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In den WarenkorbZustand: New. In.
Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 524 | Sprache: Englisch | Produktart: Bücher | This book constitutes the proceedings of the 22nd International Semantic Web Conference, ISWC 2023, which took place in October 2023 in Athens, Greece. The 58 full papers presented in this double volume were thoroughly reviewed and selected from 248 submissions. Many submissions focused on the use of reasoning and query answering, witha number addressing engineering, maintenance, and alignment tasks for ontologies. Likewise, there has been a healthy batch of submissions on search, query, integration, and the analysis of knowledge. Finally, following the growing interest in neuro-symbolic approaches, there has been a rise in the number of studies that focus on the use of Large Language Models and Deep Learning techniques such as Graph Neural Networks.
Sprache: Englisch
Verlag: Springer-Nature New York Inc, 2023
ISBN 10: 303147242X ISBN 13: 9783031472428
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In den WarenkorbPaperback. Zustand: Brand New. 522 pages. 9.25x6.10x1.05 inches. In Stock.
Sprache: Englisch
Verlag: Springer Nature Switzerland, 2023
ISBN 10: 303147242X ISBN 13: 9783031472428
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the 22nd International Semantic Web Conference, ISWC 2023, which took place in October 2023 in Athens, Greece.The 58 full papers presented in this double volume were thoroughly reviewed and selected from 248 submissions. Many submissions focused on the use of reasoning and query answering, witha number addressing engineering, maintenance, and alignment tasks for ontologies. Likewise, there has been a healthy batch of submissions on search, query,integration, and the analysis of knowledge. Finally, following the growing interest in neuro-symbolic approaches, there has been a rise in the number of studiesthat focus on the use of Large Language Models and Deep Learning techniquessuch as Graph Neural Networks.