Domain-Specific Knowledge Graph Construction (eBook)

Preface 7
Acknowledgments 9
Contents 10
Acronyms 12
1 What Is a Knowledge Graph? 14
1.1 Introduction 14
1.2 Example 1: Academic Domain 17
1.3 Example 2: Products and Companies 18
1.4 Example 3: Geopolitical Events 19
1.5 Conclusion 20
2 Information Extraction 21
2.1 Introduction 21
2.2 Challenges of IE 22
2.3 Scope of IE Tasks 23
2.3.1 Named Entity Recognition 24
2.3.1.1 Supervised Methods 25
2.3.1.2 Semi-supervised Methods 26
2.3.1.3 Unsupervised Methods 27
2.3.1.4 Features 28
2.3.2 Relation Extraction 34
2.3.3 Event Extraction 36
2.3.4 Web IE 38
2.4 Evaluating IE Performance 41
2.5 Summary 42
3 Entity Resolution 44
3.1 Introduction 44
3.2 Challenges and Requirements 45
3.3 Two-Step Framework 49
3.3.1 Blocking 50
3.3.1.1 Traditional Blocking 51
3.3.1.2 Sorted Neighborhood 52
3.3.1.3 Canopies 53
3.3.1.4 Research Frontier: Learning Blocking Keys 54
3.3.2 Similarity 55
3.4 Measuring Performance 58
3.4.1 Measuring Blocking Performance 59
3.4.2 Measuring Similarity Performance 61
3.5 Extending the Two-Step Workflow: A Brief Note 62
3.6 Related Work: A Brief Review 62
3.6.1 Automated ER Solutions 63
3.6.1.1 The Automation-Scalability Tradeoff 65
3.6.2 Structural Heterogeneity 66
3.6.3 Blocking Without Supervision: Where Do We Stand? 67
3.7 Summary 68
4 Advanced Topic: Knowledge Graph Completion 69
4.1 Introduction 69
4.2 Knowledge Graph Embeddings 71
4.2.1 TransE 73
4.2.2 TransE Extensions and Alternatives 74
4.2.3 Limitations and Alternatives 76
4.2.4 Research Frontiers and Recent Work 76
4.2.4.1 Ontological Information 77
4.2.4.2 Text 78
4.2.4.3 Other Extrinsic Information Sets 79
4.2.5 Applications of KGEs 82
4.3 Summary 84
5 Ecosystems 85
5.1 Introduction 85
5.2 Web of Linked Data 85
5.2.1 Linked Data Principles 87
5.2.2 Technology Stack 88
5.2.3 Linking Open Data 89
5.2.4 Example: DBpedia 90
5.3 Google Knowledge Vault 92
5.4 Schema.org 94
5.5 Where is the Future Going? 96
Glossary 98
References 101
Index 111