The Semantic Web ¿ ISWC 2023

Terry R. Payne

ISBN 10: 303147239X / ISBN 13: 9783031472398

Published by Springer Nature Switzerland Nov 2023, 2023

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 688 pp. Englisch.

Seller Inventory # 9783031472398

Bibliographic Details

• Title: The Semantic Web ¿ ISWC 2023
• Publisher: Springer Nature Switzerland Nov 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 303147239X
• ISBN 13: 9783031472398
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 37D millimeters 235 millimeters width by 155 millimeters height by 37 millimeters depth
• Weight: 1025 grams

About this title

Synopsis

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, with

a 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.