Workshop at KDD 2024 on
Deep Learning and Large Language Models for Knowledge Graphs
More Details!

About

Over the past years there has been a rapid growth in the use and the importance of Knowledge Graphs (KGs) along with their application to many important tasks. KGs are large networks of real-world entities described in terms of their semantic types and their relationships to each other. On the other hand, Deep Learning methods have also become an important area of research, achieving some important breakthrough in various research fields, especially Natural Language Processing (NLP) and Image Recognition.

In order to pursue more advanced methodologies, it has become critical that the communities related to Deep Learning, Knowledge Graphs, and NLP join their forces in order to develop more effective algorithms and applications. This workshop, in the wake of other similar efforts at previous Semantic Web conferences such as ESWC2018 as DL4KGs and ISWC2018, ESWC2019, ESWC 2020, ISWC2021, ISWC2022, ISWC2023 aims to reinforce the relationships between these communities and foster inter-disciplinary research in the areas of KG, Deep Learning, and Natural Language Processing.

Topics of Interest

LLMs and Knowledge Graphs
  • Knowledge Base Construction using LLMs
  • Knowledge Graphs to improve the quality of LLMs
  • Question Answering exploiting LLMs and Knowledge Graphs (such as Retrieval Augmented Generation)
  • Hybrid LLMs-KG models (cross-attention, joint training,...)
  • Knowledge-Based fact-checking for LLMs
New Approaches for Combining Deep Learning, LLMs, and Knowledge Graphs
  • Methods for generating Knowledge Graph (node) embeddings
  • Temporal Knowledge Graph Embeddings
  • KGs for interoperability and Explainability
  • Recommender Systems leveraging Knowledge Graphs
  • Link Prediction and completing KGs
  • Ontology Learning and Matching exploiting Knowledge Graph-Based Embeddings
  • Knowledge Graph-Based Sentiment Analysis
  • Natural Language Understanding/Machine Reading
  • Question Answering exploiting Knowledge Graphs and Deep Learning
  • Approximate query answering on knowledge graphs
  • Trend Prediction based on Knowledge Graphs Embeddings
  • Learning Representations from Graphs (Graph Neural Networks, Graph Convolutional Networks, etc.)
Applications of combining Deep Learning, LLMs, and Knowledge Graphs
  • Domain Specific Applications (e.g., Scholarly, Biomedical, Cultural Heritage, etc.)
  • Applications in industry 4.0.
  • Knowledge Graph Alignment
  • Applying to real-world scenarios


Submission Details

Papers must comply with the CEUR-WStemplate (single column)
Papers are submitted in PDF format via the workshop’s Open Review submission pages



Submissions can fall in one of the following categories:
  • Full research papers (8-10 pages)
  • Short research papers (4-7 pages)

Accepted papers (after blind review of at least 3 experts) will be published by CEUR–WS.

At least one of the authors of the accepted papers must register for the workshop (pre-conference only option) to be included into the workshop proceedings.

Important Dates

  • Full and Short paper submission deadline: June 03, 2024 June 10, 2024
  • Notification of Acceptance: June 28, 2024July 03, 2024
  • Camera-ready paper due: July 05, 2024 July 16, 2024
  • KDD 2024 Workshop day: August 26, 2024
Read CFP

Organizing Committee

Genet Asefa Gesese

FIZ Karlsruhe, KIT, Germany

Mehwish Alam

Telecom Paris, Institut Polytechnique de Paris, France

Davide Buscaldi

Labortoire d'Informatique Paris Nord (LIPN), Paris, France

Michael Cochez

Vrije University of Amsterdam, the Netherlands

Francesco Osborne

Knowledge Media Institute (KMi), The Open University, UK

Diego Reforgiato Recupero

University of Cagliari, Cagliari, Italy

Program Committee

  • Rima Türker, Karlsruhe Institute of Technology
  • Danilo Dessi, GESIS
  • Paul Groth, University of Amsterdam
  • Thiviyan Thanapalasingam, University of Amsterdam, the Netherlands
  • Peter Bloem, VU Amsterdam, the Netherlands
  • Finn Arup Nielsen, Technical University of Denmark, Denmark
  • Mayank Kejriwal, University of Southern California, US
  • Femke Ongenae, Ghent University, Belgium
  • Achim Rettinger, University of Trier, Germany
  • Gerard de Melo, Hasso Plattner Institute, Germany
  • Max Berrendorf, University of Ludwig-Maximilians (LMU), Germany

Volunteer to be the part of program committee of DL4KG by filling in this form.