TaxoKnow: Taxonomy as Prior Knowledge in the Loss Function of Multi-class Classification

Mohsen Pourvali, Yao Meng, Chen Sheng, Yangzhou Du

Keywords: Hybrid Learning, Neuro-Symbolic Learning, Taxonomy-Aware Classification

Abstract:

In this paper, we investigate the effectiveness of integrating
a hierarchical taxonomy of labels as prior knowledge
into the learning algorithm of a flat classifier. We introduce
two methods to integrate the hierarchical taxonomy as an
explicit regularizer into the loss function of learning algorithms.
By reasoning on a hierarchical taxonomy, a neural
network alleviates its output distributions over the classes, allowing
conditioning on upper concepts for a minority class.
We limit ourselves to the flat classification task and provide
our experimental results on two industrial in-house datasets
and two public benchmarks, RCV1 and Amazon product reviews.
Our obtained results show the significant effect of a
taxonomy in increasing the performance of a learner in semisupervised
multi-class classification and the considerable results
obtained in a fully supervised fashion.

Submission Number: 2