Domain-Specific Knowledge Graph Construction (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
107
Utgivningsdatum
2019-03-15
Upplaga
1st ed. 2019
Förlag
Springer Nature Switzerland AG
Illustratör/Fotograf
Bibliographie
Illustrationer
19 Illustrations, black and white; XIV, 107 p. 19 illus.
Dimensioner
234 x 156 x 7 mm
Vikt
186 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030123741
Domain-Specific Knowledge Graph Construction (häftad)

Domain-Specific Knowledge Graph Construction

Häftad Engelska, 2019-03-15
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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 describes 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 book serves as a useful reference, as well as an accessible but rigorous overview of this body of work. The book presents interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. The book also appeals to practitioners in industry and data scientists since it has chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations.
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Innehållsförteckning

1. What is a knowledge graph?.- 2. Information Extraction.- 3. Entity Resolution.- 4. Advanced Topic: Knowledge Graph Completion.- 5. Ecosystems