Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …

Attempts at combining logic and neural networks into neurosymbolic approaches have been
on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists …

Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for
learning with logical reasoning. This survey identifies several parallels across seven …

In recent years, there has been an increasing interest in exploiting logically specified
background knowledge in order to obtain neural models (i) with a better performance,(ii) …

Current high-performance semantic segmentation models are purely data-driven sub-
symbolic approaches and blind to the structured nature of the visual world. This is in stark …

Deep learning and symbolic reasoning are complementary techniques for an intelligent
system. However, principled combinations of these techniques have limited scalability …

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …

Large language models (LLMs) such as ChatGPT have recently demonstrated significant
potential in mathematical abilities, providing valuable reasoning paradigm consistent with …

Despite significant progress in the development of neural-symbolic frameworks, the question
of how to integrate a neural and a symbolic system in a compositional manner remains …