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Taxonomies are important knowledge ontologies that underpin numerousapplications on a daily basis, but many taxonomies used in practice suffer fromthe low coverage issue. We study the taxonomy expansion problem, which aims toexpand existing taxonomies with new concept terms. We propose a self-supervisedtaxonomy expansion model named STEAM, which leverages natural supervision inthe existing taxonomy for expansion. To generate natural self-supervisionsignals, STEAM samples mini-paths from the existing taxonomy, and formulates anode attachment prediction task between anchor mini-paths and query terms. Tosolve the node attachment task, it learns feature representations forquery-anchor pairs from multiple views and performs multi-view co-training forprediction. Extensive experiments show that STEAM outperforms state-of-the-artmethods for taxonomy expansion by 11.6\% in accuracy and 7.0\% in meanreciprocal rank on three public benchmarks. The implementation of STEAM can befound at \url{https://github.com/yueyu1030/STEAM}.

STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths