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Domain-Specific Knowledge Graph Construction [electronic resource] /

By: Kejriwal, Mayank [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: XIV, 107 p. 19 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030123758.Subject(s): Computer Science | Data mining | Information storage and retrieval systems | Computer science | Data Mining and Knowledge Discovery | Information Storage and Retrieval | Information Systems Applications (incl. Internet) | Probability and Statistics in Computer ScienceOnline resources: Click here to access online
Contents:
1 What is a knowledge graph?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Example 1: Academic Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Example 2: Products and Companies . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Example 3: Geopolitical Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 -- 2 Information Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Challenges of IE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Scope of IE Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1 Named Entity Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.2 Relation Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.3 Event Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3.4 Web IE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.4 Evaluating IE Performance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 -- 3 Entity Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.2 Challenges and Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3 Two-step Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.3.1 Blocking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.3.2 Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.4 Measuring Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.4.1 Measuring Blocking Performance . . . . . . . . . . . . . . . . . . . . . . 46 3.4.2 Measuring Similarity Performance . . . . . . . . . . . . . . . . . . . . . . 48 3.5 Extending the Two-Step Workflow: A Brief Note . . . . . . . . . . . . . . . . 48 3.6 Related Work: A Brief Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.6.1 Automated ER Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.6.2 Structural Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.6.3 Blocking Without Supervision: Where Do We Stand? . . . . . . 54 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 -- 4 Advanced Topic: Knowledge Graph Completion . . . . . . . . . . . . . . . . . . . 55 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2 Knowledge Graph Embeddings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.2.1 TransE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.2.2 TransE Extensions and Alternatives . . . . . . . . . . . . . . . . . . . . . 60 4.2.3 Limitations and Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2.4 Research Frontiers and Recent Work . . . . . . . . . . . . . . . . . . . . 62 4.2.5 Applications of KGEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 -- 5 Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.2 Web of Linked Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.2.1 Linked Data Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.2.2 Technology Stack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 5.2.3 Linking Open Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 5.2.4 Example: DBpedia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.3 Google Knowledge Vault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5.4 Schema.org . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.5 Where is the future going? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 -- References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 -- Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 -- Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97.
In: Springer eBooksSummary: The vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner. The book will describe a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This work would serve as a useful reference, as well as an accessible but rigorous overview of this body of work. The book will present interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. This will allow the book to be marketed in multiple venues and conferences. The book will also appeal to practitioners in industry and data scientists since it will have chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations. The author has, and continues to, present on this topic at large and important conferences. He plans to make the powerpoint he presents available as a supplement to the work. This will draw a natural audience for the book. Some of the reviewers are unsure about his position in the community but that seems to be more a function of his age rather than his relative expertise. I agree with some of the reviewers that the title is a little complicated. I would recommend “Domain Specific Knowledge Graphs”. .
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1 What is a knowledge graph?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Example 1: Academic Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Example 2: Products and Companies . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Example 3: Geopolitical Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 -- 2 Information Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Challenges of IE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Scope of IE Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1 Named Entity Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.2 Relation Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.3 Event Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3.4 Web IE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.4 Evaluating IE Performance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 -- 3 Entity Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.2 Challenges and Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3 Two-step Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.3.1 Blocking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.3.2 Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.4 Measuring Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.4.1 Measuring Blocking Performance . . . . . . . . . . . . . . . . . . . . . . 46 3.4.2 Measuring Similarity Performance . . . . . . . . . . . . . . . . . . . . . . 48 3.5 Extending the Two-Step Workflow: A Brief Note . . . . . . . . . . . . . . . . 48 3.6 Related Work: A Brief Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.6.1 Automated ER Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.6.2 Structural Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.6.3 Blocking Without Supervision: Where Do We Stand? . . . . . . 54 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 -- 4 Advanced Topic: Knowledge Graph Completion . . . . . . . . . . . . . . . . . . . 55 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2 Knowledge Graph Embeddings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.2.1 TransE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.2.2 TransE Extensions and Alternatives . . . . . . . . . . . . . . . . . . . . . 60 4.2.3 Limitations and Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2.4 Research Frontiers and Recent Work . . . . . . . . . . . . . . . . . . . . 62 4.2.5 Applications of KGEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 -- 5 Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.2 Web of Linked Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.2.1 Linked Data Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.2.2 Technology Stack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 5.2.3 Linking Open Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 5.2.4 Example: DBpedia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.3 Google Knowledge Vault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5.4 Schema.org . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.5 Where is the future going? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 -- References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 -- Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 -- Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97.

The vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner. The book will describe a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This work would serve as a useful reference, as well as an accessible but rigorous overview of this body of work. The book will present interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. This will allow the book to be marketed in multiple venues and conferences. The book will also appeal to practitioners in industry and data scientists since it will have chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations. The author has, and continues to, present on this topic at large and important conferences. He plans to make the powerpoint he presents available as a supplement to the work. This will draw a natural audience for the book. Some of the reviewers are unsure about his position in the community but that seems to be more a function of his age rather than his relative expertise. I agree with some of the reviewers that the title is a little complicated. I would recommend “Domain Specific Knowledge Graphs”. .

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