Intro; Preface; Acknowledgments; Contents; Acronyms; 1 What Is a Knowledge Graph?; 1.1 Introduction; 1.2 Example 1: Academic Domain; 1.3 Example 2: Products and Companies; 1.4 Example 3: Geopolitical Events; 1.5 Conclusion; 2 Information Extraction; 2.1 Introduction; 2.2 Challenges of IE; 2.3 Scope of IE Tasks; 2.3.1 Named Entity Recognition; 2.3.1.1 Supervised Methods; 2.3.1.2 Semi-supervised Methods; 2.3.1.3 Unsupervised Methods; 2.3.1.4 Features; 2.3.2 Relation Extraction; 2.3.3 Event Extraction; 2.3.4 Web IE; 2.4 Evaluating IE Performance; 2.5 Summary; 3 Entity Resolution

3.1 Introduction3.2 Challenges and Requirements; 3.3 Two-Step Framework; 3.3.1 Blocking; 3.3.1.1 Traditional Blocking; 3.3.1.2 Sorted Neighborhood; 3.3.1.3 Canopies; 3.3.1.4 Research Frontier: Learning Blocking Keys; 3.3.2 Similarity; 3.4 Measuring Performance; 3.4.1 Measuring Blocking Performance; 3.4.2 Measuring Similarity Performance; 3.5 Extending the Two-Step Workflow: A Brief Note; 3.6 Related Work: A Brief Review; 3.6.1 Automated ER Solutions; 3.6.1.1 The Automation-Scalability Tradeoff; 3.6.2 Structural Heterogeneity; 3.6.3 Blocking Without Supervision: Where Do We Stand?; 3.7 Summary

4 Advanced Topic: Knowledge Graph Completion4.1 Introduction; 4.2 Knowledge Graph Embeddings; 4.2.1 TransE; 4.2.2 TransE Extensions and Alternatives; 4.2.3 Limitations and Alternatives; 4.2.4 Research Frontiers and Recent Work; 4.2.4.1 Ontological Information; 4.2.4.2 Text; 4.2.4.3 Other Extrinsic Information Sets; 4.2.5 Applications of KGEs; 4.3 Summary; 5 Ecosystems; 5.1 Introduction; 5.2 Web of Linked Data; 5.2.1 Linked Data Principles; 5.2.2 Technology Stack; 5.2.3 Linking Open Data; 5.2.4 Example: DBpedia; 5.3 Google Knowledge Vault; 5.4 Schema.org; 5.5 Where is the Future Going?