  Knowledge graph (KG) reasoning is becoming increasingly popular in both
academia and industry. Conventional KG reasoning based on symbolic logic is
deterministic, with reasoning results being explainable, while modern
embedding-based reasoning can deal with uncertainty and predict plausible
knowledge, often with high efficiency via vector computation. A promising
direction is to integrate both logic-based and embedding-based methods, with
the vision to have advantages of both. It has attracted wide research attention
with more and more works published in recent years. In this paper, we
comprehensively survey these works, focusing on how logics and embeddings are
integrated. We first briefly introduce preliminaries, then systematically
categorize and discuss works of logic and embedding-aware KG reasoning from
different perspectives, and finally conclude and discuss the challenges and
further directions.
