NSS):2.2B−  Numberofretrievablestatements(NRS):9.8B

−  7.6Bstatements“compressed”throughBigOWLIM’sowl:sameAsopSmisaSon


Presenta+onOutline



–  Contents:thelargestbodyofcommon-senseknowledge–  RDFSearch:probablythebestwaytoexploreunknowndatasets–  LoadingandInferenceStaSsScs–  Queryingmul+pledatasets,consideringtheirseman+cs




GuesswhoisthemostpopularGermanentertainer?


PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX dbp-ont: <http://dbpedia.org/ontology/> PREFIX dbpedia: <http://dbpedia.org/resource/> PREFIX opencyc: <http://sw.opencyc.org/2008/06/10/concept/en/> PREFIX geo-ont: <http://www.geonames.org/ontology#> PREFIX FactForge: <http://www.ontotext.com/

SELECT * WHERE { ?Person dbp-ont:birthPlace ?BirthPlace ; rdf:type opencyc:Entertainer ; FactForge:hasPageRank ?RR . ?BirthPlace geo-ont:parentFeature dbpedia:Germany . } ORDER BY DESC(?RR) LIMIT 100

•  Involvesdatafrom:DBPedia,Geonames,UMBEL,andMusicBrainz•  Requiresinferenceovertypes,sub-classes,andtransiSverelaSonships•  WithoutFactForge,ge}nganswertosuchqueriesinrealSmeisimpossible•  AndthemostpopularentertainerborninGermanyis:

–  AskingfactualquesSonstoaglobalknowledgebasecanbringunexpectedandstrange,butformallycorrectresults–oneshouldbepreciseandconsidercontexts

F. Nietzsche

TheModiglianiTestfortheSeman+cWeb

•  ReadWriteWeb’sfounderRichardMcManus:“…the+ppingpointfortheSeman+cWebmaybewhenonecan…deliver–usingLinkedData–acomprehensivelistoflocaSonsoforiginalModiglianiartworks…”

hnp://www.readwriteweb.com/archives/the_modigliani_test_for_linked_data.php

….


TheFactForgeQueryPassingtheModiglianiTest


PREFIX fb: <http://rdf.freebase.com/ns/> PREFIX dbpedia: <http://dbpedia.org/resource/> PREFIX dbp-prop: <http://dbpedia.org/property/> PREFIX dbp-ont: <http://dbpedia.org/ontology/> PREFIX umbel-sc: <http://umbel.org/umbel/sc/> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX ot: <http://www.ontotext.com/> SELECT DISTINCT ?painting_l ?owner_l ?city_fb_con ?city_db_loc ?city_db_cit WHERE { ?p fb:visual_art.artwork.artist dbpedia:Amedeo_Modigliani ; fb:visual_art.artwork.owners [ fb:visual_art.artwork_owner_relationship.owner ?ow ] ; ot:preferredLabel ?painting_l. ?ow ot:preferredLabel ?owner_l .

OPTIONAL { ?ow fb:location.location.containedby [ ot:preferredLabel ?city_fb_con ] } . OPTIONAL { ?ow dbp-prop:location ?loc. ?loc rdf:type umbel-sc:City ; ot:preferredLabel ?city_db_loc }

OPTIONAL { ?ow dbp-ont:city [ ot:preferredLabel ?city_db_cit ] } }

Presenta+onOutline



•  PARTNERPRESENTATIONS(3:20-4:20)–  BPEng-Businessprocessmanagement(Italy)–  TopQuadrant(USA)–  StructureDynamics(Canada)

•  WRAP-UP(4:20-4:30)


StructuredDynamics

•  StructuredDynamicsisdeveloperofUMBEL–  AderivaSveofCyc,whichisoneofthedatasetsinFachorge

•  LikeOntotextStructuredDynamicsalsousesGATE

•  StructuredDynamicsandOntotextstartpartnershiptoprovideabererusefulsemanScstructuretoWikipediaviaupdatedversionofUMBEL–  AsdiscussedintheWikipedia'sStructureddatachallengesession


Presenta+onOutline




•  WRAP-UP(4:20-4:30)


Presenta+onOutline


Thankyou!

Wedevelopcoreseman+ctechnologyOntotextinvested200person-years,partneredwith100leadinggroups,

createdsomeofthemostpopulartools,anddeliveredmulSplesoluSons.

Weknowwhatworksandwhatdoesn’tOntotextsetmanybenchmarksandadvancedthefronSersofthesemanScdatabases.

Weinventedthe“semanScannotaSon”–linkingtextwithdata

Nowwearepreparedto

interlinkyourdata,yourcontent,andtheweb


360° semanc technologies: web mining, text analysis ... · jun 2010 360° semanc technologies: web...

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