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实体对齐汇总

• 人工智能 • 阅读 94

文章目录

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

  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]
  2. MTransE: “Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment”.
    Muhao Chen, Yingtao Tian, Mohan Yang, Carlo Zaniolo. (IJCAI 2017) [paper][code]
  3. JAPE: “Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding”.
    Zequn Sun, Wei Hu, Chengkai Li. (ISWC 2017) [paper][code]
  4. IPTransE: “Iterative Entity Alignment via Joint Knowledge Embeddings”.
    Hao Zhu, Ruobing Xie, Zhiyuan Liu, Maosong Sun. (IJCAI 2017) [paper][code]
  5. BootEA: “Bootstrapping Entity Alignment with Knowledge Graph Embedding”.
    Zequn Sun, Wei Hu, Qingheng Zhang, Yuzhong Qu. (IJCAI 2018) [paper][code][笔记]
  6. 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]
  7. NTAM: “Non-translational Alignment for Multi-relational Networks”.
    Shengnan Li, Xin Li, Rui Ye, Mingzhong Wang, Haiping Su, Yingzi Ou. (IJCAI 2018) [paper][code]
  8. **”LinkNBed: Multi-Graph Representation Learning with Entity Linkage”.””
    Rakshit Trivedi, Bunyamin Sisman, Jun Ma, Christos Faloutsos, Hongyuan Zha, Xin Luna Dong (ACL 2018) [paper]
  9. GCN-Align: “Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks”.
    Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang. (EMNLP 2018) [paper][code]
  10. AttrE: “Entity Alignment between Knowledge Graphs Using Attribute Embeddings”.
    Bayu D. Trsedya, Jianzhong Qi, Rui Zhang. (AAAI 2019) [paper][code]
  11. 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]
  12. RSN4EA: “Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs”.
    Lingbing Guo, Zequn Sun, Wei Hu. (ICML 2019) [paper][code]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. OTEA: “Improving Cross-lingual Entity Alignment via Optimal Transport”.
    Shichao Pei, Lu Yu, Xiangliang Zhang. (IJCAI 2019) [paper][code]
  18. NAEA: “Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs”.
    Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, Li Guo. (IJCAI 2019) [paper][code]
  19. 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]
  20. 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]
  21. 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]
  22. HGCN: “Jointly Learning Entity and Relation Representations for Entity Alignment”.
    Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. (EMNLP 2019) [paper][code]
  23. MMEA: “Modeling Multi-mapping relations for Precise Cross-lingual Entity Alignment”.
    Xiaofei Shi, Yanghua Xiao. (EMNLP 2019) [paper][code]
  24. 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]
  25. AKE: “Guiding Cross-lingual Entity Alignment via Adversarial Knowledge Embedding”.
    Xixun Lin, Hong Yang, Jia Wu, Chuan Zhou, Bin Wang. (ICDM 2019) [paper][code]
  26. 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]
  27. 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]
  28. “Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment”.
    Kun Xu, Linfeng Song, Yansong Feng, Yan Song, Dong Yu. (AAAI 2020) [paper][code]
  29. 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]
  30. CEAFF: “Collective Embedding-based Entity Alignment via Adaptive Features”.
    Weixin Zeng, Xiang Zhao, Jiuyang Tang, Xuemin Lin. (ICDE 2020) [paper][code]
  31. “Deep Graph Matching Consensus”.
    Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege. (ICLR 2020) [paper][code]
  32. 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]
  33. JarKA: “JarKA: Modeling Attribute Interactions for Cross-lingual Knowledge Alignment”.
    Bo Chen, Jing Zhang, Xiaobin Tang, Hong Chen, Cuiping Li. (PAKDD 2020) [paper][code]
  34. NMN: “Neighborhood Matching Network for Entity Alignment”.
    Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. (ACL 2020) [paper][code]
  35. 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]
  36. 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]
  37. DAT: “Degree-Aware Alignment for Entities in Tail”.
    Weixin Zeng, Xiang Zhao, Wei Wang, Jiuyang Tang, Zhen Tan. (SIGIR 2020) [paper][code]
  38. RREA: “Relational Reflection Entity Alignment”.
    Xin Mao, Wenting Wang, Huimin Xu, Yuanbin Wu, Man Lan. (CIKM 2020) [paper][code]
  39. REA: “REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs”.
    Shichao Pei, Lu Yu, Guoxian Yu, Xiangliang Zhang. (KDD 2020) [paper][code]
  40. HyperKA: “Knowledge Association with Hyperbolic Knowledge Graph Embeddings”.
    Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, Wei Zhang. (EMNLP 2020) [paper][code]
  41. 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]
  42. EPEA: “Knowledge Graph Alignment with Entity-Pair Embedding”.
    Zhichun Wang, Jinjian Yang, Xiaoju Ye. (EMNLP 2020) [paper]
  43. “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]
  44. “Visual Pivoting for (Unsupervised) Entity Alignment”.
    Fangyu Liu, Muhao Chen, Dan Roth, Nigel Collier. (AAAI 2021) [paper][code]
  45. DINGAL: “Dynamic Knowledge Graph Alignment”.
    Yuchen Yan, Lihui Liu, Yikun Ban, Baoyu Jing, Hanghang Tong. (AAAI 2021) [paper]
  46. RNM: “Relation-Aware Neighborhood Matching Model for Entity Alignment”.
    Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yingpeng Du. (AAAI 2021) [paper]
  47. “Cross-lingual Entity Alignment with Incidental Supervision”.
    Muhao Chen, Weijia Shi, Ben Zhou, Dan Roth. (EACL 2021) [paper][code]
  48. “Active Learning for Entity Alignment”.
    Max Berrendorf, Evgeniy Faerman, Volker Tresp. (ECIR 2021) [paper]
  49. 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]
  50. MTransE: “Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment”.
    Muhao Chen, Yingtao Tian, Mohan Yang, Carlo Zaniolo. (IJCAI 2017) [paper][code]
  51. JAPE: “Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding”.
    Zequn Sun, Wei Hu, Chengkai Li. (ISWC 2017) [paper][code]
  52. IPTransE: “Iterative Entity Alignment via Joint Knowledge Embeddings”.
    Hao Zhu, Ruobing Xie, Zhiyuan Liu, Maosong Sun. (IJCAI 2017) [paper][code]
  53. BootEA: “Bootstrapping Entity Alignment with Knowledge Graph Embedding”.
    Zequn Sun, Wei Hu, Qingheng Zhang, Yuzhong Qu. (IJCAI 2018) [paper][code]
  54. 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]
  55. NTAM: “Non-translational Alignment for Multi-relational Networks”.
    Shengnan Li, Xin Li, Rui Ye, Mingzhong Wang, Haiping Su, Yingzi Ou. (IJCAI 2018) [paper][code]
  56. **”LinkNBed: Multi-Graph Representation Learning with Entity Linkage”.””
    Rakshit Trivedi, Bunyamin Sisman, Jun Ma, Christos Faloutsos, Hongyuan Zha, Xin Luna Dong (ACL 2018) [paper]
  57. GCN-Align: “Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks”.
    Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang. (EMNLP 2018) [paper][code]
  58. AttrE: “Entity Alignment between Knowledge Graphs Using Attribute Embeddings”.
    Bayu D. Trsedya, Jianzhong Qi, Rui Zhang. (AAAI 2019) [paper][code]
  59. 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]
  60. RSN4EA: “Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs”.
    Lingbing Guo, Zequn Sun, Wei Hu. (ICML 2019) [paper][code]
  61. 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]
  62. 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]
  63. 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]
  64. 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]
  65. OTEA: “Improving Cross-lingual Entity Alignment via Optimal Transport”.
    Shichao Pei, Lu Yu, Xiangliang Zhang. (IJCAI 2019) [paper][code]
  66. NAEA: “Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs”.
    Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, Li Guo. (IJCAI 2019) [paper][code]
  67. 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]
  68. 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]
  69. 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]
  70. HGCN: “Jointly Learning Entity and Relation Representations for Entity Alignment”.
    Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. (EMNLP 2019) [paper][code]
  71. MMEA: “Modeling Multi-mapping relations for Precise Cross-lingual Entity Alignment”.
    Xiaofei Shi, Yanghua Xiao. (EMNLP 2019) [paper][code]
  72. 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]
  73. AKE: “Guiding Cross-lingual Entity Alignment via Adversarial Knowledge Embedding”.
    Xixun Lin, Hong Yang, Jia Wu, Chuan Zhou, Bin Wang. (ICDM 2019) [paper][code]
  74. 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]
  75. 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]
  76. “Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment”.
    Kun Xu, Linfeng Song, Yansong Feng, Yan Song, Dong Yu. (AAAI 2020) [paper][code]
  77. 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]
  78. CEAFF: “Collective Embedding-based Entity Alignment via Adaptive Features”.
    Weixin Zeng, Xiang Zhao, Jiuyang Tang, Xuemin Lin. (ICDE 2020) [paper][code]
  79. “Deep Graph Matching Consensus”.
    Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege. (ICLR 2020) [paper][code]
  80. 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]
  81. JarKA: “JarKA: Modeling Attribute Interactions for Cross-lingual Knowledge Alignment”.
    Bo Chen, Jing Zhang, Xiaobin Tang, Hong Chen, Cuiping Li. (PAKDD 2020) [paper][code]
  82. NMN: “Neighborhood Matching Network for Entity Alignment”.
    Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. (ACL 2020) [paper][code]
  83. 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]
  84. 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]
  85. DAT: “Degree-Aware Alignment for Entities in Tail”.
    Weixin Zeng, Xiang Zhao, Wei Wang, Jiuyang Tang, Zhen Tan. (SIGIR 2020) [paper][code]
  86. RREA: “Relational Reflection Entity Alignment”.
    Xin Mao, Wenting Wang, Huimin Xu, Yuanbin Wu, Man Lan. (CIKM 2020) [paper][code]
  87. REA: “REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs”.
    Shichao Pei, Lu Yu, Guoxian Yu, Xiangliang Zhang. (KDD 2020) [paper][code]
  88. HyperKA: “Knowledge Association with Hyperbolic Knowledge Graph Embeddings”.
    Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, Wei Zhang. (EMNLP 2020) [paper][code]
  89. 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]
  90. EPEA: “Knowledge Graph Alignment with Entity-Pair Embedding”.
    Zhichun Wang, Jinjian Yang, Xiaoju Ye. (EMNLP 2020) [paper]
  91. “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]
  92. “Visual Pivoting for (Unsupervised) Entity Alignment”.
    Fangyu Liu, Muhao Chen, Dan Roth, Nigel Collier. (AAAI 2021) [paper][code]
  93. DINGAL: “Dynamic Knowledge Graph Alignment”.
    Yuchen Yan, Lihui Liu, Yikun Ban, Baoyu Jing, Hanghang Tong. (AAAI 2021) [paper]
  94. RNM: “Relation-Aware Neighborhood Matching Model for Entity Alignment”.
    Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yingpeng Du. (AAAI 2021) [paper]
  95. “Cross-lingual Entity Alignment with Incidental Supervision”.
    Muhao Chen, Weijia Shi, Ben Zhou, Dan Roth. (EACL 2021) [paper][code]
  96. “Active Learning for Entity Alignment”.
    Max Berrendorf, Evgeniy Faerman, Volker Tresp. (ECIR 2021) [paper]
  97. 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

EA

  • 分类：
  • Scope:
    • entity alignment
  • 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
      实体对齐汇总
      实体对齐汇总
  • EN-FR的统计
    实体对齐汇总

; 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
    • s i m ( e i , e j ) = α ⋅ s i m a t t r ( e i , e j ) + ( 1 − α ) ⋅ s i m N B ( e i , e j ) 实 体 本 身 的 相 似 度 ： s i m a t t r ( e i , e j ) = Σ ( a 1 , a 2 ) ∈ A t t r ( e i , e j ) s i m ( a 1 , a 2 ) 实 体 关 联 实 体 相 似 度 s i m N B ( e i , e j ) = Σ ( e i ′ , e j ′ ) ∈ N B ( e i , e j ) s i m a t t r ( e i ′ , e j ′ ) 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’)}s i m (e i ​,e j ​)=α⋅s i m a t t r ​(e i ​,e j ​)+(1 −α)⋅s i m N B ​(e i ​,e j ​)实体本身的相似度：s i m a t t r ​(e i ​,e j ​)=Σ(a 1 ​,a 2 ​)∈A t t r (e i ​,e j ​)​s i m (a 1 ​,a 2 ​)实体关联实体相似度s i m N B ​(e i ​,e j ​)=Σ(e i ′​,e j ′​)∈N B (e i ​,e j ​)s i m a t t r ​(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

Original: https://blog.csdn.net/weixin_40485502/article/details/116782987
Author: 叶落叶子
Title: 实体对齐汇总

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