Knowledge Graphs (Fundamentals, Techniques, and Applications)
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Product Details
Author:
Mayank Kejriwal, Craig A. Knoblock, Pedro Szekely
Series:
Adaptive Computation and Machine Learning series
Format:
Hardcover
Pages:
568
Publisher:
MIT Press (March 30, 2021)
Language:
English
ISBN-13:
9780262045094
ISBN-10:
0262045095
Weight:
36.9oz
Dimensions:
7.31" x 9.31" x 1.18"
Case Pack:
14
File:
RandomHouse-PRH_Book_Company_PRH_PRT_Onix_full_active_D20260705T122003_156890354-20260705.xml
Folder:
RandomHouse
List Price:
$55.00
As low as:
$42.35
Publisher Identifier:
P-RH
Discount Code:
A
QuickShip:
Yes
Audience:
General/trade
Country of Origin:
United States
Pub Discount:
65
Imprint:
The MIT Press
Overview
A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence.
The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.
The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.
