Taxonomy completion, a task aimed at automatically enriching an existing taxonomy with new concepts, has gained significant interest in recent years. Previous works have introduced complex modules, external information, and pseudo-leaves to enrich the representation and unify the matching process of attachment and insertion. While they have achieved good performance, these introductions may have brought noise and unfairness during training and scoring. In this paper, we present TaxBox, a novel framework for taxonomy completion that maps taxonomy concepts to box embeddings and employs two probabilistic scorers for concept attachment and insertion, avoiding the need for pseudo-leaves. Specifically, TaxBox consists of three components: (1) a graph aggregation module to leverage the structural information of the taxonomy and two lightweight decoders that map features to box embedding and capture complex relationships between concepts; (2) two probabilistic scorers that correspond to attachment and insertion operations and ensure the avoidance of pseudo-leaves; and (3) three learning objectives that assist the model in mapping concepts more granularly onto the box embedding space. Experimental results on four real-world datasets suggest that TaxBox outperforms baseline methods by a considerable margin and surpasses previous state-of-art methods to a certain extent.

翻译：分类法补全任务旨在自动为现有分类法添加新概念，近年来受到广泛关注。先前研究引入了复杂模块、外部信息及伪叶节点来增强概念表示，并统一了附着与插入操作的匹配过程。尽管这些方法取得了良好性能，但其引入的模块可能在训练和评分阶段带来噪声与不公平性。本文提出TaxBox——一种基于盒嵌入的新型分类法补全框架，该框架将分类法概念映射为盒嵌入，并采用两个概率评分器分别处理概念附着与插入，从而避免使用伪叶节点。具体而言，TaxBox包含三个组件：（1）图聚合模块用于捕获分类法结构信息，配合两个轻量级解码器将特征映射为盒嵌入并捕捉概念间的复杂关系；（2）两个概率评分器分别对应附着与插入操作，确保无需伪叶节点；（3）三种学习目标辅助模型将概念更精细地映射至盒嵌入空间。在四个真实数据集上的实验结果表明，TaxBox以显著优势超越基线方法，并在一定程度上突破了现有最优方法的性能上限。