Humanist Discussion Group, Vol. 38, No. 80.
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        Date: 2024-07-26 13:40:01+00:00
        From: Genet Asefa Gesese <gasefa24@gmail.com>
        Subject: CfP: Workshop on Generative AI and Knowledge Graphs (GenAIK) co-located with COLING 2025

Workshop on Generative AI and Knowledge Graphs (GenAIK),
19 or 20 January 2025,  Abu Dhabi, UAE
Web: https://genetasefa.github.io/GenAIK2025/
Twitter: @GenAIK25

In conjunction with COLING 2025, January 19-24

Workshop Overview

Generative Artificial Intelligence (GenAI) is a branch of artificial
intelligence capable of creating seemingly new, meaningful content,
including text, images, and audio. It utilizes deep learning models, such
as Large Language Models (LLMs), to recognize and replicate data patterns,
enabling the generation of human-like content. Notable families of LLMs
include GPT (GPT-3.5, GPT-3.5 Turbo, and GPT-4), LLaMA (LLaMA and LLaMA-2),
and Mistral (Mistral and Mixtral). GPT, which stands for Generative
Pretrained Transformer, is especially popular for text generation and is
widely used in applications like ChatGPT. GenAI has taken the world by
storm and revolutionized various industries, including healthcare, finance,
and entertainment. However, GenAI models have several limitations,
including biases from training data, generating factually incorrect
information, and difficulty in understanding complex content. Additionally,
their performance can vary based on domain specificity.

In recent times, Knowledge Graphs (KGs) have attracted considerable
attention for their ability to represent structured and interconnected
information, and adopted by many companies in various domains. KGs
represent knowledge by depicting relationships between entities, known as
facts, usually based on formal ontological models. Consequently, they
enable accuracy, decisiveness, interpretability, domain-specific knowledge,
and evolving knowledge in various AI applications. The intersection between
GenAI and KG has ignited significant interest and innovation in Natural
Language Processing (NLP). For instance, by integrating LLMs with KGs
during pre-training and inference, external knowledge can be incorporated
for enhancing the model’s capabilities and improving interpretability. When
integrated, they offer a robust approach to problem solving in diverse
areas such as information enrichment, representation learning,
conversational AI, cross-domain AI transfer, bias, content generation, and
semantic understanding. This workshop aims at reinforcing the relationships
between Deep Learning, Knowledge Graphs, and NLP communities and foster
interdisciplinary research in the area of GenAI.


Topics of Interest

   -   Enhancing KG construction and completion with GenAI
   -   Multimodal KG generation
   -   Text-to-KG using LLMs
   -   Multilingual KGs
   -   GenAI for KG embeddings
   -   GenAI for Temporal KGs
   -   Dialogue systems enhanced by KG and GenAI
   -   Cross-domain knowledge transfer with GenAI
   -   Bias mitigation using KGs in GenAI
   -   Explainability with KGs and GenAI
   -   Natural language querying of KGs via GenAI
   -   NLP tasks using KGs and GenAI
   -   Prompt Engineering using KGs
   -   GenAI for Ontology learning and schema induction in KGs
   -   Hybrid QA systems combining KGs and GenAI
   -   Recommendation systems and KGs with GenAI
   -   Creating benchmark datasets relevant for tasks combining KGs and GenAI
   -   Real-world applications on scholarly data, biomedical domain, etc.
   -   Knowledge Graph Alignment
   -   Applying to real-world scenarios

Important Dates

- Submission deadline: 5 November 2024
- Notification of Acceptance: 5 December 2024
- Camera-ready paper due: 13 December 2024
- COLING2025 Workshop day: 19 or 20 January 2025


Submissions

Full research papers (6-8 pages)
Short research papers (4-6 pages)
Position papers (2 pages)

Papers must follow the two-column format of *ACL conferences, using
the official templates
<https://www.overleaf.com/latex/templates/association-for-computational-
linguistics-acl-conference/jvxskxpnznfj/>.
The templates are available for download as style files and formatting
guidelines. Submissions that do not adhere to the specified styles,
including paper size, font size restrictions, and margin width, will be
desk-rejected. Submissions are open to all and must be anonymous, adhering
to COLING 2025's <https://coling2025.org/calls/main_conference_papers/>
double-blind submission and reproducibility guidelines.  All accepted
papers  (after double-blind review of at least 3 experts) will appear in
the workshop proceedings that will be published in ACL Anthology.

At least one of the authors of the accepted papers must register for the
workshop to be included into the workshop proceedings. The workshop will be
a 100% in-person 1-day event at COLING 2025.

Submissions must be made using the START portal:
https://softconf.com/coling2025/GenAIK25/

Organization

- Genet Asefa Gesese
<https://www.fiz-karlsruhe.de/en/forschung/lebenslauf-und-publikationen-dr-ing-
genet-asefa-gesese>,
FIZ Karlsruhe, KIT, Germany

- Mehwish Alam <https://sites.google.com/view/mehwish-alam/home>, Telecom
Paris, Institut Polytechnique de Paris, France

- Harald Sack
<https://www.fiz-karlsruhe.de/en/forschung/lebenslauf-prof-dr-harald-sack>,
FIZ Karlsruhe, KIT, Germany

- Heiko Paulheim
<https://www.uni-mannheim.de/dws/people/professors/prof-dr-heiko-paulheim/>,
University of Mannheim, Germany

- Albert Meroño-Peñuela <https://www.albertmeronyo.org/>, King’s College
London, UK

- Lihu Chen <https://chenlihu.com/>, Imperial College London, UK


If you have published in ACL conferences previously, and are interested to
be part of the program committee of GenAIK2025, please fill in this
<https://forms.gle/t56dP6McD1VJmTfT9> form.


--
Dr.-Ing. Genet Asefa Gesese
Postdoctoral Researcher (Wíssenschaftlerin)
FIZ Karlsruhe – Leibniz Institute for Information Infrastructure
(https://www.fiz-karlsruhe.de/de/forschung/lebenslauf-und-publikationen-dr-ing-
genet-asefa-gesese )
AND
Karlsruhe Institute of Technology (KIT)
https://www.aifb.kit.edu/web/Genet_Asefa_Gesese/en



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