知识图谱_梳理PPT

知识图谱需要的技术,知识图谱架构,知识图谱一般架构:来源自百度百科复旦大学知识图谱架构:早期知识图谱架构,知识图谱一般架构:来源自百度百科,架构讨论,早期知识图谱架构,知识抽取,实体概念抽取实体概念映射关系抽取质量评估,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,Asamplerofresearchproblems,Growth:knowledgegraphsareincomplete!Linkprediction:addrelationsOntologymatching:connectgraphsKnowledgeextraction:extractnewentitiesandrelationsfromweb/textValidation:knowledgegraphsarenotalwayscorrect!Entityresolution:mergeduplicateentities,splitwronglymergedonesErrordetection:removefalseassertionsInterface:howtomakeiteasiertoaccessknowledge?Semanticparsing:interpretthemeaningofqueriesQuestionanswering:computeanswersusingtheknowledgegraphIntelligence:canAIemergefromknowledgegraphs?AutomaticreasoningandplanningGeneralizationandabstraction,9,7,2,关系抽取,定义:常见手段:语义模式匹配频繁模式抽取,基于密度聚类,基于语义相似性层次主题模型弱监督,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,Methodsandtechniques,SupervisedmodelsSemi-supervisedmodelsDistantsupervision,2.Entityresolution,Singleentitymethods,Relationalmethods,3.Linkprediction,Rule-basedmethodsProbabilisticmodelsFactorizationmethodsEmbeddingmodels,80,Notinthistutorial:Entityclassification,Group/expertdetection,OntologyalignmentObjectranking,1.Relationextraction:,9,2,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,Extractingsemanticrelationsbetweensetsofgroundedentities,Numerousvariants:,Undefinedvspre-determinedsetofrelationsBinaryvsn-aryrelations,facetdiscoveryExtractingtemporalinformationSupervision:fully,un,semi,distant-supervisionCuesused:onlylexicalvsfulllinguisticfeatures,82,RelationExtractionKobeBryant,LALakers,playFor,thefranchiseplayerofonceagainsavedmanofthematchfor,theLakers”histeam”LosAngeles”,“KobeBryant,“Kobe“KobeBryant,?,10,2,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,Supervisedrelationextraction,Sentence-levellabelsofrelationmentions,AppleCEOSteveJobssaid.=(SteveJobs,CEO,Apple)SteveJobssaidthatApplewill.=NIL,Traditionalrelationextractiondatasets,ACE2004MUC-7Biomedicaldatasets(e.gBioNLPclallenges),Learnclassifiersfrom+/-examplesTypicalfeatures:contextwords+POS,dependencypathbetweenentities,namedentitytags,token/parse-path/entitydistance,83,11,2,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,Semi-supervisedrelationextraction,Genericalgorithm(遗传算法),.5.,Startwithseedtriples/goldenseedpatternsExtractpatternsthatmatchseedtriples/patternsTakethetop-kextractedpatterns/triplesAddtoseedpatterns/triplesGoto2,Manypublishedapproachesinthiscategory:DualIterativePatternRelationExtractorBrin,98SnowballAgichtein&Gravano,00TextRunnerBankoetal.,07almostunsupervisedDifferinpatterndefinitionandselection,86,12,2,founderOf,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,Distantly-supervisedrelationextraction,88,Existingknowledgebase+unlabeledtextgenerateexamplesLocatepairsofrelatedentitiesintextHypothesizesthattherelationisexpressed,GoogleCEOLarryPageannouncedthat.SteveJobshasbeenAppleforawhile.Pixarlostitsco-founderSteveJobs.IwenttoParis,Franceforthesummer.,Google,CEO,capitalOfLarryPage,FranceAppleCEO,PixarSteveJobs,13,2,Distantsupervision:modelinghypothesesTypicalarchitecture:1.Collectmanypairsofentitiesco-occurringinsentencesfromtextcorpus2.If2entitiesparticipateinarelation,severalhypotheses:,1.,AllsentencesmentioningthemexpressitMintzetal.,09,“BarackObamaisthe44thandcurrentPresidentoftheUS.”(BO,employedBy,USA)89KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,14,2,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,Sentence-levelfeatures,Lexical:wordsinbetweenandaroundmentionsandtheirparts-of-speechtags(conjunctiveform)Syntactic:dependencyparsepathbetweenmentionsalongwithsidenodesNamedEntityTags:forthementionsConjunctionsoftheabovefeaturesDistantsupervisionisusedontolotsofdatasparsityofconjunctiveformsnotanissue,92,15,2,Distantsupervision:modelinghypothesesTypicalarchitecture:1.Collectmanypairsofentitiesco-occurringinsentencesfromtextcorpus2.If2entitiesparticipateinarelation,severalhypotheses:,1.2.,AllsentencesmentioningthemexpressitMintzetal.,09AtleastonesentencementioningthemexpressitRiedeletal.,10,“BarackObamaisthe44thandcurrentPresidentoftheUS.”(BO,employedBy,USA)“ObamaflewbacktotheUSonWednesday.”(BO,employedBy,USA)95KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,16,2,Distantsupervision:modelinghypothesesTypicalarchitecture:1.Collectmanypairsofentitiesco-occurringinsentencesfromtextcorpus2.If2entitiesparticipateinarelation,severalhypotheses:,1.2.3.,AllsentencesmentioningthemexpressitMintzetal.,09AtleastonesentencementioningthemexpressitRiedeletal.,10Atleastonesentencementioningthemexpressitand2entitiescanexpress,multiplerelationsHoffmannetal.,11Surdeanuetal.,12“BarackObamaisthe44thandcurrentPresidentoftheUS.”(BO,employedBy,USA)“ObamaflewbacktotheUSjustWednesday.”said.”employedBy,USA)98KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,wasborninonhealways(BO,(BO,bornIn,17,2,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,Distantsupervision,Pros,Canscaletotheweb,asnosupervisionrequiredGeneralizestotextfromdifferentdomainsGeneratesalotmoresupervisioninoneiteration,Cons,Needshighqualityentity-matchingRelation-expressionhypothesiscanbewrong,Canbecompensatedbytheextractionmodel,redundancy,languagemodel,Doesnotgeneratenegativeexamples,Partiallytackledbymatchingunrelatedentities,101,18,2,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,104,KobeBryant,Gasol,teammate,bornIn,playInLeague,BlackMamba,EntityresolutionLALakersplayForplayForPau,35,age,KobeB.Bryant,VanessaL.Bryant,marriedTo,1978Singleentityresolution,Relationalentityresolution,19,2,DEF:Weconsidertheentityresolution(ER)problem(alsoknownasdeduplication,ormergepurge),inwhichrecordsdeterminedtorepresentthesamereal-worldentityaresuccessivelylocatedandmergedtheproblemofextracting,matchingandresolvingentitymentionsinstructuredandunstructureddataMethods,Entityresolution/deduplicationMultiplementionsofthesameentityiswrongandconfusing.,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,Single-entityentityresolution,EntityresolutionwithoutusingtherelationalcontextofentitiesManydistances/similaritiesforsingle-entityentityresolution:Editdistance(Levenshtein,etc.)Setsimilarity(TF-IDF,etc.)Alignment-basedNumericdistancebetweenvaluesPhoneticSimilarityEqualityonabooleanpredicateTranslation-basedDomain-specific,105,21,2,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,RelationalentityresolutionSimplestrategiesEnrichmodelwithrelationalfeaturesrichercontextformatching,Relationalfeatures:,ValueofedgeorneighboringattributeSetsimilaritymeasures,Overlap/JaccardAveragesimilaritybetweensetmembersAdamic/Adar:twoentitiesaremoresimilariftheysharemoreitemsthatareoveralllessfrequentSimRank:twoentitiesaresimilariftheyarerelatedtosimilarobjectsKatzscore:twoentitiesaresimilariftheyareconnectedbyshorterpaths,114,KobeBryant,1978,teammate,bornIn,playFor,playInLeague,BlackMamba,LALakersplayFor,35,age,Pau,Gasol,22,2,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,KobeBryant,1978,teammate,bornIn,playFor,playInLeague,BlackMamba,LALakersplayFor,35age,Pau,Gasol,RelationalentityresolutionAdvancedstrategies,DependencygraphapproachesDongetal.,05RelationalclusteringBhattacharya&Getoor,07ProbabilisticRelationalModelsPasulaetal.,03MarkovLogicNetworksSingla&Domingos,06ProbabilisticSoftLogicBroecheler&Getoor,10,115,23,2,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,LINKPREDICTION,116,24,2,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,117,KobeBryant,LinkpredictionNYKnicks,PauGasol,teammate,playInLeague,teamInLeague,opponent,playFor,LALakersplayForAddknowledgefromexistinggraph,Noexternalsource,Reasoningwithinthegraph1.Rule-basedmethods2.Probabilisticmodels3.Factorizationmodels4.Embeddingmodels,25,2,KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014,FirstOrderInductiveLearnerFOILlearnsfunction-freeHornclauses:,118,Gasol,givenpositivenegativeexamplesofaconceptasetofbackground-knowledgepredicatesFOILinductivelygeneratesalogicalrulefortheconceptthatcoverall+andno-LALakersplayForplayForPau,teammate(x,y)playFor(y,z)playFor(x,z),teammateKobeBryantComputationallyexpensive:hugesearchspacelarge,costlyHornclausesMustaddconstraintshighprecisionbutlowrecallInductiveLogicProgramming:determini