Knowledge Graphs provide a robust framework for organizing and representing large-scale knowledge in machine-readable formats. Their flexibility and scalability in capturing knowledge makes them invaluable across diverse domains, fostering seamless integration with Machine Learning techniques. This integration enhances their relevance for traditional AI applications and positions them as crucial components in the emerging field of Neurosymbolic AI. At its core, Neurosymbolic AI bridges the strengths of symbolic reasoning and neural network learning.

In the rapidly-evolving landscape of AI, this workshop aims to explore research topics in the intersection of Knowledge Graphs and Neurosymbolic AI. Neurosymbolic AI/Machine Learning techniques can be applied to construct and refine Knowledge Graphs, contributing to their ongoing evolution. Simultaneously, leveraging Knowledge Graphs for learning in Neurosymbolic AI allows intelligent systems to draw insights from structured and unstructured symbolic representations, paving the way for more informed AI models and improving interpretability and transparency in their decision-making process.

We invite papers that explore these synergies as well as any other combinations, seeking to gain a better understanding of how Knowledge Graphs and Neurosymbolic AI influence and benefit each other. Application papers are also welcome.

Topics of Interest

Topics of interest include, but are not limited to:

• Neurosymbolic AI for Knowledge Engineering
  • Knowledge Representation and Reasoning using Deep Neural Networks
  • Large-Language Models for Knowledge Engineering
  • Machine Learning techniques for creating, improving, or aligning Knowledge Graphs.
• Knowledge Graphs and Neurosymbolic approaches for Robust, Trustworthy and Interpretable AI
  • Knowledge Graph-based approaches for improving fairness, bias mitigation, and ethical AI
  • Knowledge Graphs for trustworthy Neurosymbolic AI systems
  • Knowledge Graphs quality and its influence on Neurosymbolic AI systems
  • Knowledge Infusion in Machine Learning algorithms
  • Neurosymbolic AI for (Autonomous) Agentic Systems
  • Symbolic AI methods, systems, and techniques for Explainable AI
  • Using symbolic reasoning and ontologies to identify, mitigate, and explain biases in neural systems
  • Utilizing knowledge graphs, ontologies, and other structured knowledge representations to enhance AI reliability and accountability
• Application of Neurosymbolic AI and Knowledge Graphs
  • Applications of Neurosymbolic AI and Knowledge Graphs in Industry
  • Applications of Neurosymbolic AI in domains such as medicine, biology, IoT, security, robotics and others
  • Certification, auditing, and documentation of AI systems using Knowledge Graphs and Neurosymbolic AI
  • Integration of Knowledge Graphs, Neurosymbolic AI and human-machine intelligence

Important Dates

• 2025-06-14: Paper submission deadline
• 2025-07-05: Notification of acceptance
• 2025-09-03: KG-NeSy Workshop @ SEMANTiCS 2025

Submissions

All submissions must be written in English and adhere to the CEUR-ART style (one column). Submissions can fall in one of the following categories:

• Short papers (8 pages - including references)
• Full papers (15 pages - including references)

Workshop Organizers

• Shqiponja Ahmetaj (TU Wien)
• Fajar J. Ekaputra (WU Wien)
• Andreas Ekelhart (Universität Wien)
• Sebastian Neumaier (FH St. Pölten)