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Format: 1 Online-Ressource (XI, 247 p. 1 illus).

Edition: 1st ed. 2023

ISBN: 978-981-9942-50-3

Series Statement: Big Data Management

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-49-7

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-51-0

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-52-7

Language: English

DOI: 10.1007/978-981-99-4250-3

URL: Volltext  (kostenfrei)

UID:

Format: 1 Online-Ressource (XI, 247 p. 1 illus).

Edition: 1st ed. 2023

ISBN: 978-981-9942-50-3

Series Statement: Big Data Management

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-49-7

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-51-0

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-52-7

Language: English

DOI: 10.1007/978-981-99-4250-3

URL: Volltext  (kostenfrei)

UID:

Format: 1 Online-Ressource (XI, 247 p. 1 illus).

Edition: 1st ed. 2023

ISBN: 978-981-9942-50-3

Series Statement: Big Data Management

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-49-7

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-51-0

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-52-7

Language: English

DOI: 10.1007/978-981-99-4250-3

URL: Volltext  (kostenfrei)

UID:

Format: 1 Online-Ressource (XI, 247 p. 1 illus)

Edition: 1st ed. 2023

ISBN: 9789819942503

Series Statement: Big Data Management

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-49-7

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-51-0

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9942-52-7

Language: English

DOI: 10.1007/978-981-99-4250-3

URL: Volltext  (kostenfrei)

UID:

Format: 1 Online-Ressource (XVIII, 639 p. 217 illus., 168 illus. in color).

Edition: 1st ed. 2023

ISBN: 978-981-9962-22-8

Series Statement: Lecture Notes in Computer Science 14094

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9962-21-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9962-23-5

Language: English

Keywords: Konferenzschrift

DOI: 10.1007/978-981-99-6222-8

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (252 pages)

Edition: 1st ed.

ISBN: 9789819942503

Series Statement: Big Data Management Series

Note: 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.

Additional Edition: Print version: Zhao, Xiang Entity Alignment Singapore : Springer Singapore Pte. Limited,c2023 ISBN 9789819942497

Language: English

Keywords: Electronic books. ; Electronic books.

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UID:

Format: 1 Online-Ressource (XVIII, 639 p. 217 illus., 168 illus. in color).

Edition: 1st ed. 2023

ISBN: 978-981-9962-22-8

Series Statement: Lecture Notes in Computer Science 14094

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9962-21-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9962-23-5

Language: English

Keywords: Konferenzschrift

DOI: 10.1007/978-981-99-6222-8

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XVIII, 639 p. 217 illus., 168 illus. in color).

Edition: 1st ed. 2023

ISBN: 978-981-9962-22-8

Series Statement: Lecture Notes in Computer Science 14094

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9962-21-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9962-23-5

Language: English

Keywords: Konferenzschrift

DOI: 10.1007/978-981-99-6222-8

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: IX, 167 p. 93 illus., 73 illus. in color. , online resource.

ISBN: 9783319025889

Series Statement: SpringerBriefs in Earth Sciences,

Content: This book describes the algorithms, validation and preliminary analysis of the Global LAnd Surface Satellite (GLASS) products, a long-term, high-quality dataset that is now freely available worldwide to government organizations and agencies, scientific research institutions, students and members of the general public. The GLASS products include leaf area index, broadband albedo, broadband emissivity, downward shortwave radiation and photosynthetically active radiation. The first three GLASS products cover 1981 to 2012 with 1km and 5km spatial resolutions and 8-day temporal resolution, and the last two GLASS products span 2008 to 2010 with 3-hour temporal resolution and 5km spatial resolution. These GLASS products are unique. The first three are spatially continuous and cover the longest period of time among all current similar satellite products. The other two products are the highest spatial-resolution global radiation products from satellite observations that are currently available. These products can be downloaded from Beijing Normal University at  http://glass-product.bnu.edu.cn/ and the University of Maryland Global Land Cover Facility at http://www.glcf.umd.edu/ The GLASS products  are the outcome of a key research project entitled “Generation & Applications of Global Products of Essential Land Variables”, supported by funding from the High-Tech Research and Development Program of China and involving dozens of institutions and nearly one hundred scientists and researchers. Following an introduction, the book contains five chapters corresponding to these five GLASS products: background, algorithm, quality control and validation, preliminary analysis and applications. It discusses the long-term environmental changes detected from the GLASS products and other data sources at both global and local scales and also provides detailed analysis of regional hotspots where environmental changes are mainly associated with climate change, drought, land-atmosphere interactions, and human activities.   The book is based primarily on a set of published journal papers about these five GLASS products and includes updated information. Since these products have now begun to be widely used, this book is an essential reference document. It is also a very helpful resource to anyone interested in satellite remote sensing and its applications.      .

Note: Introduction -- Leaf Area Index -- Shortwave Albedo -- Longwave Emissivity -- Incident Shortwave Radiation -- Incident Photosynthetic Active Radiation -- Challenges and prospects.

In: Springer eBooks

Additional Edition: Printed edition: ISBN 9783319025872

Language: English

DOI: 10.1007/978-3-319-02588-9

URL: http://dx.doi.org/10.1007/978-3-319-02588-9

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UID:

Format: 1 Online-Ressource (XVIII, 639 p. 217 illus., 168 illus. in color)

Edition: 1st ed. 2023

ISBN: 9789819962228

Series Statement: Lecture Notes in Computer Science 14094

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9962-21-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9962-23-5

Language: English

Keywords: Konferenzschrift

DOI: 10.1007/978-981-99-6222-8

URL: Volltext  (URL des Erstveröffentlichers)