1 research outputs found
Neural and Symbolic AI - mind the gap! Aligning Artificial Neural Networks and Ontologies
Artificial neural networks have been the key to solve a variety of different problems.
However, neural network models are still essentially regarded as black boxes, since they
do not provide any human-interpretable evidence as to why they output a certain re sult. In this dissertation, we address this issue by leveraging on ontologies and building
small classifiers that map a neural network鈥檚 internal representations to concepts from
an ontology, enabling the generation of symbolic justifications for the output of neural
networks. Using two image classification problems as testing ground, we discuss how to
map the internal representations of a neural network to the concepts of an ontology, exam ine whether the results obtained by the established mappings match our understanding
of the mapped concepts, and analyze the justifications obtained through this method