From Shallow to Deep Interactions Between Knowledge Representation,
  Reasoning and Machine Learning (Kay R. Amel group)
  This paper proposes a tentative and original survey of meeting points between
Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two
areas which have been developing quite separately in the last three decades.
Some common concerns are identified and discussed such as the types of used
representation, the roles of knowledge and data, the lack or the excess of
information, or the need for explanations and causal understanding. Then some
methodologies combining reasoning and learning are reviewed (such as inductive
logic programming, neuro-symbolic reasoning, formal concept analysis,
rule-based representations and ML, uncertainty in ML, or case-based reasoning
and analogical reasoning), before discussing examples of synergies between KRR
and ML (including topics such as belief functions on regression, EM algorithm
versus revision, the semantic description of vector representations, the
combination of deep learning with high level inference, knowledge graph
completion, declarative frameworks for data mining, or preferences and
recommendation). This paper is the first step of a work in progress aiming at a
better mutual understanding of research in KRR and ML, and how they could
cooperate.
