  Computational context understanding refers to an agent's ability to fuse
disparate sources of information for decision-making and is, therefore,
generally regarded as a prerequisite for sophisticated machine reasoning
capabilities, such as in artificial intelligence (AI). Data-driven and
knowledge-driven methods are two classical techniques in the pursuit of such
machine sense-making capability. However, while data-driven methods seek to
model the statistical regularities of events by making observations in the
real-world, they remain difficult to interpret and they lack mechanisms for
naturally incorporating external knowledge. Conversely, knowledge-driven
methods, combine structured knowledge bases, perform symbolic reasoning based
on axiomatic principles, and are more interpretable in their inferential
processing; however, they often lack the ability to estimate the statistical
salience of an inference. To combat these issues, we propose the use of hybrid
AI methodology as a general framework for combining the strengths of both
approaches. Specifically, we inherit the concept of neuro-symbolism as a way of
using knowledge-bases to guide the learning progress of deep neural networks.
We further ground our discussion in two applications of neuro-symbolism and, in
both cases, show that our systems maintain interpretability while achieving
comparable performance, relative to the state-of-the-art.
