全國知識圖譜與語義計(jì)算大會ccks2016工業(yè)界論壇-t4wei

知識圖譜的集成 計(jì)算機(jī)科學(xué)與軟件新技術(shù)國CCKS2016講習(xí)班,提 IntroductiontoSemanticWebandknowledge PartI:ontology PartII:entity PartIII:anapplicationtodata 2Semantic SemanticWebwasathoughtfromTimBerners- GiveformalmeaningstoWebinformation– Web1.0(page)àWeb2.0(social)àWeb3.0(awebof SemanticWebiscommonformats integrationandcombinationofdrawnfromdiverselanguages recordinghowthedatarelatestoreal-worldobjects3 RDF

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LayerTheworldisnotmadeofstrings,butismadeofthings4Linkeddata 數(shù)據(jù)/關(guān)聯(lián)數(shù)據(jù) AsarealizationofSemantic LinkedDatareferstoacollectionofinterrelated Usedforlarge-scaleintegrationof,reasoningon,dataonthe LinkeddataUseURIstonameUseHTTPURIs(canbeProvideusefulinformationusingopenWebstandards(e.g.Includelinkstootherrelated5Linkedopendata(LOD)1,000+

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6Knowledge KnowledgeGraphisaknowledgebaseusedby toenhanceitssearchengine’ssearchresultswithsemantic-searchinformationgatheredfromawidevarietyofsources?知識圖譜是使用的一個(gè)知識庫， 亦可看作是一張巨大的圖，節(jié)點(diǎn)表實(shí)體或概念，邊則由屬性或關(guān)系 除了關(guān) (部分)真實(shí)世界的一個(gè)模 引入領(lǐng)域相關(guān)的 指定術(shù)語的含義(語義 使用合適的邏輯來形 描述 HeartisamuscularorganispartofthecirculatoryI.Horrocks.Ontologiesandthesemanticweb:thestorysofar. 大規(guī)模知識庫/圖譜規(guī)英文：4百萬個(gè)實(shí)體，5億個(gè)RDF三元125種1千萬個(gè)實(shí)體，1.2億個(gè)RDF三元4千萬個(gè)實(shí)體，10億個(gè)RDF三元 知識圖譜6億個(gè)實(shí)體，35億條RDF三元WolframAlpha計(jì)算知識引擎,CMUNELL,知心,搜狗知立9知識圖譜的技術(shù)族知識體已有知識 知識圖譜提 IntroductiontoSemanticWebandknowledge PartI:ontology PartII:entity PartIII:anapplicationtodata Sincelonglongtimes SyntacticSchema- e.g.,“WeiHu”vs.Schema- Terminological e.g.,“notebook”vs.Data-entityData-entity Pragmatic OntheSemantic Datahasexplicitsemantics,richlinks,Ontology Thepopularityofontologiesisrapidlygrowing,andthenumberofontologiescontinuesincreasing Ontology Theprocessofdeterminingcorrespondencesbetween 本體匹配即發(fā)現(xiàn)一個(gè)三元組????????>，包括一個(gè)源本體??，一個(gè)目標(biāo)本體??’，以及一個(gè)映射單元的集合??={??1??2????}。其中，????表示一個(gè)基本的映射元，可以寫成????=<????,??????>的四元 ????為映射單元的標(biāo)識符，用于唯一標(biāo)識該四元 ??,??’分別為??,??’中的術(shù) ??表示????’之間的相似度，滿足??//另外，可以有??表示??,??’之間的關(guān)系，常見的關(guān)系有等本體匹配：消除模式 （驅(qū)動的）Stateofthe語言學(xué)特征 本體中術(shù)語的語言學(xué)描 本地名(localnForanameNinanamespaceidentifiedbyaURII,thenamespacenameisI.ForanameNthatisnotinanamespace,thenamespacenamehasnovalue.Definition:IneithercasethelocalnameisN.n -->local 注釋 其他：foaf:name、dc:title語言學(xué)特征 本體語言學(xué)特征使用現(xiàn)狀的調(diào) 本地名使用多，有一些 注 鄰居未充 詞典查詢耗√√√√√√類√√機(jī)器學(xué)√排序、S-類√ Edit 指兩個(gè)字串之間，由一個(gè)轉(zhuǎn)成另一個(gè)所需的最少編輯操作次 編輯操作包括替換、插入、刪 一般來說，編輯距離越小，兩個(gè)字串的相似度越 I-Sub:??????(??1,??2)=????????(??1,??2)?????????(??1,??2)+??????????????(??1, biggestcommonsubstringtwo thelengthofunmatchedresultedfrominitialmatching 術(shù)語的語言學(xué)描述：本地名 、注 結(jié)點(diǎn)的語言學(xué)描述：前向鄰居的語言學(xué)描 術(shù)語的鄰居：主語鄰居、謂語鄰居、賓語鄰 術(shù)語的虛擬文檔：自身+???????? =???????? +??3???????? +??1 向量空間模型：TF-Stringsimilaritymetrics Lessthantwowordsperlabel:Jaro- Twoormorewordsper Synonyms:SoftJaccard,withLevensteinbase Nosynonyms:SoftJaccard,withLevensteinbase Lessthantwowordsperlabel:TF- Twoormorewordsper Synonyms:SoftTF-IDF,withJaro-Winklerbase DifferentLanguages:SoftTF-IDF,withJaro-Winklerbase Other:SoftTF-IDF,withJaro-Winklerbase結(jié)構(gòu)特征 Intuition:termsoftwodistinctontologiesaresimilarwhenadjacenttermsarennSimilarity?^_`??, =?^??, +

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搜索引擎 distance sbetween -basedsimilarity????????, =maxlog????,log?? ?log??(??,log???min{log????,log ?? isthenumber hitsforthesearchterm ?? isthenumber hitsforthesearchterm ????, isthenumber hitsforthetupleofsearchterms?? ??isthenumberofwebpagesindexed (??≈10`x)Ontologymatching Falcon- New Alotof(semi-)automaticalgorithmsand Mostareonlyapplicableforsmall ManyapplicationsrequirematchingBIG Medicineandbiology:GALEN,FMA, Agricultureandfood:AGROVOC, Librarycollections:Brinkman, Commonknowledge:DBpedia,

≥10K Adivide-and-conquer1.ontologypartitioningà2.blockmatchingà3.termRunningNewdirectionsnHolisticontologynIncreasingamountofdataàsimultaneouslymatchingnInput:asetΩ={??1,…,????}ofontologieswith??>2nOutput:??=??12∪??13∪??23∪?nGuaranteetofindalwaysthesameAglobaloptimal Limitationofpairwise ??isconsideredasalocalsolutiondependingoftheorderwhichtheontologymatchingiscarried e.g.??12∪??`}~≠??13∪??`~}≠??23∪??}~Holisticontology Extending um-weightedgraphmatchingproblemwithconstraints(cardinality,structuralandcoherence Threetypesof Class,objectproperty,data Representvirtualconnectionsbetweenthesametypesof Haveweightstorepresentsimilaritiesbetweenthe Correspondences(1:1)with umweight?à Linearconstraints:binary Classdecision disjoint 提 IntroductiontoSemanticWebandknowledge PartI:ontology PartII:entity PartIII:anapplicationtodata Entity SemanticWebdatahavereachedascaleinbillionsof Manydifferententitiesrefertothesamereal-world TypicallydenotedbyURIs,fromdistributeddata e.g.Wei? Entitylinkage:linkdifferententitiesthatrefertothesame a.k.a.coreferenceresolution,entitymatching,recordlinkage Theentitymatchingproblemwasoriginallydefinedin1959by beetal.andwasformalizedbyFelligiandSuntertenyearslater Outof31BRDFstatements,lessthan500Marelinksacross 實(shí) 的識 數(shù) 的消 消除描述這些標(biāo)識符RDF數(shù)據(jù)之StateStateofthe Stateofthe InLOD,millionsofentitieshavealreadybeen However,potentialcandidatesarestill Current owl:sameAs,inversefunctionalpropertiesSimilaritycomputation(alsointhedatabase ComparepropertiesandvaluesofEquivalence AnRDFtriple:???,??,???∈(??∪??)×??×(??∪??∪ Same-asrelation: ???,owl:sameAs,???à???,???∈??and???,???∈ Inversefunctionalproperty(IFP)relation: IFP:avaluecanonlybethevalueofthispropertyforasingle e.g.,??1,foaf:mbox,??,??2,foaf:mbox,??à???1,??2?∈??and???2,??1?∈ Functionalproperty(FP)relation: Cardinalityrelation: owl:cardinality/owl:maxCardinality= ??=??∪??∪??∪??+,??isanequivalenceSimilarity Similarity LinkSimilarity 問題一般為以下形??,?? ????????,??>??,??∈??,??∈?? ??和??是兩個(gè)字符串集合，??是相似度 時(shí)間復(fù)雜度為：??(??}??}) 現(xiàn)有的常規(guī)的方法是“過濾—驗(yàn)證”框 過濾階段：使用各種過濾方法縮小候選集大 常見方 All-Pairs,ED-Join,PPJoin,PassJoin Na?vepairwise:??}pairwise 1,000businesslistingseachfrom1,000differentcitiesacrossthe 1trillioncomparisons,11.6days(ifeachcomparisonis1 Mentionsfromdifferentcitiesareunlikelytobe Blockingcriterion: 1billioncomparisons,16minutes(ifeachcomparisonis1 Hashbased Pairwisesimilarity/neighborhoodbasedblocking Simpleblocking:invertedMachine Alinkage Learning Genetic ActiveSelectslinkcandidatestobelabeledbyaAhumanexpertlabelstheselectedlinkascorrectorincorrectThegeneticprogrammingalgorithmevolvesthepopulationoflinkagerules InLOD,millionsofentitieshavealreadybeen However,potentialcandidatesarestill Current Atpresent,probablymissmanypotentialSimilarity Toimprove,machine Time-consuming,labor-intensivetobuildalarge-scaletrainingDefinitionDefinition1.LetUbethesetofentitiesinasetDofdatasources.Given,theentitylinkageforuistoqueryaofforwhicharelationεwhereεlinksalltheentitiesinUthatrefertothesameobjectasudoes,arecoreferentwithHowtocombine?Oursolution: Query-drivenentity UseSearch/browsing–asystemknows“whattolink”onlyatqueryyzesmallportionsofaverylargedatasettoansweron-demandOurAutomaticallyinfersemanticallyentitiesbasedonOWL/SKOS

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