  Taxonomies are important knowledge ontologies that underpin numerous
applications on a daily basis, but many taxonomies used in practice suffer from
the low coverage issue. We study the taxonomy expansion problem, which aims to
expand existing taxonomies with new concept terms. We propose a self-supervised
taxonomy expansion model named STEAM, which leverages natural supervision in
the existing taxonomy for expansion. To generate natural self-supervision
signals, STEAM samples mini-paths from the existing taxonomy, and formulates a
node attachment prediction task between anchor mini-paths and query terms. To
solve the node attachment task, it learns feature representations for
query-anchor pairs from multiple views and performs multi-view co-training for
prediction. Extensive experiments show that STEAM outperforms state-of-the-art
methods for taxonomy expansion by 11.6\% in accuracy and 7.0\% in mean
reciprocal rank on three public benchmarks. The implementation of STEAM can be
found at \url{https://github.com/yueyu1030/STEAM}.
