Trends and Perspectives

Knowledge Graphs and Neurosymbolic AI

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 current ever-evolving landscape of AI, this workshop aims to unravel the potential lying at 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 and extended abstracts of published papers are also welcome.

Keynote Speakers

We are happy to announce that Alexander Schindler from Austrian Institute of Technology (AIT) has agreed to become our keynote speaker for the workshop!

(more details to follow…)

Topics of interests

Topics of interest include, but are not limited to :

  • Neurosymbolic AI for Knowledge Engineering.
  • Machine Learning techniques for creating, improving or aligning Knowledge Graphs.
  • Knowledge Infusion in Machine Learning algorithms.
  • Knowledge Graphs quality and its influence on Neurosymbolic AI systems.
  • Knowledge Graphs for trustworthy Neurosymbolic AI systems.
  • Knowledge Graphs for explainable AI.
  • Knowledge Representation and Reasoning using Deep Neural Networks.
  • Methods, systems, and techniques using Symbolic AI for the development of Explainable AI.
  • Utilizing ontologies for enhancing Neurosymbolic AI on the Web.
  • Applications of Neurosymbolic AI and Knowledge Graphs in Industry.
  • Applications of Neurosymbolic AI in domains such as medicine, biology, IoT, search, security and others.

Important Dates

  • 2024-01-31: Paper submission deadline
  • 2024-02-28: Notification of acceptance
  • 2024-03-26: AIRoV Symposium

Submissions

Submissions can fall in one of the following categories:

  • Full research papers (8 pages)
  • Short research papers (2-4 pages)
    • Work-in-progress
    • Position papers
    • Extended abstract of accepted conference/journal papers.
    • Project description / Lightning talks

Submit your contribution to this workshop on the AIROV’s CMT page, identified by the workshop acronym (KG-NeSy). The review process will be conducted in a single-blind review process. Further information on the format can be found here.

At least one of the authors of the accepted papers must register for the workshop (pre-conference only option) to be included into the workshop proceedings.

Selected papers from the workshop will be invited to the Special issue on Knowledge Graphs and Neurosymbolic AI of the NAI Journal.

Workshop Organizers

Program Committee