  Taxonomy is a hierarchically structured knowledge graph that plays a crucial
role in machine intelligence. The taxonomy expansion task aims to find a
position for a new term in an existing taxonomy to capture the emerging
knowledge in the world and keep the taxonomy dynamically updated. Previous
taxonomy expansion solutions neglect valuable information brought by the
hierarchical structure and evaluate the correctness of merely an added edge,
which downgrade the problem to node-pair scoring or mini-path classification.
In this paper, we propose the Hierarchy Expansion Framework (HEF), which fully
exploits the hierarchical structure's properties to maximize the coherence of
expanded taxonomy. HEF makes use of taxonomy's hierarchical structure in
multiple aspects: i) HEF utilizes subtrees containing most relevant nodes as
self-supervision data for a complete comparison of parental and sibling
relations; ii) HEF adopts a coherence modeling module to evaluate the coherence
of a taxonomy's subtree by integrating hypernymy relation detection and several
tree-exclusive features; iii) HEF introduces the Fitting Score for position
selection, which explicitly evaluates both path and level selections and takes
full advantage of parental relations to interchange information for
disambiguation and self-correction. Extensive experiments show that by better
exploiting the hierarchical structure and optimizing taxonomy's coherence, HEF
vastly surpasses the prior state-of-the-art on three benchmark datasets by an
average improvement of 46.7% in accuracy and 32.3% in mean reciprocal rank.
