Buch, Englisch, Band 15231, 259 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 458 g
23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part I
Buch, Englisch, Band 15231, 259 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 458 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-77843-8
Verlag: Springer Nature Switzerland
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
- Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
Weitere Infos & Material
.- 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.
