  Taxonomies, which organize domain concepts into hierarchical structures, are
crucial for building knowledge systems and downstream applications. As domain
knowledge evolves, taxonomies need to be continuously updated to include new
concepts. Previous approaches have mainly focused on adding concepts to the
leaf nodes of the existing hierarchical tree, which does not fully utilize the
taxonomy's knowledge and is unable to update the original taxonomy structure
(usually involving non-leaf nodes). In this paper, we propose a two-stage
method called ATTEMPT for taxonomy completion. Our method inserts new concepts
into the correct position by finding a parent node and labeling child nodes.
Specifically, by combining local nodes with prompts to generate natural
sentences, we take advantage of pre-trained language models for
hypernym/hyponymy recognition. Experimental results on two public datasets
(including six domains) show that ATTEMPT performs best on both taxonomy
completion and extension tasks, surpassing existing methods.
