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Book 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, 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.

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

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    A Paperback by Terry R. Payne, Valentina Presutti, Guilin Qi

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      View other formats and editions of The Semantic Web – ISWC 2023: 22nd International Semantic Web Conference, Athens, Greece, November 6–10, 2023, Proceedings, Part I by Terry R. Payne

      Publisher: Springer International Publishing AG
      Publication Date: 02/11/2023
      ISBN13: 9783031472398, 978-3031472398
      ISBN10: 303147239X

      Description

      Book 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, 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.

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