Call for Papers: Special Issue on Knowledge Graphs and Neurosymbolic AI
Knowledge Graphs are by far the largest scale knowledge representation formalism in the history of AI. Graphs of hundreds of millions of triples are now routinely used in both academic research and industrial practice. Their large scale makes these symbolic representations particularly suitable for integration with Machine Learning (ML) techniques and therefore highly relevant for the emerging area of Neurosymbolic AI. In this special issue we aim to explore emerging research at the confluence of Knowledge Graphs research and Neurosymbolic AI thus gaining a better understanding of how these two fields influence and benefit each other. To that end, we are soliciting papers on the use of techniques from Neurosymbolic AI/Machine Learning to construct or improve Knowledge Graphs, techniques that use Knowledge Graphs to improve Machine Learning results as part of emerging Neurosymbolic AI systems, as well as any other combinations. Survey papers and application papers are also welcome.
Topics of Interest (include but are not limited to):
- Neurosymbolic AI for knowledge engineering
- Combination of Knowledge Engineering and Machine Learning
- The use of ML techniques for creating, extending, improving or aligning knowledge graphs
- Knowledge graphs for Neurosymbolic AI systems
- Knowledge infusion in machine learning algorithms
- Empirical results on the impact of enhancing ML systems with knowledge graphs
- Knowledge graph quality and its influence on Neurosymbolic AI systems
- Benchmarks, Quality Metrics
- Knowledge graphs for trustworthy Neurosymbolic AI systems
- Transparent/provenance-aware knowledge graphs
- Policy-aware/compliant knowledge graphs
- Commonsense knowledge
- Explainable AI
- The use of knowledge graphs for human-centric aspects of Neurosymbolic AI systems
- Engineering knowledge based Neurosymbolic AI systems
- Design methodologies, e.g. Pattern-based Neurosymbolic AI systems
- Applications of knowledge graphs-based Neurosymbolic AI systems
- Domain-specific knowledge graphs for Neurosymbolic AI systems in domains such as medicine, biology, IoT, search, and others.
May 31, 2024. Earliest submissions will be processed as they come in.
- Marta Sabou (Vienna University of Economics and Business)
- Raghava Mutharaju (IIIT Delhi)
- Frank van Harmelen (Vrije Universiteit Amsterdam)
Contact Email for the guest editors : email@example.com
Guest Editorial Board:
(to be completed)
We invite full papers, dataset descriptions, survey papers, application reports and reports on tools and systems. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this special issue. Authors can extend previously published conference or workshop papers - see the submission guidelines at https://neurosymbolic-ai-journal.com/content/author-guidelines for details. Submissions shall be made through the journal website at https://neurosymbolic-ai-journal.com/. Prospective authors must take notice of the submission guidelines posted at https://neurosymbolic-ai-journal.com/content/author-guidelines.
Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the "Special Issue on Knowledge Graphs in Neurosymbolic AI". All manuscripts will be reviewed based on the journal's open and transparent review policy and will be made available online during the review process.