Call for Papers: Special Issue on Neurosymbolic AI and Ontologies

Neurosymbolic AI is the principled integration of neural learning and representation with symbolic learning and representation. It is, perhaps, inarguable that ontology is paramount as a vehicle for knowledge representation and reasoning. This call pertains to the intersection of the use of ontology (and other formal semantics) and neural systems of varying complexity. Symbolic methods, particularly ontologies, capture knowledge in (generally) rigid ways that are guaranteed to be correct, and thus have predictable outputs, given their underlying logical natures. Yet, these guarantees come with expensive side-effects. Reasoning breaks in the face of inconsistent data and is computationally intensive. On the other hand, neural systems can manage vast quantities of data to extract surprising insight. Yet, these systems – especially at the highest levels of complexity – can be prone to hallucination or confabulation. In essence, we seek submissions that bridge the complementary approaches of symbolic representation and reasoning (i.e., in the form of ontologies) and neural systems, broadly defined, that address these challenges, and tackle important, emerging, or foundational topics in Neurosymbolic AI.

Topics of Interest (include but are not limited to):

• Ontology Embeddings
• Ontology Engineering with Large Language Models
  • Using Prompts to Engineer Ontologies
  • Using Ontologies to Engineer Prompts
• Post-symbolic Processing with Neural Systems
• Neural Systems for Ontology Matching
• Ontology Alignment with Neural Systems
• Reasoning with Neural Systems

Deadline:

December 18 2024

Guest Editors:

• Mehwish Alam, Institut Polytechnique de Paris, France
• Genet Asefa Gesese, FIZ Karlsruhe, Karlsruhe Institute of Technology, Germany
• Ernesto Jiménez-Ruiz, City, University of London, UK
• Heiko Paulheim, University of Mannheim, Germany
• Cogan Shimizu, Wright State University, USA

Contact Email for the guest editors : nesyai-ontology@wright.edu

Guest Editorial Board:

• Catia Pesquita, University of Lisbon, Portugal
• Cassia Trojahn, Institut de Recherche en Informatique de Toulouse
• Brandon Dave, Wright State University, USA
• Raghava Mutharaju, IIIT-Delhi, India
• Pierre Monnin, WIMMICS, France
• Adrita Barua, Kansas State University, USA
• Nikita Gautam, Kansas State University, USA
• Sven Hertling, KIT, Germany and FIZ Karlsruhe,Germany
• Shufan Jiang, KIT, Germany and FIZ Karlsruhe, Germany
• Stefano De Giorgis, ISTC-CNR, Italy
• Angelo Salatino, Knowledge Media Institute (KMi) and Open University, UK
• Nicolas Lazzari, University of Bologna, Italy

Author Guidelines:

We invite full (research) papers. 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.