Neurosymbolic AI deals with models that combine symbolic processing, like classic AI, and
neural networks, as it's a very established area. These models are emerging as an effort …

Taxonomy Expansion, which relies on modeling concepts and concept relations, can be
formulated as a set representation learning task. The generalization of set, fuzzy set …

The AI community is increasingly focused on merging logic with deep learning to create
Neuro-Symbolic (NeSy) paradigms and assist neural approaches with symbolic knowledge …

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust
decision-making in adversarial environments. The integration of machine learning (ML) …

Ontologies are one of the richest sources of knowledge. Real-world ontologies often contain
thousands of axioms and are often human-made. Hence, they may contain inconsistency …

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust
decision-making in adversarial environments. The integration of machine learning (ML) …