Humanist Discussion Group, Vol. 38, No. 216.
Department of Digital Humanities, University of Cologne
Hosted by DH-Cologne
www.dhhumanist.org
Submit to: humanist@dhhumanist.org
[1] From: Mapes, Kristen <kmapes@msu.edu>
Subject: Day of DH is Monday, 2 December 2024! (30)
[2] From: Chen, Lihu <lihu.chen@imperial.ac.uk>
Subject: CfP: Workshop on Generative AI and Knowledge Graphs (GenAIK) co-located with COLING 2025 (with some travel grant for 2 students) (138)
--[1]------------------------------------------------------------------------
Date: 2024-10-31 20:38:47+00:00
From: Mapes, Kristen <kmapes@msu.edu>
Subject: Day of DH is Monday, 2 December 2024!
Dear colleagues,
centerNet is happy to announce that this year’s Day of DH will take place on
Monday 2 December 2024! We want to hear about what you are planning for the day.
Is there a lecture, book discussion, social event, or other activity happening
in your community on the Day of DH?
Let us know by filling out this simple form:
https://docs.google.com/forms/d/e/1FAIpQLSce5_69wV1BswCzx-
IGqXzy4GbKbyOJXKvDKdVvEjGY9d9jug/viewform
On the Day of DH, post about your work. What does DH mean? What does it look
like? Day of DH is a window into the world of DH on one day of the year. Share
about your work using the hashtag #DayofDH2024 on your social media platform of
choice.
We will compile a list of activities and gather the discussion as we can on the
(newly refreshed!) centerNet website: https://dhcenternet.org/
Thanks,
Kristen
Kristen Mapes
centerNet, Co-Chair
Associate Director of Digital Humanities, College of Arts & Letters
Michigan State University
East Lansing MI
kmapes@msu.edu
she/her
--[2]------------------------------------------------------------------------
Date: 2024-10-30 13:34:57+00:00
From: Chen, Lihu <lihu.chen@imperial.ac.uk>
Subject: CfP: Workshop on Generative AI and Knowledge Graphs (GenAIK) co-located with COLING 2025 (with some travel grant for 2 students)
Workshop on Generative AI and Knowledge Graphs (GenAIK),
19 January 2025, Abu Dhabi, UAE
Web: https://genetasefa.github.io/GenAIK2025/
X: @GenAIK25
LinkedIn: https://www.linkedin.com/groups/9868047
Mastodon: https://sigmoid.social/@GenAIK
-
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 January 2025
Submissions
Full research papers (6-8 pages)
Short research papers (4-6 pages)
Position papers (2 pages)
These page limits only apply to the main body of the paper. At the end of the
paper (after the conclusions but before the references) papers need to include a
mandatory section discussing the limitations of the work and, optionally, a
section discussing ethical considerations. Papers can include unlimited pages of
references and an unlimited appendix.
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 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/
Sponsors
NFDI4DataScience (NFDI4DS - https://www.nfdi4datascience.de/ ) is a national
research data infrastructure for Data Science and AI project. The overarching
objective of the project is the development, establishment, and sustainment of a
national research data infrastructure (NFDI) for the Data Science and Artificial
Intelligence community in Germany. The vision of NFDI4DS is to support all steps
of the complex and interdisciplinary research data lifecycle, including
collecting/creating, processing, analyzing, publishing, archiving, and reusing
resources in Data Science and Artificial Intelligence. NFDI4ds is offering a
total of €2000 in travel grants (€1000 each) to two selected students who will
attend and present their work at GenAIK 2025! To be considered, submit your
paper to the workshop, and if your paper is accepted, you’ll be eligible for a
chance to receive one of the two grants.
Organization
- Genet Asefa Gesese, FIZ Karlsruhe, KIT, Germany
- Harald Sack, FIZ Karlsruhe, KIT, Germany
- Heiko Paulheim, University of Mannheim, Germany
- Albert Meroño-Peñuela, King’s College London, UK
- Lihu Chen, 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 form:
https://forms.gle/t56dP6McD1VJmTfT9
Kind Regards,
Lihu Chen
Imperial College London
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