内容参考讲稿kg applications for vertical sectors

PublishingandConsumingKnowledgeGraphsinVerticalSectorsSomeSomeWell-knownApplicationsofKnowledgeGraphConstructingandConstructingandMiningWeb-scaleKnowledgeGraphs@KDD2014ConstructingandMiningWeb-scaleKnowledgeGraphs@KDD2014ConstructingandConstructingandMiningWeb-scaleKnowledgeGraphs@KDD2014LinkingDatatoUncoverBusinessinternallinkeddata+externallinkeddata=CommonWaytoPublish&ConsumeUnstructured/Semi-structuredData

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KGinLifeSciencePracticeinDrugLOD,Text,UnstructuredDataforHealthPfizerwantstolinklabdatafromtheirlabs(mostlychemicalsandtests)toexternaldatasetstocreatealifesciencedatacloud.NOVARTIScombinesenterprisedatawith10billioncuratedlifesciencetriplestheOpenPHACTSOnlySemanticDBcanreadLODLookingforAlzheimerDiseaseSignaltransductionpathwaysareconsideredtoberichin“druggable”targets-proteinsthatmightrespondtochemicalCA1PyramidalNeuronsareknowntobeparticularlydamagedinAlzheimer’sdisease.CanwefindcandidategenesknowntobeinvolvedinsignaltransductionandactiveinPyramidalNeurons?ASPARQLASPARQLQuerySpanning4multipledataadhocqueriesoverurces(inRDF)easyAutomaticAutomaticICDMeetingtheChallengeswithSemanticUnaffordableCostsofTherateofincreaseinhealthcarecostsfarexceedsthegrowthofthenationaleconomyandindividualearningsin52%ofcostisfordrugs,5Xofdevelopedcountries50%ofhealthcarecostscomeoutof70%havenoorinadequateinsuranceIn2006,49%sickpeopledon’tseekadoctorand30%refusetogotohospitalTimeBomb–RapidAging (65orolder)projectedtotriplefrom8%to24%by2050,to322millionpeopleTheratioofworking-adult(age15to64)toelderlywilldropfrom9to2.5by2050Theagingtrendisinevitableandwillacceleratewithexpected:IncreaseoflifeDecreaseofEnormouspressureonhealthcareCosts,qualityandTheTheHealthcareHowtoreducetheoverallcostsofHowtoenhancehealthcareHowtoimprovecost/benefitratioofhealthHowtoincreaseefficiencyofhealthcarefacilities(hospitalandclinics)?ICDICD–InternationalStatisticalClassificationofDiseasesPublishedbyWorldHealthOrganization(WHO),currently:ICD-10forclassificationof14,813ICD-9forclassificationof5,458surgicalRelyonmanualcodingbasedonpatientmedicalrecordsTime-consumingandinconsistentPotentialforinsuranceChallengesofAutomaticICDKeywordmatchdoesnotTraditionalmachinelearningisvery疾病名称PYM内内疾病名称PYM内内内内内器郑XX:女49岁医保 ,少。术后抗炎治疗。,出院情况：少量 Mapto⼿术名称SPMTurnEMRintoaSemantic ,,, 。术后抗炎治疗。,出院情况：少 流Ontology-BasedNLPAutomaticNLPguided -specificAutomaticclassificationworksmore 疾病名称PYM内内疾病名称PYM内内内内内器郑XX:女49岁医保 ,检查，予息隐＋扩宫颈，于2011.1.13在宫腔镜检查+定位取环术。HSC下见：内⼿术名称SPMSemanticTechnologywithApplicationApplicationofWatsoninHealthcareandMoonShotsinAndersonCancerKGinAgricultureAgricultureinAsiaAgricultureisstrategictonationalwellbeing,LossoffarmersallovertheAsia,duetomajormigrationtocitiesandmanufacturingsectorsJapanimports60%ofitsfoodconsumedin60%ofJapanesefarmersareover65yearsSlow-downincropyieldsdespitebettertechnology(fertilizers,pesticides,etc.)Estimated50%offertilizerappliedislostinMalaysiaduetorun-offandover-fertilizationYieldofcitrusperacre isonly¼ofyieldintheYieldgapinAndhra exceedsChallenge–Crop⼤量元素组成氨基酸、蛋叶小黄化，枝条纤细，小 粗，叶色浓绿；白、叶绿素的枯死。座果率低，果实光花量减少，座果率低；重要元素 ；促进滑，果小皮薄 提早，果皮粗厚，果型增大，果实和叶生含酸量稍低；轻度缺乏品

施用氮肥，增加农家肥料用量，叶面喷布尿长 改善

素、硝酸钾参与蛋白质、核酸的合成；

花期老叶大量脱落，老叶古铜色；花少；新梢纤弱。果 糖、酸代谢和果皮稍粗厚 较深，浮皮保持细胞膨压；促进光合作用和

VC叶小，花后大量落叶，叶片绿色浅或变黄，小枝枯死。 呼吸作用；活化果小而光滑，皮薄 定酶；促进果快，酸稍少，果汁含量提实 解毒作用；细胞田间条件下， 结合作用和酶激象发生。光合作用；酶活 化成分；促进蛋块，或白质合成；促进（2）土壤施石灰100-200公斤/亩，调节土壤pH值5.5-6.5EncodingCropKnowledgeSemanticSemanticMap -AutomaticDataTableTransformationData

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RDFDatabase<0 09~1锂Semantic 计<009~1钙镁硫硼铁锰锌铜钼钠氯锂SampleFertilization(N)SampleSampleQuerytontRefinedRefinedQueryntCompleteOntology(knowledge)forLackingNitrogenKnowledgeCropCropntingnning(whatcropandwhen)–ntingManagement,andhowtoincreaseyieldandquality–YieldSoil/Terrain/HowbestandcosteffectivelytoprovideoptimalnutrienttocropatdifferentpointsofitslifeDisease/CatchandtreatdiseaseatthefirstsightingbeforeitcausessignificantorirreversibledamagetothecropanditsyieldLong-termagriculturenningandshort-termdrought/floodmitigation–YieldManagementCompleydependsontraditional,tacitknowledgeEmployup-to-datecrop/soil/ambientknowledgetoincreasecropyieldsFertilizationbasedonrule-of-thumb,oftenwastefulandBasedonsoil,terrain,croplifecycle,nutrientneedstocreateindividualizedfertilizationnOftenmisdiagnosecropdisease,ineffectivediseaseDiagnoseandtreatdiseaseatfirstsightwiththecorrectandmostcosteffectivepesticideFarmersatthewhimofweather,missonsetofdroughtortoolateforfloodAdvancedwarningofanyimpendingweatherchanges;helpprovidetimelyalertPracticeinBiodiversityPracticeinBiodiversityChallengesforBiodiversity Verydiversesubjects,evenjustforSabahBiodiversityIntegratedInformationSystem–SaBIISSharedWhyWhyCustomConverterConvertRDBDatatoRDFTripleCSVCSVRDFTablesTablesofdataautomaticallyimportedintoSabahConservationKGKGinmunicationsIndividualizedIndividualizedalizedcustomerAmdocsAmdocsNextalizedCustomerMrs.Porterlastmonth’sphonebillincreasedbyUS$30,becauseherdaughtersignedup3dailyhoroscopesfor$9.99permontheachwithoutherknowingit.Shecalledthephone tocancelthehoroscopesubscription.Theagentknewrightawaywhyshecalledbeforesheevenex inedtotheagent.Theagentnotonlycanceledthesubscriptionforher,butalsowaivedthelastmonthfeeof$30.Howdoestheagentknowhowbesttoservethecustomer,Mrs.Porter?HowDoestheAgentThesystemknowsthereasonofthecall–Thelatestphonebillcontainsunusual3rdparty–Many intsforsuchchargesfrompastThesystemknowsMrs.Porterasa“good”FromherpastbillingandpaymentThesystemwantstokeeproyaltyofgood encodedinthesystemwaivesanypotentiallytroublesomefeesforgoodcustomersAlldonebythesystemautomaticallybeforetheTheagentsimplyfollowstheguidanceandthescriptprovidebythesystemtohandlethecall,savingtimeandmoneywhilemakingagoodcustomerhappycoisOriginalCreatorandUsersBigMassivevolumeofrawFora cowith10millionsubscribers,itgenerates300MillionCDRsDAILY(~110BillionCDRsyearly)EachCDRcontains50Fields,andupto10Thousandsof nMostdataaretoolowlevel,must intobusinessconcepts,CDRdataintocallingMonthlypaymentdataintocreditriskPaymentdataintospendingExampleofDatamappedtoBusinessBusinessBusinessPaymenttimeliness–early,on-timeorPaymentpattern–good,bad,improvingorTurnMassiveRawDataIntoBusiness ChargedisputePayDeviceActivated

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WebEventDataEventDataTurnTurnBigDataIntoBusinessKGinMediaPublishDynamicSemanticPublishinginKGinFinanceIntegratingRDBsforintegratedFindingFinancialCrookswhoAbuseBankruptcyDutchIrsBigDataNetherlandhasveryforgivingbankruptcylaw,andiseasytosetupnewcompaniesFinancialcrooksrepeatedlysetupcompaniestopurchasegoodsoncredit,shipthegoodsaway,thenfilebankruptcytoforfeitthedebtsManyfraudulentcompaniessellortransfercarsbeforefilingbankruptcy NeedtocatchthesecrookstostopthemfromfleecingthesystemTheBigDataManyorganizationsinvolvedinregisteringactivities:IRS,nationalandregionalFinancialControlagencies,ChamberCommerce,Non-profit,DMV,Massiveamountof“relevant”datainmanydatabasesfrommanyinstitutions,allwithdifferen