Workshop on Deep Learning and Large Language Models for Knowledge Graphs

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.