知识图谱 概念与技术 第13章：基于知识图谱的问答技术

《知识图谱:概念与技术》的问答崔万云WanyunCuioIcomefrom•2013-2017PhD,FudanUniversity•AdvisedbyWeiWangandYanghuaXiaoIworkon•Questionanswering•2012.1–2012.11MicrosoftResearchAsiaAdvisedbyHaixunWang,IEEEfellow•2014.7–2014.11BaiduDeepQAproject(小度机器人)•Knowledgegraph33本章大纲总结44出的自然语言问题出的自然语言问题AppleSiri背景：相关产品weoneona应用场景康咨询WatsonImproveCancerCare.ksduringtheAppleWWDC2016.allanguagesearchforfilesonyourcomputer.的问答崔万云•为不同语义理解模型的整合提供了应用出口•为不同模型的关联分析、数据共享、参数共享等提出了实际需求•为多个自然语言语义理解技术模型的整体突破带来了技术愿景•降低了人机交互的门槛•提供了访问海量知识的新渠道QA的知识源问答社区搜索引擎百科知识图谱一个简单知识图谱•(d,population,390k)为什么KBQA？富知识表示•纯文本:文本句子的内部理解•知识图谱:关联性数据，提供文本理解的语义背景识准确率•纯文本:文本错误或者不同文本的知识矛盾•知识图谱：人工标注或解析自网页表格的高质量数据•纯文本:倒排表•知识图谱:存储于数据库，使用索引加速查询上的结构化查询ereonnum}Wheresubject=‘d’andpredicate=‘population’知识问答方法概述知识问答方法概述•通过人工构造规则模板将问题映射到属性•较高的准确率和较低的召回率(对问题多样性的较低覆盖率)•可解释性强知识问答方法概述•基于图做问题-答案的相似度计算和语义消岐•召回上升•可解释性尚可：来自图的合理性Naturallanguagequestionsforthewebofdata,知识问答方法概述•将离散的问题表示为连续向量•通过深度神经网络理解问题QuestionAnsweringoverFreebasewithMulti-ColumnConvolutionalNeuralNetwork,ACL2015QANet:CombiningLocalConvolutionwithGlobalSelf-AttentionforReadingComprehension,2018•较高的准确率和召回率•可解释性差：人类无法理解和预测神经网络知识问答方法概述•将文本作为补充知识源，解决数据稀疏性问题HybridQuestionAnsweringoverKnowledgeBaseandFreeText,COLING2016•更高的准确率和召回率•可解释性差•适用条件更严格：需要有配对文本知识问答方法概述:效果(准确&召回)的问答崔万云深度学习方法 Problemanalysis:intentclassificationuestioncandidatevalue•0thevalueisnottheanswer•1thevalueistheanswerAlgorithm:howtounderstandthequestions?g•wordembedding•SimilarsemanticshaveclosevectorsAlgorithm:howtounderstandthequestions?ing•Mergethewordembeddings•Notethesequentialfeature•CNN/RNNACL2015outstandingpaper的问答崔万云Multi-ColumnCNN:addmorefeaturespathontextrtypeQuestionAnsweringoverFreebasewithMulti-ColumnConvolutionalNeuralNetwork,ACL2015•Fullyutilizeallthefeatures.•Hierarchicalfeatureorganization.ns•Question•上海财经大学的校长是谁nowledgebases•Value•樊丽明•Entitydescription•教授•Predicate•现任校长的问答崔万云•3es•现任校长•Onehot:•现任校长•Wordlist:•现任校长•Charlist:•现任for1feature校长orcharlist的问答崔万云NeuralnetworkfordicateLSTMlayermbeddingermbeddingerLSTMlayermbeddingerMetaoutputNeuralnetworkfordicateLSTMlayermbeddingermbeddingerLSTMlayermbeddingerMetaoutput•3representationgranularity•Aggregation•现任校长•Onehot:•现任校长•Wordlist:•现任校长•Charlist:•现任校长现任校长现任校长现任校长的问答崔万云NeuralnetworkforteNeuralnetworkforQuestionLSTMlayerEmbeddinglayerEmbeddinglayer长现任校现任校长MetaoutputEmbeddinglayerLSTMlayer现任校长MGDNN:predicate-questionsimilarityNeuralnetworkforteNeuralnetworkforQuestionLSTMlayerEmbeddinglayerEmbeddinglayer长现任校现任校长MetaoutputEmbeddinglayerLSTMlayer现任校长OuOutputqCConcatenateOutputqOutputvConcatenateConcatenateNeuralnetworkforQuestionNeuralnetworkforValueNeuralnetworkforPredicateMetaoutputLSTMlayerEmbeddingerEmbeddinger现任校长现任校长答Embeddinger现任校长LSTMlayerOutputqOutputvConcatenateConcatenateNeuralnetworkforQuestionNeuralnetworkforValueNeuralnetworkforPredicateMetaoutputLSTMlayerEmbeddingerEmbeddinger现任校长现任校长答Embeddinger现任校长LSTMlayerOOutputqConcatenateConcatenateOOutputeConcatenateConcatenateNeuralNeuralnetworkforEntityDescriptionOutputqConcatenateNeuralnetworkforQuestionNeuralnetworkforPredicateMetaoutputLSTMlayerEmbeddingerEmbeddinger现任校长现任校长现任校长 答崔万云LSTMlayerEmbeddingerOutputvConcatenateNeuralnetworkforValueOutputeConcatenateNeuralnetworkforEntityOutputqConcatenateNeuralnetworkforQuestionNeuralnetworkforPredicateMetaoutputLSTMlayerEmbeddingerEmbeddinger现任校长现任校长现任校长 答崔万云LSTMlayerEmbeddingerOutputvConcatenateNeuralnetworkforValueOutputeConcatenateNeuralnetworkforEntityDescriptionOutputqConcatenateConcatenate的问答崔万云Ourscoreimproving)•KBQAbeatsallcompetitors.SEQ2SQL:agenerativemodel•HowmanypeopleliveinUSA?•Location:0.04•Population:0.85•President:0.02•…•Generativemodel•HowmanyCFLteamsarefromkCollege•SELECTCOUNTCFLTeamCollege=‘York’•SEQ2SEQ->SEQ2SQL•PROS:feasibletoSEQ2SQL:dataaugmentationbyRL•Input:aquestionandthecolumnsofthetable•Output:generatesthecorrespondingSQLoftheSQLagainstthedatabaseText:extrafeaturesforQAsityofKBQA•Incompletenessofknowledgegraphs•LackoflabeledtrainingdataobetteransweraquestiongetherwithKBQA•DoKBQA,textunderstandingsimultaneously•UsejointinferencetocomputethefinalanswerAAnswerreranking•FirstdoKBQA•Get(answer1,score1),…,(answern,scoren)•ThenreranktheanswersFeatures:scorei,textTogetherwithKBQA•••••Jointinferenceforheterogeneousfeatures.KBQA:generatescandidatewersandscoresbyKBQA.MC:providesanswersbyMCaseatures.Rerank:reranksanswersbyExperimentalResultsThisverifiestheeffectivenessofthereranking.如何构建更鲁棒的问答系统 odelsbeathumanAretheyrobustenoughs•ControllabilityAretheyrobustenough?ta•AdaptationPercyLiang’sQuestionArticleArticle:SuperBowl50(fromSQuADdataset)gbecamethefirstquarterbackevertoleadtwodifferentteamstomultipleSuperBowls.HeisalsotheoldestquarterbackevertoplayinaSuperBowlatage39.ThepastrecordwasheldbyJohnElway,wholedtheBroncostovictoryinSuperBowlXXXIIIatage38andisurrentlyDenversExecutiveVicePresidentofFootballOperationsandGeneralManager.erseynumberinChampBowlXXXIVQuestionWhatisthenameofthequarterbackwhowas38inSuperBowlXXXIII?”deradversaryJeffDeannJohnElwayEvaluatingReadingComprehensionSystemsRobinJiaetal.2017PercyLiang’sQuestionOriginalF1AdversarialF1BiDAF-EjNetlesF1:74.3->70.0IftestthismodelonprependedsentencesF1:70.0->ReadingComprehensionSystemsRobinJiaetal.2017Aretheyrobust?-NOAdaptationtonewdataLearning atvaluesandialsinquestionunderstanding•Theinputsentenceisawordsequence.•Theordersofthewordsaffecttheirsemantics.•recurrentneuralnetwork•Usesequentialmemoryfromtheformerunitincurmoreerrorsinspecificdomains.gdataforspecificromthesource•recurrentneuralnetwork•UsesequentialmemoryfromtheformerunitowledgeemoryintheHowtotransferknowledge?•Thetargetdomain’sparametersarewell-trained•No•Thetargetdomain’sparametersaretrainedinsufficiently.Yes•Thetargetdomain’slabelisthesameasthesourcedomain’s.•No->directlyusethesourcedomain’sprediction•Thetargetdomain’slabelisdifferentfromthesourcedomain’s.YesHowtotransferknowledge?•Thetargetdomain’sparametersaretrainedufficiently•Thetargetdomain’slabelisdifferentfromthesourcedomain’s.Howtotransferknowledge?10OpenSpecificdomaindomainHowtotransferknowledge?cercc00Open1Open1Opendomainknowledgetransferredthroughtherededge.siHowtotransfersequentialsi-TransferableRNNomainstothetargetdomain.SourceSourceDomainTargetDomainSiSiTixiSSi-1xxiorrespondingsequentialmemoryThesequentialmemoryiserredthroughtheblueedge.Theinputknowledgeisansferredthroughtherededge.ferableM•Allowthesequentialmemorytransferring•TransferableRNNframeworkndthesequentialmemoryis•Ifthesourcedomain’sparametersaretrainedsufficiently,usethesourcedomainmore-andviceversa.•Usegatestodecidetowhatextendtheknowledgeisretained.cTt+×+×hTi,tftitσcTt+×+×hTi,tftitσ×××itfit σσhTi,t-1byLearningQuestionAnsweringwithTemplatesandusworksed•Representsentencesbytemplates•Byhumanlabeling•PROs:•User-controllable•Applicabletoindustryuse•CONs:•Cannothandlethediversityofquestions.•Neuralnetworkbasedapproaches•Representsentencesbyembeddings•Bylearningfromcorpus•PROs:•Feasibletounderstanddiverse•CONs:•Poorinterpretability•Notcontrollable.Unfriendlytoindustrialapplication.Ourapproach•Representnaturallanguagequestionsbytemplates.•E.g.•Howmanypeoplearetherein$city?•Interpretable•User-controllab