| Description |
1 online resource (xix, 237 pages) : illustrations |
| Series |
Synthesis lectures on data, semantics, and knowledge, 2691-2031 ; #22 |
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Synthesis lectures on data, semantics, and knowledge ; 22.
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Synthesis digital library of engineering and computer science.
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| Contents |
1. Introduction -- 2. Data graphs -- 2.1. Models -- 2.2. Querying |
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3. Schema, identity, and context -- 3.1. Schema -- 3.2. Identity -- 3.3. Context |
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4. Deductive knowledge -- 4.1. Ontologies -- 4.2. Reasoning |
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5. Inductive knowledge -- 5.1. Graph analytics -- 5.2. Knowledge graph embeddings -- 5.3. Graph neural networks -- 5.4. Symbolic learning |
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6. Creation and enrichment -- 6.1. Human collaboration -- 6.2. Text sources -- 6.3. Markup sources -- 6.4. Structured sources -- 6.5. Schema/ontology creation |
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7. Quality assessment -- 7.1. Accuracy -- 7.2. Coverage -- 7.3. Coherency -- 7.4. Succinctness -- 7.5. Other quality dimensions |
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8. Refinement -- 8.1. Completion -- 8.2. Correction -- 8.3. Other refinement tasks |
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9. Publication -- 9.1. Best practices -- 9.2. Access protocols -- 9.3. Usage control |
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10. Knowledge graphs in practice -- 10.1. Open knowledge graphs -- 10.2. Enterprise knowledge graphs -- 11. Conclusions |
| Summary |
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques--based on statistics, graph analytics, machine learning, etc.--can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics |
| Analysis |
knowledge graphs |
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graph databases |
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knowledge graph embeddings |
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graph neural networks |
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ontologies |
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knowledge graph refinement |
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knowledge graph quality |
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knowledge bases |
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artificial intelligence |
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semantic web |
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machine learning |
| Notes |
Part of: Synthesis digital library of engineering and computer science |
| Bibliography |
Includes bibliographical references (pages 165-228) |
| Notes |
Title from PDF title page (viewed on February 4, 2022) |
| Subject |
Information visualization.
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Conceptual structures (Information theory)
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Semantic computing.
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Graphic methods.
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Graphic methods -- Computer programs.
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graphs.
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Conceptual structures (Information theory)
|
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Graphic methods
|
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Graphic methods -- Computer programs
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Information visualization
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Semantic computing
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| Form |
Electronic book
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| Author |
Blomqvist, Eva, author.
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Cochez, Michael, author.
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| ISBN |
9781636392363 |
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1636392369 |
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9783031019180 |
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3031019180 |
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