Taxonomy expansion is the process of incorporating a large number of additional nodes (i.e., "queries") into an existing taxonomy (i.e., "seed"), with the most important step being the selection of appropriate positions for each query. Enormous efforts have been made by exploring the seed's structure. However, existing approaches are deficient in their mining of structural information in two ways: poor modeling of the hierarchical semantics and failure to capture directionality of is-a relation. This paper seeks to address these issues by explicitly denoting each node as the combination of inherited feature (i.e., structural part) and incremental feature (i.e., supplementary part). Specifically, the inherited feature originates from "parent" nodes and is weighted by an inheritance factor. With this node representation, the hierarchy of semantics in taxonomies (i.e., the inheritance and accumulation of features from "parent" to "child") could be embodied. Additionally, based on this representation, the directionality of is-a relation could be easily translated into the irreversible inheritance of features. Inspired by the Darmois-Skitovich Theorem, we implement this irreversibility by a non-Gaussian constraint on the supplementary feature. A log-likelihood learning objective is further utilized to optimize the proposed model (dubbed DNG), whereby the required non-Gaussianity is also theoretically ensured. Extensive experimental results on two real-world datasets verify the superiority of DNG relative to several strong baselines.

翻译：Taxonomy 扩展是指将大量新增节点（即“查询”）融入现有 taxonomy（即“种子”）的过程，其中最关键的一步是为每个查询选择合适的位置。已有大量研究通过探索种子结构来推进该任务。然而，现有方法在结构信息挖掘方面存在两方面的不足：对层级语义的建模能力较弱，且未能捕捉 is-a 关系的有向性。本文旨在解决这些问题，通过将每个节点明确表示为继承特征（即结构部分）与增量特征（即补充部分）的组合。具体而言，继承特征源自“父”节点，并由继承因子加权。借助这种节点表示，taxonomy 中语义的层级性（即特征从“父”节点到“子”节点的继承与积累）得以体现。此外，基于该表示，is-a 关系的有向性可被轻松转化为特征的不可逆继承。受达莫瓦-斯基托维奇定理启发，我们通过对补充特征施加非高斯约束来实现这种不可逆性。进一步利用对数似然学习目标来优化所提出的模型（称为 DNG），同时从理论上确保所需的非高斯性。在两个真实数据集上的广泛实验结果验证了 DNG 相对于多个强基线的优越性。