  Taxonomies have found wide applications in various domains, especially online
for item categorization, browsing, and search. Despite the prevalent use of
online catalog taxonomies, most of them in practice are maintained by humans,
which is labor-intensive and difficult to scale. While taxonomy construction
from scratch is considerably studied in the literature, how to effectively
enrich existing incomplete taxonomies remains an open yet important research
question. Taxonomy enrichment not only requires the robustness to deal with
emerging terms but also the consistency between existing taxonomy structure and
new term attachment. In this paper, we present a self-supervised end-to-end
framework, Octet, for Online Catalog Taxonomy EnrichmenT. Octet leverages
heterogeneous information unique to online catalog taxonomies such as user
queries, items, and their relations to the taxonomy nodes while requiring no
other supervision than the existing taxonomies. We propose to distantly train a
sequence labeling model for term extraction and employ graph neural networks
(GNNs) to capture the taxonomy structure as well as the query-item-taxonomy
interactions for term attachment. Extensive experiments in different online
domains demonstrate the superiority of Octet over state-of-the-art methods via
both automatic and human evaluations. Notably, Octet enriches an online catalog
taxonomy in production to 2 times larger in the open-world evaluation.

Computation and Language