专家观点基于深度学习的知识图谱实体对齐-王志春(31 页 P P T)

2020全国知识图谱与语义计算大会China

ConferenceonKnowledge

GraphandSemantic

Computing

南昌

–2020年11月基于深度学习的知识图谱实体对齐王志春北京师范大学人工智能学院知识图谱n知识图谱(KnowledgeGraph)以结构化的形式描述客观世界中概念、实体及其关系n大规模的知识图谱被构建和应用于多个领域

语义检索、智能问答、实体链接、阅读理解……pro

BaseCN-DBpediaKnowItAllNELL知识图谱实体对齐n实体对齐

(entityalignment)，是判断不同知识图谱中的两个实体是否指向真实世界同一对象的过程。ChinaBeijingNormal

UniversityBeijingForbidden

CityGreatWall北京师范大学故宫中国长城KG1KG2北京实体对齐应用场景知识图谱互联知识集成图片出自/versions/2014-08-30/lod-cloud.png基于相似度特征的实体对齐n实体对齐的基本假设：（1）等价实体具有相似的属性（2）等价实体具有相似的邻接实体n实体对齐的基本框架：迭代式对齐KG1KG2外部资源实体对齐相似特征计算候选实体

对选择初始对齐对齐结果对齐决策•

基于链出的相似度

•

基于链入的相似度

•

基于分类的相似度

•

基于作者的相似度

基于相似度特征的实体对齐维基百科百度百科[1]Wang,Z.,Li,J.,Wang,Z.,&Tang,J.

(2012).Cross-lingualknowledgelinkingacrosswikiknowledgebases.

WWW.对齐决策（分类）相似度特征计算输入Use

of-externaldictionaries-existingmappings?*-?*---?*-------Post-processing--Coherence

checksInconsistency

repair---Parallelprocessing-----MapReduce-GUI/webinterface/API-/

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√√√/--√/--Runtimeoptimization-Blocking--------Filteringindexingindexing-indexing-indexingindexingString

similaritymeasures√√√√√√√Furthersimilaritymeasures-----geographicalcoordinatesinverteddisparityStructurematcher-semanticsimilarityiterativeanchor-

basedmapping

generationiterativeanchor-

-basedmappinggenerationsemanticsimilarity-RDF数据集实体对齐工具RiMOMAgreementMakerCODILogMapSERIMIZhishi.links

SLINT+DataInputRDF,

OWLSPARQLRDF,

OWLRDF,

OWLSPARQLRDFRDFSupportedlinktypesowl:sameAsowl:sameAsowl:sameAsowl:sameAsowl:sameAsowl:sameAsowl:sameAsConfigurationadaptivemanualmanualmanualadaptivemanualadaptive-matchercombinationweightedaverageweightedcombinationweightedaverageweightedaverage-weightedcombinationweightedaverage1[2]NentwigM,HartungM,NgongaNgomo

AC,RahmE.

Asurveyof

currentlinkdiscoveryframeworks.

SemanticWeb.2017

Jan

1;8(3):419-36.候选实体对

选择相似度特征计算方法深度学习方法的引入n基于相似度特征的方法

对齐结果依赖于人工设计的特征

不同的对齐任务需要不同的特征相似度特征计算决策模型Yes/No深度学习方法的引入n基于相似度特征的方法

对齐结果依赖于人工设计的特征

不同的对齐任务需要不同的特征n基于深度学习的方法

利用表示学习、神经网络模型自动获

取隐式特征

在隐式向量空间计算实体相似度EncoderEncoder知识图谱分布式表示n在隐式向量空间对知识图谱中的实体

及关系进行表示、建模与学习

实体：表示为向量

关系：表示为向量或矩阵n分布式表示的应用

链接预测

三元组分类[3]BordesA,UsunierN,Garcia-Duran

A,WestonJ,YakhnenkoO.Translatingembeddingsformodelingmulti-relationaldata.InAdvances

inneural

informationprocessingsystems2013(pp.2787-2795).[4]WangZ,ZhangJ,FengJ,ChenZ.KnowledgeGraph

Embeddingby

Translating

on

Hyperplanes.

InAAAI

2014

Jul

27

(Vol.

14,pp.

1112-1119).[5]LinY,LiuZ,

SunM,LiuY,ZhuX.Learningentityandrelation

embeddings

forknowledge

graph

completion.

InAAAI2015

Jan

25

(Vol.

15,pp.

2181-2187).scorefunction:f(h,

r,t)

=

D(h

丄

+

dr,

t丄)scorefunction:f(h,

r,t)=

D(hr

+

r,tr)scorefunction:f(h,

r,t)

=

D(h+

r,

t)TransHTransRTransE•Knowledge

model

L∈{Li,Lj}T∈GL•Alignment

modelSA

=

Σ

Sa(T,

TI)

T,TI

∈δ(Li,Lj)

All

aligned

triples

MTransE：面向跨语言实体对齐的表示学习模型/~muhao/slides/mtranse_slides_short.pdf[6]MuhaoChen,YingtaoTian,MohanYang,andCarloZaniolo.Multilingualknowledgegraphembeddings

for

cross-lingual

knowledge

alignment.

InProceedingsof

the26thInternationalJointConferenceon

ArtificialIntelligence,pages

1511–1517,2017. Space

L1Knowledge

modelAlignment

model(h,,r,,

t,)Space

L2(h,

r,

t)MTransE：面向跨语言实体对齐的表示学习模型/~muhao/slides/mtranse_slides_short.pdf[6]MuhaoChen,YingtaoTian,MohanYang,andCarloZaniolo.Multilingualknowledgegraphembeddings

for

cross-lingual

knowledge

alignment.

InProceedingsof

the26thInternationalJointConferenceon

ArtificialIntelligence,pages

1511–1517,2017.基于TransE模型的实体对齐[7]Zequn

Sun,WeiHu,andChengkaiLi.Cross-lingualentity

alignmentvia

joint

attribute-preserving

embedding.

In

International

SemanticWeb

Conference,pages

628–644.

Springer,

2017.[8]BayuD.Trsedya,JianzhongQi,RuiZhang.Entity

AlignmentbetweenKnowledgeGraphsUsing

Attribute

Embeddings.AAAI2019•

对属性值进行字符Embdding的组合•

<实体,属性,属性值>按照TransE评分函数进行评分•

使用skip-gram模型对属性类型进行表示学习•结构信息、属性信息联合Embeddingn实体属性和实体关系相结合：JAPE[7]

、AttrE[8]JAPE[7]

AttrE[8]KB1seed

al,gnmentKB2(e11

,

a11

,

Inte@er)

(e21

,

a21

,

strin@)

(e22

,

a12

,

Double)……AE-basedsimiIaritycaIcuIati0nJ0intattribute-preservingembeddinge

:

{e32

,

e52

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…

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:

{e22

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1

LatentaIignment

searching0.58

…一0.81:

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0.73Attributeembeddingstructure

embeddings,m,lar,tyconstra,nts

(matr,ces)

e42e21,2overlayrelat,onsh,pgraphvectorrepresentat,onsattr,bute

valueabstract,one2:e30utputinputr22(1)(2)基于TransE模型的实体对齐使用新发现的等价实体更新实体的向量表示[9]HaoZhu,RuobingXie,ZhiyuanLiu,andMaosong

Sun.Iterativeentity

alignmentvia

jointknowledge

embeddings.

In

Proceedings

of

the

26th

International

Joint

Conference

on

ArtificialIntelligence,pages4258–4264.

AAAIPress,2017.[10]

SunZ,HuW,ZhangQ,QuY.BootstrappingEntity

AlignmentwithKnowledgeGraph

Embedding.

InIJCAI

2018

(pp.4396-4402).n迭代式实体对齐：IPTransE[9]

、BootEA[10]IPTransE[9]基于TransE模型的实体对齐n实体对齐结果评价

数据集：

DBP15k

DWY100k[9]

SunZ,HuW,ZhangQ,QuY.BootstrappingEntity

AlignmentwithKnowledgeGraphEmbedding.

InIJCAI

2018

(pp.

4396-4402).DWY100kDBP15k基于深度学习的实体对齐TranslationalModels(e.g.TransE)

MTransE(Chenet

al.,

2017)

IPTransE(Zhuet

al.,

2017)

JAPE(Sun

et

al.,2017),

AttrE(Trsedyaet

al.,

2019)

MultiKE(Zhanget

al.,

2019)基于TransE实体对齐模型的特点•同时对知识图谱内部的实体关系和跨知识图谱的对齐关系进行建模•模型损失

=a*知识模型损失+b*对齐模型损失，难以平衡EncoderEncodern基于图卷积神经网络的实体对齐模型

直接面向对齐任务学习实体的向量表示

使用图卷积神经网络作为特征编码器

GCN-Align：基于图卷积神经网络的实体对齐[11]Wang,Z.,Lv,Q.,Lan,X.andZhang,Y.,2018.Cross-lingualKnowledge

Graph

Alignmentvia

Graph

ConvolutionalNetworks.InProceedingsofthe2018

ConferenceonEmpirical

MethodsinNatural

LanguageProcessing(pp.349-357).

模型目标函数图卷积神经网络（GCN）[12]Kipf,T.,&Welling,M.(2017).

Semi-SupervisedClassificationwithGraph

ConvolutionalNetworks.ICLRDBP15KFR

→

ENEN

→

FRJA

→

ENEN

→

JAHits@1

Hits@10

Hits@50Hits@1

Hits@10

Hits@50Hits@1Hits@10Hits@50Hits@1Hits@10Hits@50JE15.3838.8456.5014.6137.2554.0118.9239.9754.2417.8038.4452.48MTransE

24.4155.5574.41

21.2650.6069.9327.8657.4575.94

23.7249.9267.93JAPESE

w/o

neg.SESE

+

AE29.5562.1879.3625.4056.5574.9633.1063.9080.8029.7156.2873.8429.6364.5581.9026.5560.3078.7134.2766.3983.6131.4060.8078.5132.3966.6883.1932.9765.9182.3836.2568.5085.3538.3767.2782.65JAPE0SE

w/o

neg.28.2360.9978.4724.6855.2574.1928.9060.6180.0325.3453.3671.94SE27.5862.0379.9824.9358.9577.7929.3563.3182.7626.3757.3576.87SE

+

AE30.2165.8182.5731.4263.8680.9531.0664.1181.5732.4562.2179.08GCNSE36.5173.4285.9336.0872.3785.4438.2172.4982.6936.9068.5079.51SE

+

AE37.2974.4986.7336.7773.0686.3939.9174.4686.1038.4271.8183.72GCN-Align：基于图卷积神经网络的实体对齐[11]Wang,Z.,Lv,Q.,Lan,X.andZhang,Y.,2018.Cross-lingualKnowledge

Graph

Alignmentvia

Graph

ConvolutionalNetworks.nGCN-AlignV.S.MTransE

、JAPE

、JE、InProceedingsofthe2018

ConferenceonEmpirical

MethodsinNatural

LanguageProcessing(pp.349-357).基于深度学习的实体对齐TranslationalModels(e.g.TransE)

MTransE(Chenet

al.,

2017)

IPTransE(Zhuet

al.,

2017)

JAPE(Sun

et

al.,2017),

AttrEAttrE(Trsedyaet

al.,2019)

MultiKE(Zhanget

al.,

2019)GNNs

GCN-Align(Wanget

al.,

2018)

RDGCN(Wuet

al.,

2019)

AVR-GCN(Yeet

al.,

2019)

AttrGNN(Liuet

al.,

2020)

NMN(Wu

et

al.,2020)

AliNet(Sun

et

al.,2020)EncoderEncoder基于GNNs的实体对齐[13]YutingWu,XiaoLiu,YansongFeng,ZhengWang,RuiYan,DongyanZhao.Relation-AwareEntity

AlignmentforHeterogeneousKnowledgeGraphs.IJCAI2019.[14]RuiYe,XinLi,YujieFang,HongyuZang,MingzhongWang.AVectorizedRelationalGraphConvolutionalNetworkforMulti-RelationalNetworkAlignment.IJCAI2019RDGCN[13]•构建关系图，基于实体向量计算关系向量•

将关系向量应用于实体特征的聚合AVR-GCN[14]•同时学习实体和关系的向量•GCN卷积操作同时应用于实体和关系n考虑关系类型在特征聚合中的作用AttrGNN[15]•按照实体的属性将知识图谱划分为四个子图（实体名、文字属性、数值属性、无属性）•四个GNN通道获取实体的向量表示，文字属性和数值属性采用BERT编码[15]ZhiyuanLiu,YixinCao,LiangmingPan,JuanziLi,ZhiyuanLiu,Tat-SengChua.

Exploring

and

Evaluating

Attributes,Values,

and

Structures

forEntity

Alignment.EMNLP2020基于GNNs的实体对齐n实体属性和实体关系相结合基于GNNs的实体对齐[16]YutingWu,XiaoLiu,YansongFeng,ZhengWang,DongyanZhao.NeighborhoodMatchingNetworkforEntity

Alignment.ACL2020[17]Zequn

Sun,ChengmingWang,WeiHu,MuhaoChen,JianDai,WeiZhang,YuzhongQu.Knowledge

Graph

AlignmentNetworkwith

GatedMulti-hopNeighborhood

Aggregation.

AAAI2020n改进邻域特征聚集方法AliNet[17]NMN[16]基于深度学习的实体对齐TranslationalModels(e.g.TransE)

MTransE(Chenet

al.,

2017)

IPTransE(Zhuet

al.,

2017)

JAPE(Sun

et

al.,2017),

AttrEAttrE(Trsedyaet

al.,2019)

MultiKE(Zhanget

al.,

2019)GNNs

GCN-Align(Wanget

al.,

2018)

RDGCN(Wuet

al.,

2019)

AVR-GCN(Yeet

al.,

2019)

AttrGNN(Liuet

al.,

2020)

NMN(Wu

et

al.,2020)

AliNet(Sun

et

al.,2020)EncoderEncoderG1

G2

PCG[18]Zhichun

Wang,JinjianYang,Xiaoju

Ye.

KnowledgeGraphAlignmentwith

Entity-Pair

Embedding.EMNLP

2020基于“实体对”

表示学习的对齐方法n

生成知识图谱的成对连接图（Pair-wiseconnectivitygraph

，

PCG），将实体对（Entity-pair）作为对象进行特征的学习n

基于实体对的特征判断其是否有等价关系AlignmentsEncoder基于“实体对”

表示学习的对齐方法r2

t1a1b1r1

t1

r2

t2a2b2

a3b3n生成PCG

PCG中的节点为实体对（两个来

自不同知识图谱的实体）

PCG中的边通过以下规则建立：b1t1b2a1r1a2[18]Zhichun

Wang,JinjianYang,Xiaoju

Ye.

KnowledgeGraphAlignmentwith

Entity-Pair

Embedding.EMNLP

2020〈a,r,b〉∈T

Λ〈a0

,r0

,b0〉∈

T0⇋今〈(a,

a0

),(r,

r0

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)〉∈

TG

=(E,

R,

A,

L,

T)G0

=

(E0,

R0

,

A0

,

L0

,

T0

)RuleforgeneratingPCGKGstobe

alignedPCGof

G

and

G,a2b3a3b2a1b2a2b1a1b3a3b1r1

t2a3b3G,Gr2t2基于“实体对”

表示学习的对齐方法hhh2I1IiIhih1h3h2l实体对名称相似度特征l基于卷积神经网络的属性相似度特征提取[18]Zhichun

Wang,JinjianYang,Xiaoju

Ye.

KnowledgeGraphAlignmentwith

Entity-Pair

Embedding.EMNLP

2020CNN-based

attribute

feature

extraction

GNN-based

feature

propagationl基于图神经网络的特征传递GNN

Layersei

∈

GA1

A2

A3

A4

…

AnAlignment

prediction/AAAA...AmI4I3I2I1IAttributesimilaritiesNamesimilaritiese

∈

GIiIh3I田xiziApproachesHDBPZH-

ENDBPJA-

ENDBPFR-

ENDBP-

WDDBP-

YGH@1

H@10

MRR@1H@10MRRH@1H@10MRRH@1H@10MRRH@1H@10MRRMTransE0.3080.6140.3640.2790.5750.3490.2440.5560.3350.2810.5200.3630.252

0.493

0.334IPTransE0.4060.7350.5160.3670.6930.4740.3330.6850.4510.3490.6380.4470.297

0.558

0.386BootEA0.6290.8480.7030.6220.8540.7010.6530.8740.7310.7480.8980.8010.761

0.894

0.808MuGNN0.4940.8440.6110.5010.8570.6210.4950.8700.6210.6160.8970.7140.741

0.937

0.810RDGCN0.7080.8460.7460.7670.8950.8120.8860.9570.911----

-

-AliNet0.5390.8260.6280.5490.8310.6450.5520.8520.6570.6900.9080.7660.786

0.943

0.841NAEA0.6500.8670.7200.6410.8720.7180.6730.8940.7520.7670.9170.8170.778

0.912

0.821JAPE0.4120.7450.4900.3630.6850.4760.3240.6670.4300.3180.5890.4110.236

0.484

0.320GCN-Align0.4130.7440.5490.3990.7450.5460.3750.7450.5320.5060.7720.6000.597

0.838

0.682MultiKE---------0.9140.9510.9280.880

0.953

0.906CEA0.787--0.863--0.972--0.998--0.999

-

-CNN0.6120.8400.6940.5690.8200.6570.7770.9300.8330.8400.9860.8970.780

0.975

0.854CNN+GAT0.7260.9160.8030.7640.9360.8360.7580.9600.8390.9450.9670.9550.980

0.999

0.988EPEA0.8850.9530.9110.9240.9690.9420.9550.9860.9670.9750.9810.9771.000

1.000

1.000基于“实体对”

表示学习的对齐方法[18]Zhichun

Wang,JinjianYang,Xiaoju

Ye.

KnowledgeGraphAlignmentwith

Entity-Pair

Embedding.EMNLP

2020n实验结果n现有的工作

TransE模型

V.SGNNs模型

结构信息

V.S.

结构信息+属性信息

实体EmbeddingV.S

实体对Embeddingn存在的挑战

如何处理大规模的知识图谱实体对齐

如何处理非对称的实体对齐

如何在种子结果较少、或没有种子结果情况下进

行实体对齐总结EncoderEncoderEncoderPCGn[1]Wang,Z.,Li,J.,Wang,Z.,&Tang,J.(2012).Cross-lingualknowledgelinkingacrosswikiknowledgebases.WWW.n[2]NentwigM,HartungM,NgongaNgomoAC,RahmE.Asurveyof

currentlinkdiscoveryframeworks.SemanticWeb.2017Jan

1;8(3):419-36.n[3]BordesA,UsunierN,Garcia-DuranA,WestonJ,YakhnenkoO.Translatingembeddingsformodelingmulti-relationaldata.InAdvancesinneuralinformationprocessingsystems2013(pp.2787-2795).n[4]WangZ,ZhangJ,FengJ,ChenZ.KnowledgeGraphEmbeddingbyTranslatingonHyperplanes.InAAAI2014Jul27(Vol.

14,pp.

1112-1119).n[5]LinY,LiuZ,SunM,LiuY,ZhuX.Learningentityandrelationembeddingsforknowledgegraphcompletion.InAAAI2015Jan25(Vol.

15,pp.2181-2187).n[6]MuhaoChen,YingtaoTian,MohanYang,andCarloZaniolo.Multilingualknowledgegraphembeddingsforcross-lingualknowledgealignment.InProceedingsof

the26thInternationalJointConferenceon

ArtificialIntelligence,pages

1511–1517,2017.n[7]ZequnSun,WeiHu,andChengkaiLi.Cross-lingualentityalignmentvia

jointattribute-preservingembedding.InInternationalSemanticWeb

Conference,pages

628–644.

Springer,2017.n[8]HaoZhu,RuobingXie,ZhiyuanLiu,andMaosongSun.Iterativeentityalignmentvia

jointknowledgeembeddings.InProceedingsof

the26thInternationalJo