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.

翻译：分类体系将领域概念组织为层次化结构，对于构建知识系统及下游应用至关重要。随着领域知识的演进，分类体系需要持续更新以纳入新概念。先前的方法主要集中于向现有层次树的叶节点添加概念，这未能充分利用分类体系的知识，且无法更新原始分类体系结构（通常涉及非叶节点）。本文提出一种名为ATTEMPT的两阶段分类体系补全方法。该方法通过寻找父节点并标注子节点，将新概念插入正确位置。具体而言，通过将局部节点与提示模板结合生成自然语句，我们利用预训练语言模型实现上下位关系识别。在两个公开数据集（涵盖六个领域）上的实验结果表明，ATTEMPT在分类体系补全与扩展任务上均取得最优性能，超越现有方法。