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.

本文提出了层次扩展框架 (HEF)，旨在更好地利用分类法的分层结构进行分层扩展。HEF 在多个方面利用分类法的分层结构，利用亲属关系检测和几个树独特功能来评估其子树的连贯性，并引入适合度评分来选择最优位置，将信息交换用于消歧和自我纠正。该模型在三个基准数据集上的实验表明，通过更好地利用层次结构和优化分类法的连贯性，HEF 在准确性和平均倒数排名方面平均提高了 46.7% 和 32.3% 的精度，远远超过了之前的最佳水平。