The Semantic Web – ISWC 2023 [electronic resource] : 22nd International Semantic Web Conference, Athens, Greece, November 6–10, 2023, Proceedings, Part I /

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Material type: Text

TextSeries: Lecture Notes in Computer Science ; 14265Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edition: 1st ed. 2023Description: XLV, 640 p. 150 illus., 131 illus. in color. online resourceContent type:

text

Media type:

computer

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online resource

ISBN:

9783031472404

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Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:

005.74 23

LOC classification:

QA76.9.D3

Online resources:

Click here to access online

In: Springer Nature eBookSummary: 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.

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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.

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