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DL4KG 2024 : The 7th Edition of Workshop on Deep Learning and Large Language Models for Knowledge Graphs

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Link: https://genetasefa.github.io/dl4kg2024/
 
When Aug 25, 2024 - Aug 26, 2024
Where Barcelona, Spain
Abstract Registration Due May 20, 2024
Submission Deadline May 28, 2024
Notification Due Jun 28, 2024
Final Version Due Jul 5, 2024
Categories    NLP   computational linguistics   artificial intelligence
 

Call For Papers



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The 7th Edition of Workshop on Deep Learning and Large Language Models for Knowledge Graphs (DL4KG),

August 25 or 26, 2024

Web: https://genetasefa.github.io/dl4kg2024/

Twitter: @dl4kg1

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In conjunction with KDD 2024, Aug 25 - Aug 29,

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Workshop Overview

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Over the past years, there has been a rapid growth in the use and 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 breakthroughs in various research fields, especially Natural Language Processing (NLP) and Computer Vision.

In order to pursue more advanced methodologies, it has become critical that the communities related to Deep Learning, Knowledge Graphs, and NLP join 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 ESWC 2018 as DL4KGs and ISWC 2018, ESWC 2019, ESWC 2020, ISWC 2021, ISWC 2022, and ISWC 2023 aims to reinforce the relationships between these communities and foster inter-disciplinary research in the areas of KG, Deep Learning, and Natural Language Processing.

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Topics of Interest

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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 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

Applications to particular domains such as Cultural Heritage, Materials Sciences, Biomedical domain, Scholarly data, etc.

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Important Dates

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- May 20, 2024: Abstract submission deadline

- May 28, 2024: Full and Short paper submission deadline

- June 28, 2024: Notification of Acceptance

- July 05, 2024: Camera-ready paper due

- August 25 or 26, 2024: KDD Workshop day

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Submissions

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Full research papers (8-10 pages)

Short research papers (4-7 pages)

Papers are submitted in PDF format via the workshop’s Open Review submission pages (-- ).

Accepted papers (after a blind review of at least 3 experts) will be published by CEUR-WS (single column). At least one of the authors of the accepted papers must register for the workshop (pre-conference only option) to be included in the workshop proceedings.

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Organization

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- Mehwish Alam, Telecom Paris, Institut Polytechnique de Paris, France

- Davide Buscaldi, LIPN, University Sorbonne Paris Nord, France

- Michael Cochez, Vrije University of Amsterdam, the Netherlands

- Genet Asefa Gesese, FIZ Karlsruhe, KIT, Germany

- Francesco Osborne, Knowledge Media Institute, (KMi), The Open University, UK

- Diego Reforgiato Recupero, Department of Mathematics and Computer Science, University of Cagliari, Italy


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