  Taxonomies have been widely used in various machine learning and text mining
systems to organize knowledge and facilitate downstream tasks. One critical
challenge is that, as data and business scope grow in real applications,
existing taxonomies need to be expanded to incorporate new concepts. Previous
works on taxonomy expansion process the new concepts independently and
simultaneously, ignoring the potential relationships among them and the
appropriate order of inserting operations. However, in reality, the new
concepts tend to be mutually correlated and form local hypernym-hyponym
structures. In such a scenario, ignoring the dependencies of new concepts and
the order of insertion may trigger error propagation. For example, existing
taxonomy expansion systems may insert hyponyms to existing taxonomies before
their hypernym, leading to sub-optimal expanded taxonomies. To complement
existing taxonomy expansion systems, we propose TaxoOrder, a novel
self-supervised framework that simultaneously discovers the local
hypernym-hyponym structure among new concepts and decides the order of
insertion. TaxoOrder can be directly plugged into any taxonomy expansion system
and improve the quality of expanded taxonomies. Experiments on the real-world
dataset validate the effectiveness of TaxoOrder to enhance taxonomy expansion
systems, leading to better-resulting taxonomies with comparison to baselines
under various evaluation metrics.
