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Knowledge Graphs Synthesis Lectures on Data Semantics and Knowledge Aidan Hogan
US $169,97
Ca.EUR 146,26
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US $5,22 (ca. EUR 4,49) USPS Media MailTM.
Standort: Griffin, Georgia, USA
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eBay-Artikelnr.:336265221709
Artikelmerkmale
- Artikelzustand
- Country of Origin
- United States
- Book Title
- Knowledge Graphs (Synthesis Lectures on Data, Semantics, and Kno,
- Narrative Type
- Data Modeling & Design
- Genre
- N/A
- Intended Audience
- N/A
- Item Weight
- 17.5 Oz
- ISBN
- 9781636392370
Über dieses Produkt
Product Identifiers
Publisher
Morgan & Claypool Publishers
ISBN-10
1636392377
ISBN-13
9781636392370
eBay Product ID (ePID)
21057263821
Product Key Features
Number of Pages
257 Pages
Publication Name
Knowledge Graphs
Language
English
Subject
Data Modeling & Design, General, Data Processing, Databases / Data Mining
Publication Year
2021
Type
Textbook
Subject Area
Mathematics, Computers
Series
Synthesis Lectures on Data, Semantics, and Knowledge Ser.
Format
Hardcover
Dimensions
Item Length
9 in
Item Width
6 in
Additional Product Features
Intended Audience
Scholarly & Professional
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
001.4226
Table Of Content
Preface Acknowledgments Introduction Data Graphs Schema, Identity, and Context Deductive Knowledge Inductive Knowledge Creation and Enrichment Quality Assessment Refinement Publication Knowledge Graphs in Practice Conclusions Bibliography Authors' Biographies
Synopsis
Provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. The book defines knowledge graphs, provides a high-level overview of how they are used, and presents and contrasts popular graph models that are commonly used., 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.
LC Classification Number
QA76.9.I52
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