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Adaptive Computation and Machine Learning series: Knowledge Graphs

Fundamentals, Techniques, and Applications

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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.

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Adaptive Computation and Machine Learning series: Knowledge Graphs, Mayank Kejriwal, Craig A. Knoblock, Pedro Szekely

Taal
Jaar van publicatie
2021
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(Hardcover)
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Titel
Adaptive Computation and Machine Learning series: Knowledge Graphs
Ondertitel
Fundamentals, Techniques, and Applications
Taal
Engels
Jaar van publicatie
2021
Formaat
Hardcover
Aantal pagina's
568
ISBN10
0262045095
ISBN13
9780262045094
Reeks
Beoordeling
3,5 van 5
Aantekening
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.