知识图谱构建技术-北理工.ppt

百科和佛学知识图谱构建技术介绍 漆桂林东南大学认知智能研究所 ScheduleofMyTalk 百科知识图谱构建技术佛学知识图谱构建技术 Whatisknowledge Facts information descriptions orskillsAcquiredthroughexperienceoreducationbyperceiving discovering orlearningKnowledgebase anorganizedrepositoryofknowledgeconsistingofconcepts instances relations properties facts rulesetc Isaprincipalpartofexpertsystems thepowerofanAIprogramcametobeseenaslargelyinitsknowledgebase EdwardFeigenbaum 1994ACMTuringAward capitalCity France Paris student enrollee person 35millionarticlesin288differentlanguages 15thousandconcepts600millioninstances20billionfacts NELL GoogleKnowledgeGraph KG Itisanewgenerationofintelligentsearchtechnology whichenablesyoutosearchforthings notstringsFormaldefinition aknowledgegraphisaknowledgebasewithgraphstructure wherethenodesareinstancesorconcepts andedgesarerelationsbetweenthemItisaspecialsemanticnetworkItbelongstoknowledgeengineering 上市公司 非上市公司 子公司 供应商 客户 竞争对手 合作伙伴 Example KGandSemanticSearch Godeeperandbroader TechnologiesofKnowledgeBaseConstruction Baidu Hudong Zh Wikipedia KnowledgeGraph KG ConstructionfromOnlineEncyclopedias Well knownopenknowledgegraphssuchasDBpedia YagoandZhishi mearebuiltfromonlineencyclopedias Technologiesofencyclopedicknowledgegraphconstruction DataextractionEntitymatchingTypeinference Zhishi me Zhishi me http zhishi me isthefirstefforttopublishlargescaleChinesesemanticdataandlinkthemtogetherasaChineseLinkingOpenData CLOD OverviewofZhishi me Currently itconsistsofstructureddataextractedfromthreelargestChineseencyclopediasites BaiduBaikeHudongBaikeChineseWikipediaItnowhasover10milliondistinctinstancesand200millionRDFtriples andcanbeaccessedbyonlineAPI lookupserviceandSPARQLendpoint Labels Abstracts Redirects Images rdfs label zhishi abstractrdfs commentdbpedia abstract zhishi pageRedirects zhishi thumbnail DataExtraction XingNiu XinruoSun HaofenWang ShuRong GuilinQi YongYu Zhishi me WeavingChineseLinkingOpenData ISWC2011 205 220 infoboxProperties http zhishi me sourceName property propertyName http zhishi me baidubaike property 中文名称 南京 zh DataExtraction InternalLinks zhishi internalLink zhishi categoryskos broader DataExtraction EntityMatching Baidu 北京 Zh Wiki 北京市 Equivalententities EntityMatching Automaticallydiscoveringandrefiningdataset specificmatchingrulesiniterationsDerivingtheserulesbyfindingthemostdiscriminativedatacharacteristicsforagivendatasourcepair e g baidu 北京 Zh wiki 北京市 FromHaofenWang Foreachpairofexistingmatchedinstances theirproperty valuepairsaremerged EntityMatching FromHaofenWang Matchingrule frequentsetmining baidu xandhudong xarematched iff valueOf baidu 标签 valueOf hudong 中文学名 andvalueOf baidu 拉丁学名 valueOf hudong 二名法 andvalueOf baidu 纲 valueOf hudong 纲 EntityMatching FromHaofenWang Applyingtheobtainedrule s ontheunlabeleddatatogeneratematches candidates Thecombinerisusedtocombineconfidencevaluesofamatch scandidate EntityMatching FromHaofenWang TypeInference Typeinformationstatingthataninstanceisofacertaintype e g Chinaisaninstanceofcountry isanimportantcomponentofknowledgebasesGivenanapplicationscenario QuestionAnswering Question WhoistheNobellaureateinliteratureofpeople srepublicofChina Answer Moyan Howtogettheanswer MoyanInstanceOfNobellaureateofpeople srepublicofChina TianxingWu ShaoweiLing GuilinQi HaofenWang MiningTypeInformationfromChineseOnlineEncyclopedias JIST2014 213 229 The4thJointInternationalSemanticTechnologyConference Approach InChineseonlineencyclopedias wediscoverthatlotsoffine grainedtypesexistincategoriesofarticlepages TimBerners Lee hasseveralcategories Englishcomputerscientists PeopleassociatedwithCERN EnglishexpatriatesintheUnitedStates LivingPeople WorldWideWebConsortium The4thJointInternationalSemanticTechnologyConference Approach InChineseonlineencyclopedias wediscoverthatlotsoffine grainedtypesexistincategoriesofarticlepages Givenanexample Giventhearticlepagesof China inBaiduBaike HudongBaikeandChineseWikipedia itscategoriesareasfollows The4thJointInternationalSemanticTechnologyConference Approach cont Wetakethecategoriesofonegiveninstanceasitscandidatetypesandtrytofilteroutthenoiseleveragingtheattributes The4thJointInternationalSemanticTechnologyConference Approach cont Wetakethecategoriesofonegiveninstanceasitscandidatetypesandtrytofilteroutthenoiseleveragingtheattributes Intuitively whengivenattributesofacertaininstanceasfollows actors releasedate director aninstanceof Movie name foreignname aninstanceof The4thJointInternationalSemanticTechnologyConference Approach cont Wetakethecategoriesofonegiveninstanceasitscandidatetypesandtrytofilteroutthenoiseleveragingtheattributes Intuitively whengivenattributesofacertaininstanceasfollows actors releasedate director aninstanceof Movie name foreignname aninstanceof Weassumethatifaninstancecontainstherepresentativeattributesofonecandidatetype theinstanceprobablybelongstothistype The4thJointInternationalSemanticTechnologyConference Approach cont Wetakethecategoriesofonegiveninstanceasitscandidatetypesandtrytofilteroutthenoiseleveragingtheattributes Intuitively whengivenattributesofacertaininstanceasfollows actors releasedate director aninstanceof Movie name foreignname aninstanceof Weassumethatifaninstancecontainstherepresentativeattributesofonecandidatetype theinstanceprobablybelongstothistype Butanotherproblemis categoryattributesarenotabundantlyavailable The4thJointInternationalSemanticTechnologyConference Approach cont ExplicitIsARelationDetector DetectexplicitinstanceOfandsubclassOfrelationsCategoryAttributesGenerator GenerateattributesforcategorieswithanattributepropagationalgorithmInstanceTypeRanker Rankcandidatetypeswithagraph basedrandomwalkmethod The4thJointInternationalSemanticTechnologyConference ExplicitIsARelationDetector ExplicitInstanceOfRelationDetection MiningexplicitinstanceOfrelationfrominfoboxes infobox anAVPset The4thJointInternationalSemanticTechnologyConference ExplicitIsARelationDetector ExplicitInstanceOfRelationDetection MiningexplicitinstanceOfrelationfrominfoboxes infobox anAVPset Example The4thJointInternationalSemanticTechnologyConference ExplicitIsARelationDetector ExplicitInstanceOfRelationDetection MiningexplicitinstanceOfrelationfromabstracts performdependencyparsingwithFudanNLP Qiuetal 2013 The4thJointInternationalSemanticTechnologyConference ExplicitIsARelationDetector ExplicitInstanceOfRelationDetection MiningexplicitinstanceOfrelationfromabstracts performdependencyparsingwithFudanNLP Qiuetal 2013 The4thJointInternationalSemanticTechnologyConference ExplicitIsARelationDetector ExplicitInstanceOfRelationDetection MiningexplicitinstanceOfrelationfromabstracts performdependencyparsingwithFudanNLP Qiuetal 2013 迈克尔 乔丹instanceOf篮球运动员 MichaelJeffreyJordan BasketballPlayer The4thJointInternationalSemanticTechnologyConference ExplicitIsARelationDetector ExplicitSubclassOfRelationDetection GeneratecandidateSubclassOfcategorypairsintheformof sub category category basedonthecategorysystem Checkwhetherthe sub category category sharethesamelexicalheadwithPOStagging Foreach sub category category checkwhetherthecategoryisaparentconceptofthesub categoryinZhishi schema Wangetal 2014 江苏学校 schoolinJiangSu subclassOf中国学校 schoolinChina The4thJointInternationalSemanticTechnologyConference CategoryAttributesGenerator Wetakeattributesininfoboxtemplatesasexistingcategoryattributesandattributesininfoboxofarticlepagesasinstanceattributes WeconstructaCategoryGraphcomposedofallcategorieswithsubclassOfrelations WepropagateattributesovertheCategoryGraphleveragingexistingcategoryattributes instanceattributes identifiedinstanceOfandsubclassOfrelations The4thJointInternationalSemanticTechnologyConference CategoryAttributesGenerator Theattributepropagationalgorithmarebasedonfollowingrules Rule1 Ifacategorychasattributesfrominfoboxtemplates theseattributesshouldremainunchanged Rule2 Ifacategorychassomeinstanceswithattributes theattributesshouldbepropagatedtocwhentheyaresharedbymorethanhalfoftheseinstances Rule3 Ifacategorychassomechildcategorieswithattributes theattributesshouldbepropagatedtocwhentheyaresharedbymorethanhalfofthesechildcategories Rule4 Ifparentcategoriesofacategorychaveattributes alltheattributesshouldbeinheritedbyc The4thJointInternationalSemanticTechnologyConference InstanceTypeRanker Weorganizeeachgiveninstance itsattributesandcategories i e candidatetypes ofthecorrespondingarticlepageintoanInstanceGraph WegroupsynonymousattributeswithBabelNetbeforeconstructingallInstanceGraphs The4thJointInternationalSemanticTechnologyConference InstanceTypeRanker The4thJointInternationalSemanticTechnologyConference InstanceTypeRanker Weassumethatthefewercategoriesanattributebelongsto themorerepresentativetheattributeis The4thJointInternationalSemanticTechnologyConference InstanceTypeRanker Whenexecutingarandomstepfromthegiveninstancetooneofitsattributes thewalktendstochoosethemostrepresentativeattributeinordertowalktothecorrectcategories Whenexecutingarandomstepfromanattributetotheoneofthecategoriesinthearticlepage thecategoriescontainingthisattributehaveequalopportunity The4thJointInternationalSemanticTechnologyConference Experiment AccuracyEvaluation Werandomlyselect500 category attribute pairsfromeachonlineencyclopediaand500typestatementsfromdifferentsourcesineachonlineencyclopedias Weinvitesixpostgraduatestudentswhoarefamiliarwithlinkeddatatolabeltheeachsamplementionedabovewith Correct Incorrect or Unknown Togeneralizefindingsoneachsampletothewholedataset wecomputetheWilsonintervals Brownetal 2001 for 5 The4thJointInternationalSemanticTechnologyConference Experiment AccuracyEvaluation Werandomlyselect500 category attribute pairsfromeachonlineencyclopediaand500typestatementsfromdifferentsourcesineachonlineencyclopedias Weinvitesixpostgraduatestudentswhoarefamiliarwithlinkeddatatolabeltheeachsamplementionedabovewith Correct Incorrect orUnknown Togeneralizefindingsoneachsampletothewholedataset wecomputetheWilsonintervals Brownetal 2001 for 5 The4thJointInternationalSemanticTechnologyConference Experiment ComparisonwithOtherKnowledgeBases OverlapofTypeinformation WecomparealltheobtainedChinesetypeinformationwiththatofotherwell knownknowledgebases namelyDBpedia YagoandBabelNet SinceDBpediaandYagohavemultilingualversions wemappedtheEnglishtypestatementstoChineseones bothinstanceandtypeinonetypestatementcanbemappedtotheChineselabels The4thJointInternationalSemanticTechnologyConference Experiment ComparisonwithOtherKnowledgeBases OverlapofTypeinformation WecomparealltheobtainedChinesetypeinformationwiththatofotherwell knownknowledgebases namelyDBpedia YagoandBabelNet SinceDBpediaandYagohavemultilingualversions wemappedtheEnglishtypestatementstoChineseones bothinstanceandtypeinonetypestatementcanbemappedtotheChineselabels TechnologiesofKnowledgeBaseConstruction WebAccesstoZhishi mehttp zhishi me api ScheduleofMyTalk 百科知识图谱构建佛学知识图谱构建 Framework takeBuddhistfiguresastheexample KnowledgeCollection Category方法人工观察百科中与佛教人物相关的分类抽取佛教人物分类下所有文章对应的实体命名规则方法例 菩萨 禅师 维基百科 佛教头衔 分类下的所有实体已抽取出的实体名中高频的公共字符串 KnowledgeFusion 主语融合实体的 别名 属性和重定向作为实体的别名集合不同来源的实体存在一个完全匹配的别名则认为是相同实体人工检查相同实体数多于三个的映射 百度百科 互动百科 维基百科 确吉坚赞