=Paper=
{{Paper
|id=Vol-3749/genesy-preface
|storemode=property
|title=First International Workshop on Generative
Neuro-Symbolic AI (GeNeSy 2024)
|pdfUrl=https://ceur-ws.org/Vol-3749/genesy-preface.pdf
|volume=Vol-3749
|authors=Filip Ilievski,Jacopo de Berardinis,Jongmo Kim,Nitisha Jain
|dblpUrl=https://dblp.org/rec/conf/esws/IlievskiBKJ24
}}
==First International Workshop on Generative
Neuro-Symbolic AI (GeNeSy 2024)==
https://ceur-ws.org/Vol-3749/genesy-preface.pdf
First International Workshop on Generative
Neuro-Symbolic AI (GeNeSy 2024)
Filip Ilievski1 , Jacopo de Berardinis2 , Jongmo Kim3 and Nitisha Jain3
1
Department of Computer Science, Vrije Universiteit Amsterdam, Netherlands
2
Department of Computer Science, University of Manchester, UK
3
Department of Informatics, King’s College London, UK
Abstract
The fields of generative and neuro-symbolic AI have recently gained significant traction in both academia and
industry, owing to their profound impact on real-world applications and their potential to achieve human-level
intelligence. While generative AI excels in producing human-like outputs across various tasks, neuro-symbolic
AI aims to integrate cognitive and perceptive intelligence. Despite their apparent relevance to human-level
AI, the relationship between these two paradigms remains largely unexplored. The GeNeSy 2024 workshop
was conceived to address this gap, providing a platform for researchers to present and discuss novel ideas and
approaches in the emerging field of generative neuro-symbolic AI.
Keywords
Generative Model, Neuro-symbolic AI, Neuro-symbolic Representations, Large Language Models
1. Introduction
The GeNeSy 2024 workshop1 sought to initiate a comprehensive discourse on the nature and definition
of generative neuro-symbolic AI, encompassing its methodologies, architectures, and approaches. While
Large Language Models (LLMs) undoubtedly play a central role in this domain, current research suggests
that the integration of well-structured symbolic approaches—such as ontologies, knowledge graphs, and
probabilistic logic programming—is crucial for developing generative models capable of human-level
intelligence.
Held in conjunction with the 21st Extended Semantic Web Conference (ESWC 2024) in Hersonis-
sos, Greece, on May 26, 2024, the workshop benefited from the diverse expertise of attendees from
both academia and industry, spanning fields such as knowledge graphs, semantic web technologies,
and AI/ML. The workshop received 7 submissions, of which 6 were accepted for publication in the
proceedings. These comprised 4 regular papers and 2 dissemination papers.
2. Keynotes
The GeNeSy workshop featured two distinguished keynote speakers, both renowned for their significant
contributions to generative neuro-symbolic AI. Sungjin Ahn2 (Professor of KAIST University) presented
recent work in cognitive-grounded machine learning, opening up new opportunities to bridge both
fields around common challenges. He elucidated an abstract architecture for integrating generative and
inductive neural models while incorporating symbolic reasoning capabilities. Frank van Harmelen3
GeNeSy ’24: The workshop of Generative Neuro-Symboli AI, May 26–30, 2024, Hersonissos, Greece
Envelope-Open f.ilievski@vu.nl (F. Ilievski); jacopo.deberardinis@kcl.ac.uk (J. d. Berardinis); jongmo.kim@kcl.ac.uk (J. Kim);
nitisha.jain@kcl.ac.uk (N. Jain)
GLOBE https://www.ilievski.info (F. Ilievski); http://www.jacopodeberardinis.com (J. d. Berardinis); https://nitishajain.github.io
(N. Jain)
Orcid 0000-0002-1735-0686 (F. Ilievski); 0000-0001-6770-1969 (J. d. Berardinis); 0000-0002-4984-1674 (J. Kim);
0000-0002-7429-7949 (N. Jain)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
1
https://sites.google.com/view/genesy2024/
2
https://mlml.kaist.ac.kr/sungjinahn
3
https://www.cs.vu.nl/~frankh/
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
(Professor of Vrije University (VU) Amsterdam) delineated the crucial distinctions between neuro-
symbolic and neuro-semantic AI, emphasizing the pivotal role of semantic representations in endowing
neural models with comprehensive symbolic reasoning abilities. These insightful presentations have
been made available on the GeNeSy website for broader dissemination and continued discussion within
the research community.
3. Program Committee
• Vaishak Belle, The University of Edinburgh
• Inès Blin, Sony Computer Science Laboratories Paris
• Jindong Jiang, Rutgers University
• Ligong Han, Rutgers University
• Viktor Schlegel, The University of Manchester
• Jan-Christoph Kalo, University of Amsterdam
• Emile van Krieken, Vrije Universiteit Amsterdam
• Nicolas Lazzari, University of Bologna
• Alessandro Oltramari, Bosch Research and Technology Center
• Chung-Chi Chen, National Institute of Advanced Industrial Science and Technology
• Pascal Hitzler, Kansas State University
• Hongbo Zhu, University of Manchester
• Michael Fisher, University of Manchester
• Ioannis Reklos, King’s College London
Acknowledgments
We would like to thank all contributors, in particular the program committee, our keynote speakers,
the workshop chairs of ESWC 2024, and all the authors for their contributions. Furthermore, we wish
to thank the attendees of the workshop for making GeNeSy a captivating venue to discuss preliminary
work in the field of generative neuro-symbolic AI.