Entity Alignment : : Concepts, Recent Advances and Novel Approaches.

• Superior document: Big Data Management Series
• Author: Zhao, Xiang.
• TeilnehmendeR: Zeng, Weixin.; Tang, Jiuyang.
• Place / Publishing House: Singapore : : Springer Singapore Pte. Limited,, 2023.; Ã2023.
• Year of Publication: 2023
• Edition: 1st ed.
• Language: English
• Series: Big Data Management Series
• Physical Description: 1 online resource (252 pages)

Table of Contents

Intro -- Preface -- Background -- Content Organization -- Contents -- Part I Concept and Categorization -- 1 Introduction to Entity Alignment -- 1.1 Background -- 1.2 Related Works -- 1.2.1 Entity Linking -- 1.2.2 Entity Resolution -- 1.2.3 Entity Resolution on KGs -- 1.3 Evaluation Settings -- References -- 2 State-of-the-Art Approaches -- 2.1 Introduction -- 2.2 A General EA Framework -- 2.2.1 Embedding Learning Module -- 2.2.2 Alignment Module -- 2.2.3 Prediction Module -- 2.2.4 Extra Information Module -- 2.3 Experiments and Analysis -- 2.3.1 Categorization -- 2.3.2 Experimental Settings -- 2.3.3 Results and Analyses on DBP15K -- 2.3.4 Results and Analyses on SRPRS -- 2.3.5 Results and Analyses on DWY100K -- 2.3.6 Efficiency Analysis -- 2.3.7 Comparison with Unsupervised Approaches -- 2.3.8 Module-Level Evaluation -- 2.3.9 Summary -- 2.3.10 Guidelines and Suggestions -- 2.4 New Dataset and Further Experiments -- 2.4.1 Dataset Construction -- 2.4.2 Experimental Results on DBP-FB -- 2.4.3 Unmatchable Entities -- 2.5 Conclusion -- Appendix A -- Methods in Group i of Table 2.1 -- Methods in Group ii of Table 2.1 -- Methods in Group iii of Table 2.1 -- Appendix B -- Parameter Setting -- References -- Part II Recent Advances -- 3 Recent Advance of Representation Learning Stage -- 3.1 Overview -- 3.2 Models -- 3.2.1 ALiNet -- 3.2.2 MRAEA -- 3.2.3 RREA -- 3.2.4 RPR-RHGT -- 3.2.5 RAGA -- 3.2.6 Dual-AMN -- 3.2.7 ERMC -- 3.2.8 KE-GCN -- 3.2.9 RePS -- 3.2.10 SDEA -- 3.3 Experiments -- 3.3.1 Experimental Setting -- 3.3.2 Overall Results and Analysis -- 3.3.3 Further Experiments -- 3.3.3.1 Pre-processing Module -- 3.3.3.2 Messaging Module -- 3.3.3.3 Attention Module -- 3.3.3.4 Aggregation Module -- 3.3.3.5 Post-processing Module -- 3.3.3.6 Loss Function Module -- 3.4 Conclusion -- References -- 4 Recent Advance of Alignment Inference Stage.

4.1 Introduction -- 4.2 Preliminaries -- 4.2.1 Task Formulation and Framework -- 4.2.2 Related Work and Scope -- 4.2.3 Key Assumptions -- 4.3 Alignment Inference Algorithms -- 4.3.1 Overview -- 4.3.2 Simple Embedding Matching -- 4.3.3 CSLS Algorithm -- 4.3.4 Reciprocal Embedding Matching -- 4.3.5 Embedding Matching as Assignment -- 4.3.6 Stable Embedding Matching -- 4.3.7 RL-Based Embedding Matching -- 4.4 Main Experiments -- 4.4.1 EntMatcher: An Open-Source Library -- 4.4.2 Experimental Settings -- 4.4.3 Main Results and Comparison -- 4.4.4 Results on Large-Scale Datasets -- 4.4.5 Analysis and Insights -- 4.5 New Evaluation Settings -- 4.5.1 Unmatchable Entities -- 4.5.2 Non-1-to-1 Alignment -- 4.6 Summary and Future Direction -- 4.7 Conclusion -- References -- Part III Novel Approaches -- 5 Large-Scale Entity Alignment -- 5.1 Introduction -- 5.2 Framework -- 5.3 Partition Strategies for Entity Alignment -- 5.3.1 Seed-Oriented Unidirectional Graph Partition -- 5.3.2 Bidirectional Graph Partition -- 5.3.3 Iterative Bidirectional Graph Partition -- 5.3.4 Complexity Analysis -- 5.3.5 Discussion -- 5.4 Reciprocal Alignment Inference -- 5.4.1 Entity Structural Representation Learning -- 5.4.2 Preference Modeling -- 5.4.3 Preference Aggregation -- 5.4.4 Correctness Analysis -- 5.4.5 Complexity Analysis -- 5.5 Variants of Reciprocal Alignment Inference -- 5.5.1 No-Ranking Aggregation -- 5.5.2 Progressive Blocking -- 5.6 Experimental Settings -- 5.6.1 Dataset -- 5.6.2 Construction of a Large-Scale Dataset -- 5.6.3 Implementation Details -- 5.6.4 Evaluation Metrics -- 5.6.5 Competing Methods -- 5.7 Results -- 5.7.1 Evaluation on Large-Scale Dataset -- 5.7.2 Comparison with State-of-the-Art Methods -- 5.7.3 Experiments and Analyses on Partitioning -- 5.7.4 Experiments and Analyses on Reciprocal Inference.

5.7.5 Experiments and Analyses on Progressive Blocking -- 5.8 Related Work -- 5.9 Conclusion -- Appendix -- Correctness Analysis -- References -- 6 Long-Tail Entity Alignment -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Impact of Long-Tail Phenomenon -- 6.4 Methodology -- 6.4.1 Name Representation Learning -- 6.4.2 Degree-Aware Co-attention Feature Fusion -- 6.4.3 Iterative KG Completion -- 6.5 Experiments -- 6.5.1 Experimental Setting -- 6.5.2 Results -- 6.5.3 Ablation Study -- 6.5.4 Error Analysis -- 6.5.5 Further Experiment -- 6.6 Conclusion -- References -- 7 Weakly Supervised Entity Alignment -- 7.1 Introduction -- 7.2 Preliminaries -- 7.2.1 Problem Formulation -- 7.2.2 Model Overview -- 7.3 Reinforced Active Learning -- 7.3.1 Query Strategies -- 7.3.2 Reinforced Active Entity Selection via MAB -- 7.4 Contrastive Embedding Learning -- 7.4.1 Semi-supervised Alignment Loss -- 7.4.2 Graph Encoders -- 7.4.3 Unsupervised Contrastive Loss -- 7.4.4 Alignment Inference -- 7.5 Experiment -- 7.5.1 Experimental Settings -- 7.5.2 Main Results (RQ1) -- 7.5.3 Experiments on Contrastive Learning (RQ2) -- 7.5.4 Experiments on Reinforced AL (RQ3) -- 7.6 Related Work -- 7.7 Conclusion -- References -- 8 Unsupervised Entity Alignment -- 8.1 Introduction -- 8.2 Task Definition and Related Work -- 8.3 Methodology -- 8.3.1 Model Outline -- 8.3.2 Side Information -- 8.3.3 Unmatchable Entity Prediction -- 8.3.3.1 Thresholded Bidirectional Nearest Neighbor Search -- 8.3.3.2 Confidence-Based TBNNS -- 8.3.4 The Progressive Learning Framework -- 8.3.4.1 Knowledge Graph Representation Learning -- 8.3.4.2 The Progressive Learning Algorithm -- 8.3.4.3 Dynamic Threshold Adjustment -- 8.4 Experiment -- 8.4.1 Experimental Settings -- 8.4.2 Results -- 8.4.2.1 Results Using Low-Quality Side Information -- 8.4.3 Ablation Study -- 8.4.4 Quantitative Analysis -- 8.5 Conclusion.

References -- 9 Multimodal Entity Alignment -- 9.1 Introduction -- 9.2 Related Work -- 9.2.1 Multi-Modal Knowledge Graph -- 9.2.2 Representation Learning in Hyperbolic Space -- 9.3 Preliminaries -- 9.3.1 Task Formulation -- 9.3.2 Graph Convolutional Neural Networks -- 9.3.3 Hyperboloid Manifold -- 9.4 Methodology -- 9.4.1 Structural Representation Learning -- 9.4.2 Visual Representation Learning -- 9.4.3 Multi-Modal Information Fusion -- 9.4.4 Alignment Prediction -- 9.4.5 Model Training -- 9.5 Experiment -- 9.5.1 Dataset and Evaluation Metric -- 9.5.2 Experimental Setting and Competing Approaches -- 9.5.3 Results -- 9.5.4 Ablation Experiment -- 9.5.5 Case Study -- 9.5.6 Additional Experiment -- 9.6 Conclusion -- References.