株式会社極東書店トップ > 商品一覧 > The Semantic Web - ISWC 2023: 22nd International Semantic Web Conference, Athens, Greece, November 6-10, 2023, Proceedings, Part I. 1st ed. 2023
商品詳細
The Semantic Web - ISWC 2023: 22nd International Semantic Web Conference, Athens, Greece, November 6-10, 2023, Proceedings, Part I. 1st ed. 2023
・ISBN 978-3-031-47239-8 paper EUR 84.99
¥22,717.- (税込) ※(※)価格はご注文時の参考価格となります。
納品価格につきましては書籍の入荷時点で確定となります。
版元の原価改定、外国為替の変動等により異なる場合がございますので、予めご了承下さい。
お気に入り
★★★
| 著者・編者 | Payne, Terry R. / Presutti, Valentina / Qi, Guilin / Poveda-Villalon, Maria / Stoilos, Giorgos / Hollink, Laura / Kaoudi, Zoi / Cheng, Gong / Li, Juanzi (eds.), |
|---|---|
| シリーズ | (Lecture Notes in Computer Science) |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2023 |
| ページ数 | 640 pp. |
| 言語 | ENG |
| ニュース番号 | <A01-82615> |
解説
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 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.