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.

本论文提出了一种基于神经符号学的混合人工智能方法，以结合数据驱动和知识驱动的优点，并将其应用于两个案例中达到可解释性的同时实现了与最先进技术相当的性能。