学做网站论坛vip教程,2345浏览器免费网站,婚庆公司广告语,discuz 修改网站标题文章目录1.综述2.技术论文3.汇总3.1定义定义统一EA3.2 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#xff1a;成对实体对齐3.6.2 协同对齐#xff1a;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em…
文章目录1.综述2.技术论文3.汇总3.1定义定义统一EA3.2 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 DBP15k7.2EN-FR7.3 SRPRS7.4 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 Linkingentity disambiguationEntity resolutionentity matchingdeduplicationrecord linkage
EA
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PARISSIGMaAML有监督基于pre-defined mappings的半监督:bootstrappingself-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 PARISAgreement-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EAER–因为相同所以比较ER方法 Bechmarks: 语言内DBPedia DBP15KDWY15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 MRMRRHitsm:m1为precisionprecision/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 FBK15FBK15-237WN18WN18RR 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248035en: 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248991en: 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273825en: 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341770WD: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383757YG: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60800FR: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55580DE: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64021WD: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58853YG: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-FBAn 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127614FB: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)}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′)
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 embeddingtransEPath: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Membedding 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)(e1,e2)∈S′,则(e1,r1,e1′)−(e2,r1,e1′) learning 监督半监督无监督 Embedding transEGCN Alignment 2个向量映射到一个空间训练一个相同的向量TransitionCorpus-fusionMargin-basedGraph matchingAttribution refined Prediction 相似度计算 cosineeuclideanManhattan 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
4.开源代码
OpenEA 开源的embedding pipeline组件库
5.效果比较
EN-FRDBP15k zh-en/dbp15k fr-en/ja-en效果比较
6.使用场景
单语言/多语言稀疏/稠密大规模/中等规模1v1/多对多 1v1:BootEA
7. 实验效果
7.1 DBP15k
DBP15k DBP15k 组1仅用结构 组2用bootstrapping 组3其他信息
7.2EN-FR
EN-FR: A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphsh
7.3 SRPRS
SRPRS 组1仅用结构 组2用bootstrapping 组3其他信息
7.4 DWY100k
参考文献
部分参考未列出
A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphsh
