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The Semantic Web – ISWC 2024 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11–15, 2024, Proceedings, Part I /

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...

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Bibliografiske detaljer
Institution som forfatter: SpringerLink (Online service)
Andre forfattere: Demartini, Gianluca (Editor), Hose, Katja (Editor), Acosta, Maribel (Editor), Palmonari, Matteo (Editor), Cheng, Gong (Editor), Skaf-Molli, Hala (Editor), Ferranti, Nicolas (Editor), Hernández, Daniel (Editor), Hogan, Aidan (Editor)
Format: Electronisk eBog
Sprog:engelsk
Udgivet: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Udgivelse:1st ed. 2025.
Serier:Lecture Notes in Computer Science, 15231
Fag:
Database management.
Artificial intelligence.
Computer science.
Data structures (Computer science).
Information theory.
Application software.
Database Management.
Artificial Intelligence.
Theory of Computation.
Data Structures and Information Theory.
Computer and Information Systems Applications.
Online adgang:https://doi.org/10.1007/978-3-031-77844-5
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Indholdsfortegnelse:
  • -- 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.

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