  Neurosymbolic AI is an increasingly active area of research that combines
symbolic reasoning methods with deep learning to leverage their complementary
benefits. As knowledge graphs are becoming a popular way to represent
heterogeneous and multi-relational data, methods for reasoning on graph
structures have attempted to follow this neurosymbolic paradigm. Traditionally,
such approaches have utilized either rule-based inference or generated
representative numerical embeddings from which patterns could be extracted.
However, several recent studies have attempted to bridge this dichotomy to
generate models that facilitate interpretability, maintain competitive
performance, and integrate expert knowledge. Therefore, we survey methods that
perform neurosymbolic reasoning tasks on knowledge graphs and propose a novel
taxonomy by which we can classify them. Specifically, we propose three major
categories: (1) logically-informed embedding approaches, (2) embedding
approaches with logical constraints, and (3) rule learning approaches.
Alongside the taxonomy, we provide a tabular overview of the approaches and
links to their source code, if available, for more direct comparison. Finally,
we discuss the unique characteristics and limitations of these methods, then
propose several prospective directions toward which this field of research
could evolve.
