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Semantic Web ISWC 2024: 23rd International Semantic Web Conference, Baltimore, MD, USA, November 1115, 2024, Proceedings, Part I [Mīkstie vāki]

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  • Formāts: Paperback / softback, 259 pages, height x width: 235x155 mm, 55 Illustrations, color; 14 Illustrations, black and white; XXXIX, 259 p. 69 illus., 55 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 15231
  • Izdošanas datums: 27-Nov-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 303177843X
  • ISBN-13: 9783031778438
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Semantic Web ISWC 2024: 23rd International Semantic Web Conference, Baltimore, MD, USA, November 1115, 2024, Proceedings, Part IPalielināt
  • Formāts: Paperback / softback, 259 pages, height x width: 235x155 mm, 55 Illustrations, color; 14 Illustrations, black and white; XXXIX, 259 p. 69 illus., 55 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 15231
  • Izdošanas datums: 27-Nov-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 303177843X
  • ISBN-13: 9783031778438
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031778438.html
This three-volume set constitutes the proceedings of the 23rd International Semantic Web Conference, ISWC 2023, held in Hanover, MD, USA, during November 11-15, 2024.

The 44 full papers presented in these proceedings were carefully reviewed and selected from 155 submissions. This conference focuses on research on the Semantic Web, including benchmarks, knowledge graphs, tools and vocabularies.
.- Research Track.



.- SnapE - Training Snapshot Ensembles of Link Prediction Models.



.- Numerical Literals in Link Prediction: A Critical Examination of Models
and Datasets.



.- Relationships are Complicated! An Analysis of Relationships Between
Datasets on the Web.



.- Multi-view Transformer-based Network for Prerequisite Learning in Concept
Graphs.



.- Knowledge Graph Structure as Prompt: Improving Small Language Models
Capabilities for Knowledge-based Causal Discovery.



.- Repairing Networks of EL Ontologies using Weakening and Completing.



.- Do LLMs Really Adapt to Domains? An Ontology Learning Perspective.



.- Supervised Relational Learning with Selective Neighbor Entities for
Few-Shot Knowledge Graph Completion.



.- Knowledge Graphs for Enhancing Large Language Models in Entity
Disambiguation.



.- Unaligned Federated Knowledge Graph Embedding.



.- Finetuning Generative Large Language Models with Discrimination
Instructions for Knowledge Graph Completion.



.- BLINK: Blank Node Matching Using Embeddings.



.- Distilling Event Sequence Knowledge From Large Language Models.