Taxonomies are fundamental to many real-world applications in various
domains, serving as structural representations of knowledge. To deal with the
increasing volume of new concepts needed to be organized as taxonomies,
researchers turn to automatically completion of an existing taxonomy with new
concepts. In this paper, we propose TaxoEnrich, a new taxonomy completion
framework, which effectively leverages both semantic features and structural
information in the existing taxonomy and offers a better representation of
candidate position to boost the performance of taxonomy completion.
Specifically, TaxoEnrich consists of four components: (1)
taxonomy-contextualized embedding which incorporates both semantic meanings of
concept and taxonomic relations based on powerful pretrained language models;
(2) a taxonomy-aware sequential encoder which learns candidate position
representations by encoding the structural information of taxonomy; (3) a
query-aware sibling encoder which adaptively aggregates candidate siblings to
augment candidate position representations based on their importance to the
query-position matching; (4) a query-position matching model which extends
existing work with our new candidate position representations. Extensive
experiments on four large real-world datasets from different domains show that
\TaxoEnrich achieves the best performance among all evaluation metrics and
outperforms previous state-of-the-art methods by a large margin.

本研究提出了一种名为 TaxoEnrich 的新的分类完成框架，它有效地利用现有分类体系中的语义特征和结构信息，并提供更好的候选位置表示以提高分类完成性能。实验结果表明，在四个来自不同领域的大型真实数据集上，TaxoEnrich 在所有评估指标上均实现了最佳性能，并且在很大程度上优于先前的最新方法。