文章目录

1.综述
2.技术论文
3.汇总
- 3.1定义
- - 定义统一
  - EA
- 3.2 评价指标
- 3.3 数据集
- 3.4 数据预处理技术
- 3.5 索引
- 3.6 对齐
- - 3.6.1 按属性相似度/文本相似度做：成对实体对齐
  - 3.6.2 协同对齐：考虑不同实体间的关联
  - - 3.6.2.1 局部实体对齐
    - 3.6.2.2 全局实体对齐
  - 3.6.3 基于embedding的方法分类
4.开源代码
5.效果比较
6.使用场景
7. 实验效果
- 7.1 DBP15k
- 7.2EN-FR
- 7.3 SRPRS
- 7.4 DWY100k
参考文献

1.综述

embedding 方法

OpenEA: “A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs”.
Zequn Sun, Qingheng Zhang, Wei Hu, Chengming Wang, Muhao Chen, Farahnaz Akrami, Chengkai Li. PVLDB, vol. 13. ACM 2020 [paper][code][笔记]
"An Experimental Study of State-of-the-Art Entity Alignment Approaches".
Xiang Zhao, Weixin Zeng, Jiuyang Tang, Wei Wang, Fabian Suchanek. TKDE, 2020 [paper][笔记]

2.技术论文

实体对齐论文列表

JE: “A Joint Embedding Method for Entity Alignment of Knowledge Bases”.
Yanchao Hao, Yuanzhe Zhang, Shizhu He, Kang Liu, Jun Zhao. (CCKS 2016) [paper][code]
MTransE: “Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment”.
Muhao Chen, Yingtao Tian, Mohan Yang, Carlo Zaniolo. (IJCAI 2017) [paper][code]
JAPE: “Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding”.
Zequn Sun, Wei Hu, Chengkai Li. (ISWC 2017) [paper][code]
IPTransE: “Iterative Entity Alignment via Joint Knowledge Embeddings”.
Hao Zhu, Ruobing Xie, Zhiyuan Liu, Maosong Sun. (IJCAI 2017) [paper][code]
BootEA: “Bootstrapping Entity Alignment with Knowledge Graph Embedding”.
Zequn Sun, Wei Hu, Qingheng Zhang, Yuzhong Qu. (IJCAI 2018) [paper][code][笔记]
KDCoE: “Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment”.
Muhao Chen, Yingtao Tian, Kai-Wei Chang, Steven Skiena, Carlo Zaniolo. (IJCAI 2018) [paper][code]
NTAM: “Non-translational Alignment for Multi-relational Networks”.
Shengnan Li, Xin Li, Rui Ye, Mingzhong Wang, Haiping Su, Yingzi Ou. (IJCAI 2018) [paper][code]
**“LinkNBed: Multi-Graph Representation Learning with Entity Linkage”.""
Rakshit Trivedi, Bunyamin Sisman, Jun Ma, Christos Faloutsos, Hongyuan Zha, Xin Luna Dong (ACL 2018) [paper]
GCN-Align: “Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks”.
Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang. (EMNLP 2018) [paper][code]
AttrE: “Entity Alignment between Knowledge Graphs Using Attribute Embeddings”.
Bayu D. Trsedya, Jianzhong Qi, Rui Zhang. (AAAI 2019) [paper][code]
SEA: “Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference”.
Shichao Pei, Lu Yu, Robert Hoehndorf, Xiangliang Zhang. (WWW 2019) [paper][code]
RSN4EA: “Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs”.
Lingbing Guo, Zequn Sun, Wei Hu. (ICML 2019) [paper][code]
MuGNN: “Multi-Channel Graph Neural Network for Entity Alignment”.
Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua. (ACL 2019) [paper][code]
GMNN: “Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network”.
Kun Xu, Liwei Wang, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, Dong Yu. (ACL 2019) [paper][code]
MultiKE: “Multi-view Knowledge Graph Embedding for Entity Alignment”.
Qingheng Zhang, Zequn Sun, Wei Hu, Muhao Chen, Lingbing Guo, Yuzhong Qu. (IJCAI 2019) [paper][code]
RDGCN: “Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs”.
Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, Dongyan Zhao. (IJCAI 2019) [paper][code]
OTEA: “Improving Cross-lingual Entity Alignment via Optimal Transport”.
Shichao Pei, Lu Yu, Xiangliang Zhang. (IJCAI 2019) [paper][code]
NAEA: “Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs”.
Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, Li Guo. (IJCAI 2019) [paper][code]
AVR-GCN: “A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment”.
Rui Ye, Xin Li, Yujie Fang, Hongyu Zang, Mingzhong Wang. (IJCAI 2019) [paper][code]
TransEdge: “TransEdge: Translating Relation-Contextualized Embeddings for Knowledge Graphs”.
Zequn Sun, Jiacheng Huang, Wei Hu, Muhao Chen, Lingbing Guo, Yuzhong Qu. (ISWC 2019) [paper][code]
KECG: “Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model”.
Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua. (EMNLP 2019) [paper][code]
HGCN: “Jointly Learning Entity and Relation Representations for Entity Alignment”.
Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. (EMNLP 2019) [paper][code]
MMEA: “Modeling Multi-mapping relations for Precise Cross-lingual Entity Alignment”.
Xiaofei Shi, Yanghua Xiao. (EMNLP 2019) [paper][code]
HMAN: “Aligning Cross-lingual Entities with Multi-Aspect Information”.
Hsiu-Wei Yang, Yanyan Zou, Peng Shi, Wei Lu, Jimmy Lin, Xu Sun. (EMNLP 2019) [paper][code]
AKE: “Guiding Cross-lingual Entity Alignment via Adversarial Knowledge Embedding”.
Xixun Lin, Hong Yang, Jia Wu, Chuan Zhou, Bin Wang. (ICDM 2019) [paper][code]
MRAEA: “MRAEA: An Efficient and Robust Cross-lingual Entity Alignment Approach via Meta Relation Aware Representation”.
Xin Mao, Wenting Wang, Huimin Xu, Man Lan, Yuanbin Wu. (WSDM 2020) [paper][code]
AliNet: “Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation”.
Zequn Sun, Chengming Wang, Wei Hu, Muhao Chen, Jian Dai, Wei Zhang, Yuzhong Qu. (AAAI 2020) [paper][code]
"Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment".
Kun Xu, Linfeng Song, Yansong Feng, Yan Song, Dong Yu. (AAAI 2020) [paper][code]
COTSAE: “COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment”.
Kai Yang, Shaoqin Liu, Junfeng Zhao, Yasha Wang, Bing Xie. (AAAI 2020) [paper][code]
CEAFF: “Collective Embedding-based Entity Alignment via Adaptive Features”.
Weixin Zeng, Xiang Zhao, Jiuyang Tang, Xuemin Lin. (ICDE 2020) [paper][code]
"Deep Graph Matching Consensus".
Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege. (ICLR 2020) [paper][code]
CG-MuAlign: “Collective Multi-type Entity Alignment Between Knowledge Graphs”.
Qi Zhu, Hao Wei, Bunyamin Sisman, Da Zheng, Christos Faloutsos, Xin Luna Dong, Jiawei Han. (WWW 2020) [paper][code]
JarKA: “JarKA: Modeling Attribute Interactions for Cross-lingual Knowledge Alignment”.
Bo Chen, Jing Zhang, Xiaobin Tang, Hong Chen, Cuiping Li. (PAKDD 2020) [paper][code]
NMN: “Neighborhood Matching Network for Entity Alignment”.
Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. (ACL 2020) [paper][code]
BERT-INT: “BERT-INT: A BERT-based Interaction Model For Knowledge Graph Alignment”.
Xiaobin Tang, Jing Zhang, Bo Chen, Yang Yang, Hong Chen, Cuiping Li. (IJCAI 2020) [paper][code]
SSP: “Global Structure and Local Semantics-Preserved Embeddings for Entity Alignment”.
Hao Nie, Xianpei Han, Le Sun, Chi Man Wong, Qiang Chen, Suhui Wu, Wei Zhang. (IJCAI 2020) [paper][code]
DAT: “Degree-Aware Alignment for Entities in Tail”.
Weixin Zeng, Xiang Zhao, Wei Wang, Jiuyang Tang, Zhen Tan. (SIGIR 2020) [paper][code]
RREA: “Relational Reflection Entity Alignment”.
Xin Mao, Wenting Wang, Huimin Xu, Yuanbin Wu, Man Lan. (CIKM 2020) [paper][code]
REA: “REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs”.
Shichao Pei, Lu Yu, Guoxian Yu, Xiangliang Zhang. (KDD 2020) [paper][code]
HyperKA: “Knowledge Association with Hyperbolic Knowledge Graph Embeddings”.
Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, Wei Zhang. (EMNLP 2020) [paper][code]
AttrGNN: “Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment”.
Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, Zhiyuan Liu, Tat-Seng Chua. (EMNLP 2020) [paper][code]
EPEA: “Knowledge Graph Alignment with Entity-Pair Embedding”.
Zhichun Wang, Jinjian Yang, Xiaoju Ye. (EMNLP 2020) [paper]
"Learning Short-Term Differences and Long-Term Dependencies for Entity Alignment".
Jia Chen, Zhixu Li, Pengpeng Zhao, An Liu, Lei Zhao, Zhigang Chen, Xiangliang Zhang. (ISWC 2020) [paper]
"Visual Pivoting for (Unsupervised) Entity Alignment".
Fangyu Liu, Muhao Chen, Dan Roth, Nigel Collier. (AAAI 2021) [paper][code]
DINGAL: “Dynamic Knowledge Graph Alignment”.
Yuchen Yan, Lihui Liu, Yikun Ban, Baoyu Jing, Hanghang Tong. (AAAI 2021) [paper]
RNM: “Relation-Aware Neighborhood Matching Model for Entity Alignment”.
Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yingpeng Du. (AAAI 2021) [paper]
"Cross-lingual Entity Alignment with Incidental Supervision".
Muhao Chen, Weijia Shi, Ben Zhou, Dan Roth. (EACL 2021) [paper][code]
"Active Learning for Entity Alignment".
Max Berrendorf, Evgeniy Faerman, Volker Tresp. (ECIR 2021) [paper]
Dual-AMN: “Boosting the Speed of Entity Alignment 10×: Dual Attention Matching Network with Normalized Hard Sample Mining”.
Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan. (WWW 2021) [paper][code]1. JE: “A Joint Embedding Method for Entity Alignment of Knowledge Bases”.
Yanchao Hao, Yuanzhe Zhang, Shizhu He, Kang Liu, Jun Zhao. (CCKS 2016) [paper][code]
MTransE: “Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment”.
Muhao Chen, Yingtao Tian, Mohan Yang, Carlo Zaniolo. (IJCAI 2017) [paper][code]
JAPE: “Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding”.
Zequn Sun, Wei Hu, Chengkai Li. (ISWC 2017) [paper][code]
IPTransE: “Iterative Entity Alignment via Joint Knowledge Embeddings”.
Hao Zhu, Ruobing Xie, Zhiyuan Liu, Maosong Sun. (IJCAI 2017) [paper][code]
BootEA: “Bootstrapping Entity Alignment with Knowledge Graph Embedding”.
Zequn Sun, Wei Hu, Qingheng Zhang, Yuzhong Qu. (IJCAI 2018) [paper][code]
KDCoE: “Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment”.
Muhao Chen, Yingtao Tian, Kai-Wei Chang, Steven Skiena, Carlo Zaniolo. (IJCAI 2018) [paper][code]
NTAM: “Non-translational Alignment for Multi-relational Networks”.
Shengnan Li, Xin Li, Rui Ye, Mingzhong Wang, Haiping Su, Yingzi Ou. (IJCAI 2018) [paper][code]
**“LinkNBed: Multi-Graph Representation Learning with Entity Linkage”.""
Rakshit Trivedi, Bunyamin Sisman, Jun Ma, Christos Faloutsos, Hongyuan Zha, Xin Luna Dong (ACL 2018) [paper]
GCN-Align: “Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks”.
Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang. (EMNLP 2018) [paper][code]
AttrE: “Entity Alignment between Knowledge Graphs Using Attribute Embeddings”.
Bayu D. Trsedya, Jianzhong Qi, Rui Zhang. (AAAI 2019) [paper][code]
SEA: “Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference”.
Shichao Pei, Lu Yu, Robert Hoehndorf, Xiangliang Zhang. (WWW 2019) [paper][code]
RSN4EA: “Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs”.
Lingbing Guo, Zequn Sun, Wei Hu. (ICML 2019) [paper][code]
MuGNN: “Multi-Channel Graph Neural Network for Entity Alignment”.
Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua. (ACL 2019) [paper][code]
GMNN: “Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network”.
Kun Xu, Liwei Wang, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, Dong Yu. (ACL 2019) [paper][code]
MultiKE: “Multi-view Knowledge Graph Embedding for Entity Alignment”.
Qingheng Zhang, Zequn Sun, Wei Hu, Muhao Chen, Lingbing Guo, Yuzhong Qu. (IJCAI 2019) [paper][code]
RDGCN: “Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs”.
Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, Dongyan Zhao. (IJCAI 2019) [paper][code]
OTEA: “Improving Cross-lingual Entity Alignment via Optimal Transport”.
Shichao Pei, Lu Yu, Xiangliang Zhang. (IJCAI 2019) [paper][code]
NAEA: “Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs”.
Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, Li Guo. (IJCAI 2019) [paper][code]
AVR-GCN: “A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment”.
Rui Ye, Xin Li, Yujie Fang, Hongyu Zang, Mingzhong Wang. (IJCAI 2019) [paper][code]
TransEdge: “TransEdge: Translating Relation-Contextualized Embeddings for Knowledge Graphs”.
Zequn Sun, Jiacheng Huang, Wei Hu, Muhao Chen, Lingbing Guo, Yuzhong Qu. (ISWC 2019) [paper][code]
KECG: “Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model”.
Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua. (EMNLP 2019) [paper][code]
HGCN: “Jointly Learning Entity and Relation Representations for Entity Alignment”.
Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. (EMNLP 2019) [paper][code]
MMEA: “Modeling Multi-mapping relations for Precise Cross-lingual Entity Alignment”.
Xiaofei Shi, Yanghua Xiao. (EMNLP 2019) [paper][code]
HMAN: “Aligning Cross-lingual Entities with Multi-Aspect Information”.
Hsiu-Wei Yang, Yanyan Zou, Peng Shi, Wei Lu, Jimmy Lin, Xu Sun. (EMNLP 2019) [paper][code]
AKE: “Guiding Cross-lingual Entity Alignment via Adversarial Knowledge Embedding”.
Xixun Lin, Hong Yang, Jia Wu, Chuan Zhou, Bin Wang. (ICDM 2019) [paper][code]
MRAEA: “MRAEA: An Efficient and Robust Cross-lingual Entity Alignment Approach via Meta Relation Aware Representation”.
Xin Mao, Wenting Wang, Huimin Xu, Man Lan, Yuanbin Wu. (WSDM 2020) [paper][code]
AliNet: “Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation”.
Zequn Sun, Chengming Wang, Wei Hu, Muhao Chen, Jian Dai, Wei Zhang, Yuzhong Qu. (AAAI 2020) [paper][code]
"Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment".
Kun Xu, Linfeng Song, Yansong Feng, Yan Song, Dong Yu. (AAAI 2020) [paper][code]
COTSAE: “COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment”.
Kai Yang, Shaoqin Liu, Junfeng Zhao, Yasha Wang, Bing Xie. (AAAI 2020) [paper][code]
CEAFF: “Collective Embedding-based Entity Alignment via Adaptive Features”.
Weixin Zeng, Xiang Zhao, Jiuyang Tang, Xuemin Lin. (ICDE 2020) [paper][code]
"Deep Graph Matching Consensus".
Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege. (ICLR 2020) [paper][code]
CG-MuAlign: “Collective Multi-type Entity Alignment Between Knowledge Graphs”.
Qi Zhu, Hao Wei, Bunyamin Sisman, Da Zheng, Christos Faloutsos, Xin Luna Dong, Jiawei Han. (WWW 2020) [paper][code]
JarKA: “JarKA: Modeling Attribute Interactions for Cross-lingual Knowledge Alignment”.
Bo Chen, Jing Zhang, Xiaobin Tang, Hong Chen, Cuiping Li. (PAKDD 2020) [paper][code]
NMN: “Neighborhood Matching Network for Entity Alignment”.
Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. (ACL 2020) [paper][code]
BERT-INT: “BERT-INT: A BERT-based Interaction Model For Knowledge Graph Alignment”.
Xiaobin Tang, Jing Zhang, Bo Chen, Yang Yang, Hong Chen, Cuiping Li. (IJCAI 2020) [paper][code]
SSP: “Global Structure and Local Semantics-Preserved Embeddings for Entity Alignment”.
Hao Nie, Xianpei Han, Le Sun, Chi Man Wong, Qiang Chen, Suhui Wu, Wei Zhang. (IJCAI 2020) [paper][code]
DAT: “Degree-Aware Alignment for Entities in Tail”.
Weixin Zeng, Xiang Zhao, Wei Wang, Jiuyang Tang, Zhen Tan. (SIGIR 2020) [paper][code]
RREA: “Relational Reflection Entity Alignment”.
Xin Mao, Wenting Wang, Huimin Xu, Yuanbin Wu, Man Lan. (CIKM 2020) [paper][code]
REA: “REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs”.
Shichao Pei, Lu Yu, Guoxian Yu, Xiangliang Zhang. (KDD 2020) [paper][code]
HyperKA: “Knowledge Association with Hyperbolic Knowledge Graph Embeddings”.
Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, Wei Zhang. (EMNLP 2020) [paper][code]
AttrGNN: “Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment”.
Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, Zhiyuan Liu, Tat-Seng Chua. (EMNLP 2020) [paper][code]
EPEA: “Knowledge Graph Alignment with Entity-Pair Embedding”.
Zhichun Wang, Jinjian Yang, Xiaoju Ye. (EMNLP 2020) [paper]
"Learning Short-Term Differences and Long-Term Dependencies for Entity Alignment".
Jia Chen, Zhixu Li, Pengpeng Zhao, An Liu, Lei Zhao, Zhigang Chen, Xiangliang Zhang. (ISWC 2020) [paper]
"Visual Pivoting for (Unsupervised) Entity Alignment".
Fangyu Liu, Muhao Chen, Dan Roth, Nigel Collier. (AAAI 2021) [paper][code]
DINGAL: “Dynamic Knowledge Graph Alignment”.
Yuchen Yan, Lihui Liu, Yikun Ban, Baoyu Jing, Hanghang Tong. (AAAI 2021) [paper]
RNM: “Relation-Aware Neighborhood Matching Model for Entity Alignment”.
Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yingpeng Du. (AAAI 2021) [paper]
"Cross-lingual Entity Alignment with Incidental Supervision".
Muhao Chen, Weijia Shi, Ben Zhou, Dan Roth. (EACL 2021) [paper][code]
"Active Learning for Entity Alignment".
Max Berrendorf, Evgeniy Faerman, Volker Tresp. (ECIR 2021) [paper]
Dual-AMN: “Boosting the Speed of Entity Alignment 10×: Dual Attention Matching Network with Normalized Hard Sample Mining”.
Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan. (WWW 2021) [paper][code]

3.汇总

3.1定义

匹配两个KG或一个KG内指向同一物理对象，合并向同时提

定义统一

Entity Linking=entity disambiguation
Entity resolution=entity matching=deduplication=record linkage

EA

分类：
- Scope:
  - entity alignment<-本文只考虑这个
  - relation
  - 类别对齐：class of taxonomies of two KGs
  - 方法：有一次性执行三种任务的joint model
- Background knowledge
  - OAEI:使用ontology(T-box)作为背景信息
  - 另一种：不使用ontology的方法
- Training
  - 无监督：PARIS，SIGMa，AML
  - 有监督：基于pre-defined mappings的
  - 半监督:bootstrapping（self-training,co-training)
EA with deep leaning:
- 基于graph representation learning technologies
  - 建模KG结构
  - 生成实体嵌入
比较
- 无监督
  - PARIS
  - Agreement-MakerLight(AML):使用背景信息(本体）
- ER方法：基于名称的启发式方法
  - goal相同：EA=ER–因为相同所以比较ER方法
Bechmarks:
- 语言内+DBPedia
  - DBP15K
  - DWY15
  - 问题：现有的Bechmarks，只包含schema和instance信息。对不假设有可用的本体的EA方法来说。–所以本文不介绍本体？
PS:
- OAEI:推广了KG track
- 不公平

3.2 评价指标

对齐质量：准确性和全面性
- MR
- MRR
- Hits@m:m=1为precision
- precision/recall/f1
  - 传统方法再用
对齐效率：分区索引技术对候选匹配对的筛选能力和准确性
- 缩减率
- 候选对完整性
- 候选对质量

3.3 数据集

Embedding数据集
- FBK15
- FBK15-237
- WN18
- WN18RR
传统实体对齐数据集：
- OAEI(since 2004）
embedding实体对齐数据集
- DBP15K：
  - 跨语言:
    - zh-en,
      - zh:关系三元组数：70414，关系数1701，属性三元组数：248035
      - en: 关系三元组数：95142，关系数1323，属性三元组数：343218
    - ja-en,
      - ja:关系三元组数：77214，关系数1299，属性三元组数：248991
      - en: 关系三元组数：93484，关系数1153，属性三元组数：320616
    - fr-en
      - fr:关系三元组数：105998，关系数903，属性三元组数：273825
      - en: 关系三元组数：115722，关系数1208，属性三元组数：351094
  - 实体对齐连接数：15k（每对语言间）
  - 度的分布：大多在1，从2-10,度越大，实体数量下降
  - DBPedia
- WK3L
- DWY100K：
  - 每个KG实体数：100k
  - 单语言：
    - DBP-WD,
      - DBP:关系三元组数：463294，关系数330，属性三元组数：341770
      - WD:关系三元组数：448774，关系数220，属性三元组数：779402
    - DBP-YG
      - DBP:关系三元组数：428952，关系数302，属性三元组数：383757
      - YG:关系三元组数：502563，关系数31，属性三元组数：98028
    - (DBP:DBPedia,YG:Yago3,WD:wikidata)
  - 每对有100k个实体对齐连接
  - 度的分布：没有度为1or2的，峰值在4，之后递减
- SRPRS
  - 认为以前的数据集太稠密了（DBP,DWY),度的分布偏离现实
  - 跨语言：
    - EN-FR,
      - EN:关系三元组数：36508，关系数221，属性三元组数：60800
      - FR:关系三元组数：33532，关系数177，属性三元组数：53045
    - EN-DE
      - EN:关系三元组数：38363，关系数220，属性三元组数：55580
      - DE:关系三元组数：37377，关系数120，属性三元组数：73753
  - 单语言：
    - DBP-WD,
      - DBP:关系三元组数：33421，关系数253，属性三元组数：64021
      - WD:关系三元组数：40159，关系数144，属性三元组数：133371
    - DBP-YG
      - DBP:关系三元组数：33748，关系数223，属性三元组数：58853
      - YG:关系三元组数：36569，关系数30，属性三元组数：18241
  - 每种有15k个实体对齐连接
  - 度的分布：很现实
    - 度小的实体多（精心取样）
- EN-FR
- DBP-FB（An Experimental Study of State-of-the-Art Entity Alignment Approaches）
  - DBP: 关系三元组数：96414，关系数407，属性三元组数：127614
  - FB:关系三元组数：111974，关系数882，属性三元组数：78740
度的分布
EN-FR的统计

3.4 数据预处理技术

3.5 索引

分区索引：过滤掉不可能匹配的实体对，降低计算复杂度，避免数据库规模二次增长

3.6 对齐

3.6.1 按属性相似度/文本相似度做：成对实体对齐

传统概率模型：
- 基于属性相似度评分–>三分类：匹配，可能匹配，不匹配
- 也可用01
机器学习的模型
- 根据实体属性构建向量
- 方法：决策树、SVM等分类模型
- 优点：自动拟合属性间的组合关系和对应程度，减少人为介入
- 可引入无监督、半监督
文本匹配/语义匹配
- 文本特征明显的实体匹配
- 实体简介很长的那种
- Bert什么的

3.6.2 协同对齐：考虑不同实体间的关联

在属性相似度基础上考虑了结构相似度

3.6.2.1 局部实体对齐

计算相似度
- 考虑邻居的属性（带匹配实体对的邻居属性集合）
- 但不把邻居节点当做平等的实体去计算结构相似性
- 计算
  - $sim(ei,ej)=α⋅simattr(ei,ej)+(1−α)⋅simNB(ei,ej)实体本身的相似度：simattr(ei,ej)=Σ(a1,a2)∈Attr(ei,ej)sim(a1,a2)实体关联实体相似度simNB(ei,ej)=Σ(ei′,ej′)∈NB(ei,ej)simattr(ei′,ej′)sim(e_i,e_j)=\alpha \cdot sim_{attr}(e_i,e_j)+(1-\alpha)\cdot sim_{NB}(e_i,e_j)\\ 实体本身的相似度：sim_{attr}(e_i,e_j)=\Sigma_{(a_1,a_2)\in Attr(e_i,e_j)}sim(a_1,a_2)\\ 实体关联实体相似度sim_{NB}(e_i,e_j)=\Sigma_{(e_i',e_j')\in NB(e_i,e_j) sim_{attr}(e_i',e_j')}$

3.6.2.2 全局实体对齐

通过不同匹配策略之间相互影响调整实体之间的相似度
基于相似度传播的方法
- 基本思想：通过seed alignment以bootstrapping的方式迭代的产生一些新的匹配
- 半监督？
基于概率模型的方法
- 基本思想：全局概率最大化。通过为实体匹配关系和匹配决策决策复杂的概率模型，来避免bootstrapping–需要人工参与
- 基本方法：贝叶斯网络/LDA/CRF/Markov

3.6.3 基于embedding的方法分类

A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
<-分类来源

Embedding Module
- 关系嵌入
  - triple embedding：transE
  - Path:路径上的长期依赖
  - Neighbor:GCN
- 属性嵌入
  - 属性
  - literal
Interaction Mode
- Combination mode
  - transformation：学习映射M
  - embedding space Calibration：嵌入到同一空间
  - parameter sharing：同一向量表示
  - parameter swapping： $(e1,e2)∈S′,则(e1,r1,e1′)−>(e2,r1,e1′)(e_1,e_2)\in S',则(e_1,r_1,e_1')->(e_2,r_1,e_1')$
- learning
  - 监督
  - 半监督
  - 无监督
Embedding
- transE
- GCN
Alignment
- 2个向量映射到一个空间
- 训练一个相同的向量
- Transition
- Corpus-fusion
- Margin-based
- Graph matching
- Attribution refined
Prediction：
- 相似度计算：
  - cosine
  - euclidean
  - Manhattan distance
Extra information Module
- 用以增强EA
- 方法
  - bootstrapping(or self-learning:
    - 利用置信度高的对齐结果加入训练数据（下个iteration）
  - multi-type literal information
    - 属性
    - 实体描述
    - 实体名
    - 完善KG的结构
模块级别的比较
- 在个模块下介绍各方法如何实现该模块