  The latest Deep Learning (DL) models for detection and classification have
achieved an unprecedented performance over classical machine learning
algorithms. However, DL models are black-box methods hard to debug, interpret,
and certify. DL alone cannot provide explanations that can be validated by a
non technical audience. In contrast, symbolic AI systems that convert concepts
into rules or symbols -- such as knowledge graphs -- are easier to explain.
However, they present lower generalisation and scaling capabilities. A very
important challenge is to fuse DL representations with expert knowledge. One
way to address this challenge, as well as the performance-explainability
trade-off is by leveraging the best of both streams without obviating domain
expert knowledge. We tackle such problem by considering the symbolic knowledge
is expressed in form of a domain expert knowledge graph. We present the
eXplainable Neural-symbolic learning (X-NeSyL) methodology, designed to learn
both symbolic and deep representations, together with an explainability metric
to assess the level of alignment of machine and human expert explanations. The
ultimate objective is to fuse DL representations with expert domain knowledge
during the learning process to serve as a sound basis for explainability.
X-NeSyL methodology involves the concrete use of two notions of explanation at
inference and training time respectively: 1) EXPLANet: Expert-aligned
eXplainable Part-based cLAssifier NETwork Architecture, a compositional CNN
that makes use of symbolic representations, and 2) SHAP-Backprop, an
explainable AI-informed training procedure that guides the DL process to align
with such symbolic representations in form of knowledge graphs. We showcase
X-NeSyL methodology using MonuMAI dataset for monument facade image
classification, and demonstrate that our approach improves explainability and
performance.
