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1、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,August24,2014Asamplerofresearchproblems•••••••••••••Growth:knowledgegraphsareincomplete!Linkp
2、rediction:addrelationsOntologymatching:connectgraphsKnowledgeextraction:extractnewentitiesandrelationsfromweb/textValidation:knowledgegraphsarenotalwayscorrect!Entityresolution:mergeduplicateentities,splitwronglymergedonesErrordetection:removefalseassertionsInterface:ho
3、wtomakeiteasiertoaccessknowledge?Semanticparsing:interpretthemeaningofqueriesQuestionanswering:computeanswersusingtheknowledgegraphIntelligence:canAIemergefromknowledgegraphs?AutomaticreasoningandplanningGeneralizationandabstraction9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
4、相似性]层次主题模型[弱监督]KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014Methodsandtechniques•••SupervisedmodelsSemi-supervisedmodelsDistantsupervision2.Entityresolution•Singleentitymethods•Relationalmethods3.Linkprediction••••Rule-basedmethods
5、ProbabilisticmodelsFactorizationmethodsEmbeddingmodels80Notinthistutorial:•Entityclassification•Group/expertdetection•Ontologyalignment•Objectranking1.Relationextraction:KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014•Extractingseman
6、ticrelationsbetweensetsof[grounded]entities•Numerousvariants:•••••Undefinedvspre-determinedsetofrelationsBinaryvsn-aryrelations,facetdiscoveryExtractingtemporalinformationSupervision:{fully,un,semi,distant}-supervisionCuesused:onlylexicalvsfulllinguisticfeatures82Relati
7、onExtractionKobeBryantLALakersplayForthefranchiseplayerofonceagainsavedmanofthematchfortheLakers”histeam”LosAngeles”“KobeBryant,“Kobe“KobeBryant?KDD2014TutorialonConstructingandMiningWeb-scaleKnowledgeGraphs,NewYork,August24,2014Supervisedrelationextraction•Sentence-lev
8、ellabelsofrelationmentions••"AppleCEOSteveJobssaid.."=>(SteveJobs,CEO,Apple)"SteveJobssaidthatApplewill.."=>NI
