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Workshop on Deep Learning and Large Language Models for Knowledge Graphs (DL4KG)

  • Mehwish Alam
  • , Davide Buscaldi
  • , Diego Reforgiato Recupero
  • , Michael Cochez
  • , Genet Asefa Gesese
  • , Francesco Osborne
  • Laboratoire d'Informatique de Paris Nord
  • Universitá di Cagliari
  • Vrije Universiteit Amsterdam
  • FIZ Karlsruhe - Leibniz Institute for Information Infrastructure
  • The Open University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The use of Knowledge Graphs (KGs) which constitute large networks of real-world entities and their interrelationships, has grown rapidly. A substantial body of research has emerged, exploring the integration of deep learning (DL) and large language models (LLMs) with KGs. This workshop aims to bring together leading researchers in the field to discuss and foster collaborations on the intersection of KG and DL/LLMs.

Original languageEnglish
Title of host publicationKDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages6704-6705
Number of pages2
ISBN (Electronic)9798400704901
DOIs
Publication statusPublished - 24 Aug 2024
Event30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024 - Barcelona, Spain
Duration: 25 Aug 202429 Aug 2024

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ISSN (Print)2154-817X

Conference

Conference30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024
Country/TerritorySpain
CityBarcelona
Period25/08/2429/08/24

Keywords

  • artificial intelligence
  • deep learning
  • knowledge graphs
  • large language models

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